{ "cells": [ { "cell_type": "markdown", "id": "3fd51524", "metadata": {}, "source": [ "*Copyright (C) 2022 Intel Corporation*
\n", "*SPDX-License-Identifier: BSD-3-Clause*
\n", "*See: https://spdx.org/licenses/*\n", "\n", "---\n", "\n", "# Excitatory-Inhibitory Neural Network with Lava" ] }, { "cell_type": "markdown", "id": "8ac8ba24", "metadata": {}, "source": [ "**Motivation**: In this tutorial, we will build a Lava Process for a neural networks of excitatory and inhibitory neurons (E/I network).
\n", "E/I networks are a fundamental example of neural networks mimicking the structure of the brain and exhibiting rich dynamical behavior.
" ] }, { "cell_type": "markdown", "id": "3fbb06e6", "metadata": {}, "source": [ "#### This tutorial assumes that you:\n", "- have the [Lava framework installed](../in_depth/tutorial01_installing_lava.ipynb \"Tutorial on Installing Lava\")\n", "- are familiar with the [Process concept in Lava](../in_depth/tutorial02_processes.ipynb \"Tutorial on Processes\")\n", "\n", "#### This tutorial gives a high level view of\n", "- how to implement simple E/I Network Lava Process\n", "- how to define and select multiple ProcessModels for the E/I Network, based on Rate and [Leaky Integrate-and-Fire (LIF)](https://github.com/lava-nc/lava/tree/main/src/lava/proc/lif \"Lava's LIF neuron\") neurons\n", "- how to use tags to chose between different ProcessModels when running the Process\n", "- the principle adjustments needed to run bit-accurate ProcessModels\n", "\n", "#### E/I Network\n", "From bird's-eye view, an E/I network is a recurrently coupled network of neurons.
\n", "Since positive couplings (excitatory synapses) alone lead to a positive feedback loop ultimately causing a divergence in the activity of the network, appropriate negative couplings (inhibitory synapses) need to be introduced to counterbalance this effect.
\n", "We here require a separation of the neurons into two populations: Neurons can either be inhibitory or excitatory.
\n", "Such networks exhibit different dynamical states. By introducing a control parameter, we can switch between these states and simultaneously alter the response properties of the network.
\n", "In the notebook below, we introduce two incarnations of E/I networks with different single neuron models: Rate and LIF neurons.
\n", "By providing a utility function that maps the weights from rate to LIF networks, we can retain hallmark properties of the dynamic in both networks.\n", "
\n", "Technically, the abstract E/I network is implemented via a LavaProcess, the concrete behavior - Rate and LIF dynamics - is realized with different ProcessModels.
" ] }, { "cell_type": "markdown", "id": "89344cf6", "metadata": {}, "source": [ "#### General imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "257a6fe8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "id": "de1acd9c", "metadata": {}, "source": [ "#### E/I Network Lava Process\n", "We define the structure of the E/I Network Lava Process class.
" ] }, { "cell_type": "code", "execution_count": 2, "id": "497c0d06", "metadata": {}, "outputs": [], "source": [ "# Import Process level primitives.\n", "from lava.magma.core.process.process import AbstractProcess\n", "from lava.magma.core.process.variable import Var\n", "from lava.magma.core.process.ports.ports import InPort, OutPort" ] }, { "cell_type": "code", "execution_count": 3, "id": "159d9263", "metadata": {}, "outputs": [], "source": [ "class EINetwork(AbstractProcess):\n", " \"\"\"Network of recurrently connected neurons.\n", " \"\"\"\n", " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", " \n", " shape_exc = kwargs.pop(\"shape_exc\", (1,))\n", " bias_exc = kwargs.pop(\"bias_exc\", 1)\n", " shape_inh = kwargs.pop(\"shape_inh\", (1,))\n", " bias_inh = kwargs.pop(\"bias_inh\", 1)\n", " # Factor controlling strength of inhibitory synapses relative to excitatory synapses.\n", " self.g_factor = kwargs.pop(\"g_factor\", 4)\n", " # Factor controlling response properties of network.\n", " # Larger q_factor implies longer lasting effect of provided input.\n", " self.q_factor = kwargs.pop(\"q_factor\", 1)\n", " weights = kwargs.pop(\"weights\")\n", " \n", " full_shape = shape_exc + shape_inh\n", " \n", " self.state = Var(shape=(full_shape,), init=0)\n", " # Variable for possible alternative state.\n", " self.state_alt = Var(shape=(full_shape,), init=0)\n", " # Biases provided to neurons.\n", " self.bias_exc = Var(shape=(shape_exc,), init=bias_exc)\n", " self.bias_inh = Var(shape=(shape_inh,), init=bias_inh)\n", " self.weights = Var(shape=(full_shape, full_shape), init=weights)\n", "\n", " # Ports for receiving input or sending output.\n", " self.inport = InPort(shape=(full_shape,))\n", " self.outport = OutPort(shape=(full_shape,))" ] }, { "cell_type": "markdown", "id": "01b9eabc", "metadata": {}, "source": [ "#### ProcessModels for Python execution" ] }, { "cell_type": "code", "execution_count": 4, "id": "bc319315", "metadata": {}, "outputs": [], "source": [ "# Import parent classes for ProcessModels for Hierarchical Processes.\n", "from lava.magma.core.model.py.model import PyLoihiProcessModel\n", "from lava.magma.core.model.sub.model import AbstractSubProcessModel\n", "\n", "# Import execution protocol.\n", "from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol\n", "\n", "# Import decorators.\n", "from lava.magma.core.decorator import implements, tag, requires" ] }, { "cell_type": "markdown", "id": "4ed07fb5", "metadata": {}, "source": [ "### Rate neurons\n", "We next turn to the different implementations of the E/I Network.\n", "We start with a rate network obeying the equation\n", "\\begin{equation}\n", " \\tau\\dot{r} = -r + W \\phi(r) + I_{\\mathrm{bias}}.\n", "\\end{equation}\n", "The rate or state $r$ is a vector containing the excitatory and inhibitory populations.
\n", "The non-linearity $\\phi$ is chosen to be the error function.
\n", "The dynamics consists of a dampening part ($-r$), a part modelling the recurrent connectivity ($ W \\phi(r)$)\n", " and an external bias ($I_{\\mathrm{bias}})$.
\n", " We discretize the equation as follows:\n", " \\begin{equation}\n", " r(i + 1) = (1 - dr) \\odot r(i) + W \\phi(r(i)) \\odot dr + I_{\\mathrm{bias}} \\odot dr\n", "\\end{equation}\n", "Potentially different time scales in the neuron dynamics of excitatory and inhibitory neurons as well as different bias currents for these subpopulations are encoded in the vectors $dr$ and $I_{\\mathrm{bias}}$. We use the error function as non-linearity $\\phi$." ] }, { "cell_type": "code", "execution_count": 5, "id": "44795c5b", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.model.py.type import LavaPyType\n", "from lava.magma.core.model.py.ports import PyInPort, PyOutPort\n", "from lava.magma.core.resources import CPU\n", "from lava.magma.core.model.model import AbstractProcessModel\n", "\n", "from scipy.special import erf\n", "\n", "@implements(proc=EINetwork, protocol=LoihiProtocol)\n", "@tag('rate_neurons') # Tag allows for easy selection of ProcessModel in case multiple are defined.\n", "@requires(CPU)\n", "class RateEINetworkModel(PyLoihiProcessModel):\n", "\n", " outport: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, float)\n", " inport: PyInPort = LavaPyType(PyInPort.VEC_DENSE, float) \n", " state : np.ndarray = LavaPyType(np.ndarray, float)\n", " state_alt : np.ndarray = LavaPyType(np.ndarray, float)\n", " bias_exc : np.ndarray = LavaPyType(np.ndarray, float)\n", " bias_inh : np.ndarray = LavaPyType(np.ndarray, float)\n", " weights : np.ndarray = LavaPyType(np.ndarray, float)\n", "\n", " def __init__(self, proc_params):\n", " super().__init__(proc_params=proc_params)\n", " \n", " self.dr_exc = proc_params.get('dr_exc')\n", " self.dr_inh = proc_params.get('dr_inh')\n", " \n", " self.shape_exc = proc_params.get('shape_exc')\n", " self.shape_inh = proc_params.get('shape_inh')\n", " \n", " self.proc_params = proc_params\n", " \n", " self.got_decay = False\n", " self.got_bias = False\n", " self.weights_scaled = False\n", " \n", " def get_decay(self):\n", " '''Construct decay factor.\n", " '''\n", " dr_full = np.array([self.dr_exc] * self.shape_exc + [self.dr_inh] * self.shape_inh)\n", " self.decay = 1 - dr_full\n", " \n", " self.got_decay= True\n", " \n", " def get_bias(self):\n", " '''Construce biases.\n", " '''\n", " self.bias_full = np.hstack([self.bias_exc, self.bias_inh])\n", " self.got_bias = False \n", " \n", " def scale_weights(self):\n", " '''Scale the weights with integration time step.\n", " '''\n", " \n", " self.weights[:, self.shape_exc:] *= self.dr_exc\n", " self.weights[:, :self.shape_exc] *= self.dr_inh\n", " self.proc_params.overwrite('weights', self.weights)\n", " \n", " self.weights_scaled = True\n", " \n", " def state_update(self, state):\n", " \"\"\"Update network state according to:\n", " r[i + 1] = (1 - dr)r[i] + Wr[i]*r*dr + bias*dr\n", " \"\"\"\n", " state_new = self.decay * state # Decay the state.\n", " state_new += self.bias_full # Add the bias.\n", " state_new += self.weights @ erf(state) # Add the recurrent input.\n", " return state_new\n", " \n", " def run_spk(self):\n", " \"\"\"The run function that performs the actual computation during\n", " execution orchestrated by a PyLoihiProcessModel using the\n", " LoihiProtocol.\n", " \"\"\"\n", " \n", " if not self.got_decay:\n", " self.get_decay()\n", " \n", " if not self.got_bias:\n", " self.get_bias()\n", " \n", " if not self.weights_scaled:\n", " self.scale_weights()\n", " \n", " a_in = self.inport.recv()\n", " self.state = self.state_update(self.state) + a_in\n", " self.outport.send(self.state)\n", " " ] }, { "cell_type": "markdown", "id": "1cd93212", "metadata": {}, "source": [ "#### Defining the parameters for the network\n", "Next, we need to constrain the network with the needed parameters.
\n", "First, we define the dimensionality of the network which we identify with the total number of neurons as well as the single neuron parameters.
\n", "We here follow the common choice that the ratio between the number of excitatory and inhibitory neurons equals $4$ and that the connection probability between two arbitrary neurons is identical.
\n", "The recurrent weights must *balance* the network, i.e. the average recurrent input to a neuron must be less or equal than $0$.
\n", "This implies that we need to increase the strength of the inhibitory weights, the `g_factor`, to at least $4$. We choose $4.5$ to unambiguously place the network in the inhibition dominated regime.
\n", "Finally, we set a parameter that controls the response properties of the network by scaling up the recurrent weights, the `q_factor`." ] }, { "cell_type": "code", "execution_count": 6, "id": "1caca08a", "metadata": {}, "outputs": [], "source": [ "# Fix the randomness.\n", "np.random.seed(1234)\n", "\n", "# Define dimensionality of the network.\n", "dim = 400\n", "shape = (dim,)\n", "\n", "# We represent the dimensionality by 400 neurons. As stated above 80% of the neurons will be excitatory.\n", "num_neurons_exc = int(dim * 0.8)\n", "num_neurons_inh = dim - num_neurons_exc\n", "\n", "# Single neuron paramters.\n", "params_exc = {\n", " \"shape_exc\": num_neurons_exc,\n", " \"dr_exc\": 0.01,\n", " \"bias_exc\": 0.1}\n", "\n", "params_inh = {\n", " \"shape_inh\": num_neurons_inh,\n", " \"dr_inh\": 0.01,\n", " \"bias_inh\": 0.1}\n", "\n", "# Inhibition-exciation balance for scaling inhibitory weights to maintain balance (4 times as many excitatory neurons).\n", "g_factor = 4.5\n", "\n", "# Factor controlling the response properties.\n", "q_factor = 1\n", "\n", "# Parameters Paramters for E/I network.\n", "network_params_balanced = {}\n", "\n", "network_params_balanced.update(params_exc)\n", "network_params_balanced.update(params_inh)\n", "network_params_balanced['g_factor'] = g_factor\n", "network_params_balanced['q_factor'] = q_factor" ] }, { "cell_type": "markdown", "id": "7e500634", "metadata": {}, "source": [ "Finally, we have to set the weights given the above constraints. To this end, we sample the weights randomly from a Gaussian distribution with zero-mean and a standard deviation that scales with the ```q_factor```." ] }, { "cell_type": "code", "execution_count": 7, "id": "7cadbf55", "metadata": {}, "outputs": [], "source": [ "def generate_gaussian_weights(dim, num_neurons_exc, q_factor, g_factor):\n", " '''Generate connectivity drawn from a Gaussian distribution with mean 0\n", " and std of (2 * q_factor) ** 2 / dim.\n", " If a excitatory neuron has a negative weight, we set it to 0 and similarly adapt\n", " positive weights for inhibitory neurons.\n", " W[i, j] is connection weight from pre-synaptic neuron j to post-synaptic neuron i.\n", " \n", " Paramerters\n", " -----------\n", " dim : int\n", " Dimensionality of network\n", " num_neurons_exc : int\n", " Number of excitatory neurons\n", " q_factor : float\n", " Factor determining response properties of network\n", " g_factor : float\n", " Factor determining inhibition-excitation balance\n", " \n", " Returns\n", " -------\n", " weights : np.ndarray\n", " E/I weight matrix\n", " '''\n", " # Set scaled standard deviation of recurrent weights, J = q_factor**2 * 6 / full_shape.\n", " J = (2 * q_factor)**2 / dim\n", " weights = np.random.normal(0, J,\n", " (dim, dim))\n", " \n", " # Impose constraint that neurons can **either** be excitatory (positive weight)\n", " # **or** inhibitory (negative weight).\n", " exc_conns = np.full(weights.shape, True)\n", " exc_conns[:, num_neurons_exc:] = False # Set entries for inhibitory neurons to False.\n", " inh_conns = np.invert(exc_conns)\n", " \n", " mask_pos_weights = (weights > 0)\n", " mask_neg_weights = (weights < 0)\n", "\n", " # Set negative weights of exciatory neurons to zero and similarly for inhibitory neurons.\n", " # This induce sparsity in the connectivity.\n", " weights[mask_neg_weights * exc_conns] = 0\n", " weights[mask_pos_weights * inh_conns] = 0\n", "\n", " # We finally need to increase the inhibitory weights by a factor to control balance.\n", " weights[inh_conns] *= g_factor\n", " \n", " return weights\n", "\n", "# Generate weights and store them in parameter dictionary.\n", "network_params_balanced['weights'] = generate_gaussian_weights(dim,\n", " num_neurons_exc,\n", " network_params_balanced['q_factor'],\n", " network_params_balanced['g_factor'])" ] }, { "cell_type": "markdown", "id": "ac80469e", "metadata": {}, "source": [ "#### Execution and Results" ] }, { "cell_type": "code", "execution_count": 8, "id": "e609ac8c", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.run_conditions import RunSteps\n", "from lava.magma.core.run_configs import Loihi1SimCfg\n", "# Import monitoring Process.\n", "from lava.proc.monitor.process import Monitor\n", "\n", "# Configurations for execution.\n", "num_steps = 1000\n", "rcfg = Loihi1SimCfg(select_tag='rate_neurons')\n", "run_cond = RunSteps(num_steps=num_steps)\n", "\n", "# Instantiating network and IO processes.\n", "network_balanced = EINetwork(**network_params_balanced)\n", "state_monitor = Monitor()\n", "\n", "state_monitor.probe(target=network_balanced.state, num_steps=num_steps)\n", "\n", "# Run the network.\n", "network_balanced.run(run_cfg=rcfg, condition=run_cond)\n", "states_balanced = state_monitor.get_data()[network_balanced.name][network_balanced.state.name]\n", "network_balanced.stop()" ] }, { "cell_type": "markdown", "id": "7febf4a0", "metadata": {}, "source": [ "#### Visualizing the activity\n", "We first have a look at the activity of the network by plotting the numerical value of the state of the first $50$ neurons." ] }, { "cell_type": "code", "execution_count": 9, "id": "f4cdddfe", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,5))\n", "plt.xlabel('Time Step')\n", "plt.ylabel('State value')\n", "plt.plot(states_balanced[:, :50])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "70982ef0", "metadata": {}, "source": [ "We observe that after an initial period the network settles in a fixed point.
\n", "As it turns out, this is a global stable fixed point of the network dynamics: If we applied a small perturbation, the network would return to the stable state.
\n", "Such a network is unfit for performing meaningful computations, the dynamics is low-dimensional and rather poor.
\n", "To better understand this, we apply an additional analysis." ] }, { "cell_type": "markdown", "id": "0f48bb1a", "metadata": {}, "source": [ "#### Further analysis\n", "We introduce the *auto-correlation function* $c(\\tau)$.
\n", "With this function, one can assess the *memory* of the network as well as the richness of the dynamics.
\n", "Denoting the (temporally averaged) network activity by $a$, the *auto-covariance function* is the variance (here denoted $\\mathrm{Cov}(\\cdot, \\cdot)$) of $a$ with a time shifted version of itself:\n", "\\begin{equation}\n", " c(\\tau) = \\mathrm{Cov}(a(t), a(t+\\tau))\n", "\\end{equation}\n", "This means for positive $\\tau$ the value of the auto-covariance function gives a measure for the similarity of the network state $a(t)$ and $a(t+\\tau)$.
\n", "By comparing $c(\\tau)$ with $c(0)$, we may assess the *memory* a network has of its previous states after $\\tau$ time steps.
\n", "Note that the auto-covariance function is not normalised!
\n", "Due to this, we may derive further information about the network state: If $c(0)$ is small (in our case $<< 1$), the network activity is not rich and does not exhibit a large temporal variety across neurons. Thus the networks is unable to perform meaningful computations." ] }, { "cell_type": "code", "execution_count": 10, "id": "67d93c4f", "metadata": {}, "outputs": [], "source": [ "def auto_cov_fct(acts, max_lag=100, offset=200):\n", " \"\"\"Auto-correlation function of parallel spike trains.\n", " \n", " Parameters\n", " ----------\n", " \n", " acts : np.ndarray shape (timesteps, num_neurons)\n", " Activity of neurons, a spike is indicated by a one \n", " max_lag : int\n", " Maximal lag for compuation of auto-correlation function\n", " \n", " Returns:\n", " \n", " lags : np.ndarray\n", " lags for auto-correlation function\n", " auto_corr_fct : np.ndarray\n", " auto-correlation function\n", " \"\"\"\n", " acts_local = acts.copy()[offset:-offset] # Disregard time steps at beginning and end.\n", " assert max_lag < acts.shape[0], 'Maximal lag must be smaller then total number of time points'\n", " num_neurons = acts_local.shape[1]\n", " acts_local -= np.mean(acts_local, axis=0) # Perform temporal averaging.\n", " auto_corr_fct = np.zeros(2 * max_lag + 1)\n", " lags = np.linspace(-1 * max_lag, max_lag, 2 * max_lag + 1, dtype=int)\n", " \n", " for i, lag in enumerate(lags):\n", " shifted_acts_local = np.roll(acts_local, shift=lag, axis=0)\n", " auto_corrs = np.zeros(acts_local.shape[0])\n", " for j, act in enumerate(acts_local):\n", " auto_corrs[j] = np.dot(act - np.mean(act),\n", " shifted_acts_local[j] - np.mean(shifted_acts_local[j]))/num_neurons\n", " auto_corr_fct[i] = np.mean(auto_corrs)\n", " \n", " return lags, auto_corr_fct" ] }, { "cell_type": "code", "execution_count": 11, "id": "830d4529", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lags, ac_fct_balanced = auto_cov_fct(acts=states_balanced)\n", "\n", "# Plotting the auto-correlation function.\n", "plt.figure(figsize=(7,5))\n", "plt.xlabel('Lag')\n", "plt.ylabel('Covariance')\n", "plt.plot(lags, ac_fct_balanced)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7815504e", "metadata": {}, "source": [ "As expected, there is covariance has its maximum at a time lag of $0$.
\n", "Examining the covariance function, we first note its values are small ($<<1$) implying low dimensional dynamics of the network.
\n", "This fits our observation made above on the grounds of the display of the time-resolved activity.
" ] }, { "cell_type": "markdown", "id": "25538ff1", "metadata": {}, "source": [ "#### Controlling the network\n", "We saw that the states of the neurons quickly converged to a globally stable fixed point.
\n", "The reason for this fixed point is, that the dampening part dominates the dynamical behavior - we need to increase the weights!
\n", "This we can achieve by increasing the `q_factor`." ] }, { "cell_type": "code", "execution_count": 12, "id": "d12da1b0", "metadata": {}, "outputs": [], "source": [ "# Defining new, larger q_factor.\n", "q_factor = np.sqrt(dim / 6)\n", "\n", "# Changing the strenghts of the recurrent connections.\n", "network_params_critical = network_params_balanced.copy()\n", "network_params_critical['q_factor'] = q_factor\n", "network_params_critical['weights'] = generate_gaussian_weights(dim,\n", " num_neurons_exc,\n", " network_params_critical['q_factor'],\n", " network_params_critical['g_factor'])\n", "\n", "\n", "# Configurations for execution.\n", "num_steps = 1000\n", "rcfg = Loihi1SimCfg(select_tag='rate_neurons')\n", "run_cond = RunSteps(num_steps=num_steps)\n", "\n", "# Instantiating network and IO processes.\n", "network_critical = EINetwork(**network_params_critical)\n", "state_monitor = Monitor()\n", "\n", "state_monitor.probe(target=network_critical.state, num_steps=num_steps)\n", "\n", "# Run the network.\n", "network_critical.run(run_cfg=rcfg, condition=run_cond)\n", "states_critical = state_monitor.get_data()[network_critical.name][network_critical.state.name]\n", "network_critical.stop()" ] }, { "cell_type": "code", "execution_count": 13, "id": "a708a851", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,5))\n", "plt.xlabel('Time Step')\n", "plt.ylabel('State value')\n", "plt.plot(states_critical[:, :50])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "57019969", "metadata": {}, "source": [ "We find that after increasing the `q_factor`, the network shows a very different behavior. The stable fixed point is gone, instead we observe chaotic network dynamics:
\n", "The single neuron trajectories behave unpredictably and fluctuate widely, a small perturbation would lead to completely different state." ] }, { "cell_type": "code", "execution_count": 14, "id": "36411f14", "metadata": {}, "outputs": [ { "data": { "image/png": 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fn8XOQp3fJkdGhU3CUvbqbTz4wTxO79SAq45v5TuOVLI6KQn89eLurNy8mzt1fpscIRU2CTs7C4v5/b9nUS81kUcuVHPjSNWnVR1uPr09k+es5a3MVb7jSBhRYZOwc/ekXFZu3s3jv+xGrWr6Xi2S/e6UtvRvU5e7J+eycMMO33EkTKiwSViZ+NMa3p21mhtPbUc/XTQ04sXGGE/8shupiXHc+Pos9uwt8R1JwkDQCpuZJZnZD2Y2x8xyzWxMYPxFM1tmZrMDS7dgZZDIsmLTLkZNzKFXi9r84VT1gYwW9Wsk8dgvurFww07GTMn1HUfCQDD32AqBU51zxwDdgLPNrF/gsT8557oFltlBzCARoqiklD+8MRszeOKibsTF6mBDNDmxfRrXn9yGN35cxQfZun6bHFrQfju4MvsushQfWDS1SY7KY58sZM6qrTw4rCtNa+v6atHoljPa07VpTYaPz2bdtj2+40gIC+rHXjOLNbPZQB7wiXPu+8BD95tZlpk9bmbqViuH9NWijTw9YwkX92nGOV0b+Y4jnsTHxvCXi7pTVFLKLW/OoaRUn5PlwIJa2JxzJc65bkBToI+ZpQMjgI5Ab6AOcMeBXmtm15pZppll5ufnBzOmhLBNOwu5+a3ZtElLZfSgLr7jiGet6qVwz7ld+HbpJv715VLfcSREVckXFc65rcB04Gzn3LrAYcpC4AWgz0Fe84xzrpdzrldaWlpVxJQQ45zjT+9ksW1PEX+9qDvJCbG+I0kIuLBXUwakN+SRjxaQvVott+R/BXNWZJqZ1QrcTgbOAOabWaPAmAFDgJxgZZDw9ur3K/lsfh4jBnSkc+MavuNIiDAzHhiWQb3URG564yd271XLLflvwdxjawRMN7Ms4EfKvmObCrxmZtlANlAPGBvEDBKmluTv5P735nJCu3pcfmxL33EkxNSqlsBjvzyGZZt2cd/Uub7jSIiJC9YPds5lAd0PMH5qsNYpkaGopJSb35xNUnwsj1x4jC5FIwfUv009rjuxDU/PWMJJ7etzdnpD35EkROhkIAk5T05bRNbqbYwbmkGDGkm+40gIu+WM9mQ0qcnw8Vms31bgO46ECBU2CSkzV2zhb9MXM6xHEwZmaGq/HFpCXAxPXNSNwqJSbn17NqU6BUBQYZMQsquwmFvemk2jmsncM1hT+6Vi2qSlMvrczny9eBMv6qrbggqbhJD7ps79T9f+GknxvuNIGLmodzNO7Vifhz6cz+I8XQUg2qmwSUj4ZO4G3vhxFded2IY+rer4jiNhxsx48PwMqiXEcstbcygqKfUdSTxSYRPv8ncUMvzdLDo1qsHNZ7TzHUfCVP3qSYwbmkHW6m08NX2x7zjikQqbeOWcY/i7WewoLOYvF3UjMU7dReToDchoxNDuTXjys8Vkrd7qO454osImXv37h1VMm5/HHWd3pH2D6r7jSAS4Z3AX0lITufnN2RQU6cKk0UiFTbxZtrGsa8Rxbevym/4tfceRCFEzOZ6HL+zKkvxdPPThfN9xxAMVNvGiONBdJD7W1F1EKt0J7dK4/NgWvPD1cr5ZvNF3HKliKmzixTNfLmX2qq3cNySdRjWTfceRCDR8QCda10vhtrfnsL2gyHccqUIqbFLlFqzfwROfLGJAekMGH9PYdxyJUMkJsTz2y25s2FHImMlqlBxNVNikShWVlLU+qp4Ux9gh6ZRdvUgkOLo1q8UNJ7fh3Vmr+TBnve84UkVU2KRK/X36EnLWbGfskHTqpib6jiNR4MZT25HepAZ3Tsgmf0eh7zhSBVTYpMrkrt3Gk58tYvAxjRmgBsdSRRLiYnjsF93YWVjMiPHZOKdGyZFOhU2qxN7iUm59aw61UxIYowbHUsXaN6jOn87swKfzNjBx9hrfcSTIVNikSjz52SLmr9/BA0MzqJ2S4DuORKErj29Fzxa1uWfyXPK269ptkUyFTYJuzqqt/P3zJZzfoymnd27gO45EqdgY4+ELulJQVMKdE3RIMpKpsElQFRSVcOvbc0hLTWT0uZ19x5Eo1zotlT+d1YFP5+XpkGQEU2GToHr804UsztvJg+dnUDNZ11gT/35znA5JRjoVNgmamSu28K8vlnJxn2ac3KG+7zgiQNkhyT//55Bkjg5JRiAVNgmKPXtLuO3tOTSqmczIc3QIUkJLm7RUbgvMkpw0e63vOFLJKlzYzCzWzBqbWfN9SzCDSXh75OMFLNu4i4cv6EpqYpzvOCL/48rjW9GjeS3unpxL3g4dkowkFSpsZvZ7YAPwCfBeYJkaxFwSxmau2MzzXy/j0n7N6d+2nu84IgcUG2M8fOExFBSVMFKHJCNKRffYbgI6OOe6OOcyAkvXYAaT8FRQVMLt72TRuGYywwd08h1H5JD2HZL8ZO4GJs/RIclIUdHCtgrYFswgEhme/GwRS/J38cCwDB2ClLCgQ5KRp6KFbSnwuZmNMLNb9i3BDCbhJ2fNNp6esZQLejblxPZpvuOIVMi+Q5K79+qQZKSoaGFbSdn3awlA9XKLCFB2OZrb38miTkoCd2kWpISZskOS7XVIMkJU6FiRc24MgJmlBu7vDGYoCT//nLGEueu28/SlPalZTSdiS/i56vjWfJiznrsn53Jc23rU02WVwlZFZ0Wmm9lPQC6Qa2YzzeyQLdrNLMnMfjCzOWaWa2b7imMrM/vezBab2Ztmpo64YW7Rhh38ddpizunaiLPTG/qOI3JUYmOMh87vyu7CEsZM0RW3w1lFD0U+A9zinGvhnGsB3Ar86zCvKQROdc4dA3QDzjazfsBDwOPOubbAFuCqo0ouIaGk1HH7u1mkJMbqcjQS9to1qM7vT23LlDlr+WTuBt9x5ChVtLClOOem77vjnPscSDnUC1yZfYcs4wOLA04F3gmMvwQMOYK8EmJe+HoZP63cyt3ndtGhG4kI153Uho4NqzNqYjbbC4p8x5GjUOFZkWZ2l5m1DCyjKJspeUiBbiWzgTzKJp8sAbY654oDT1kNNDnIa681s0wzy8zPz69gTKlKKzbt4pGPF3Bqx/qc162x7zgilSIhLoaHzu9K/o5CHvxgvu84chQqWtiuBNKA8YElLTB2SM65EudcN6Ap0AfoWNFgzrlnnHO9nHO90tI0dTzUOOcY/m428TEx3D80HTPzHUmk0hzTrBZXHd+K179fybdLNvmOI0eoQoXNObfFOfcH51yPwHKTc25LRVfinNsKTAeOBWqZ2b7ZmE0BXRQpDP37h1V8u3QTd57TiUY1k33HEal0t5zRgeZ1qjFifBYFRSW+48gROGRhM7MnAn9OMbPJ+y+HeW2amdUK3E4GzgDmUVbgLgg87XJg0s/7K0hVW7t1D+Pen0f/NnW5qHcz33FEgiI5IZYHz89g+abdPP7pQt9x5Agc7jy2VwJ/PnIUP7sR8JKZxVJWQN9yzk01s7nAG2Y2FvgJeO4ofrZ44pxj5IRsSkodDw7rqkOQEtH6t6nHRb2b8a8vlnJORiO6Nq3lO5JUwCELm3NuZuBmN+fcX8o/ZmY3ATMO8dosoPsBxpdS9n2bhKGJs9cwfUE+dw3qTPO61XzHEQm6EQM78dn8PG5/J4spvz+e+FhdxjLUVfRf6PIDjF1RiTkkDOTvKGTMlLn0aF6LK/q39B1HpErUTI7nviHpzF+/g2e+OOxkcAkBh9xjM7OLgUuAVvt9p1Yd2BzMYBJ67p06l92FJfz5gq7ExugQpESPs7o05JyMRvzl00Wc1aUhbeun+o4kh3C479i+AdYB9YBHy43vALKCFUpCz/T5eUyZs5abT29P2/rqfy3R557BXfhq8UaGv5vFW9cdS4w+3IWsQx6KdM6tcM597pw71jk3o9wyq9xJ1hLhdhUWM2piDu3qp3L9yW18xxHxIq16IncN6kzmii28+v0K33HkECraBLmfmf1oZjvNbK+ZlZjZ9mCHk9Dw6McLWbN1Dw8MyyAhTl+cS/Q6v0cTTmhXj4c+mM/qLbt9x5GDqOhvqb8BFwOLgGTgauCpYIWS0DFn1VZe/GYZl/ZrTq+WdXzHEfHKzBg3NAMHuihpCKvwx2/n3GIgNtAm6wXg7ODFklBQVFLK8PHZpFVP5PazK9wNTSSiNatTjT+d1YEZC/OZOFuNk0JRRQvb7sB102ab2Z/N7OYjeK2EqWe/XMa8ddsZMzidGkm6eKjIPr8+tiXdmtXivqnz2LJrr+84sp+KFqfLgFjgRmAX0Aw4P1ihxL8Vm3bxxKcLOatLA108VGQ/sTHGA8My2L6niPvfn+c7juynok2QVzjn9jjntjvnxjjnbgkcmpQI5JzjzgnZJMTGMGZwuu84IiGpU6MaXHNia96ZuZpvFm/0HUfKOVwT5GwzyzrYUlUhpWqNn7WGrxdv4vYBHWlYM8l3HJGQddNp7WhRtxojJ+boCgAh5HAnaA+qkhQSMjbtLGTse3Pp2aI2v+rT3HcckZCWFB/L/UMyuPS573lq+mJuPbOD70hCxU7QXuGc23c2YrvA7TzUUisijX1vHjsLi3lwWIY6K4hUwPHt6jGsexOenrGEhRt2+I4jVPwE7WuAd4B/BoaaAhODlEk8mbEwnwk/reH6k9vSroHaZolU1MhzOpGaGMeI8dmUlurcNt8qOivyBuA4YDuAc24RUD9YoaTq7d5bzMgJ2bROS+F3apslckTqpiZy58BOzFyxhX//uNJ3nKhX0cJW6Jz7z8kaZhYH6GNJBHni00Ws3rKHB4ZmkBQf6zuOSNi5oGdTjm1dlwc/mE/e9gLfcaJaRQvbDDO7E0g2szOAt4EpwYslVSlnzTae/XIpF/dpRt/WdX3HEQlLZsa4YRkUFpcyZspc33GiWkUL2x1APpANXAe8D4wKViipOsUlpQwfn0Xd1ESGD+jkO45IWGtVL4U/nNqW97LXMW3eBt9xotbhpvtjZrFArnOuI/Cv4EeSqvTC18vJWbOdpy7pQc1ktc0S+bmuPbENk+esZfSkXPq1rktK4mF/zUolO+wem3OuBFhgZjqpKcKs2rybxz5ZyOmd6jMwQ22zRCpDQlwMDwzLYM3WPTz2yULfcaJSRT9K1AZyzewHynpFAuCcGxyUVBJ0zjlGTcwhxuDe89Ix0zlrIpWlZ4s6/Kpvc174ehlDujUho2lN35GiSkUL211BTSFV7v3s9cxYmM/oQZ1pXCvZdxyRiHP72R35ZO4Gho/PYtINxxEXqwuiVJXDbunAd2z/dM7N2H+pgnwSBDsKihgzJZf0JjX49bEtfMcRiUg1k+O5Z3AXctdu58VvlvuOE1X0HVsUevTjheTvLOT+IRn6FCkSRAPSG3J6p/o8+vFCVm3e7TtO1Kjob7V937FNM7PJ+5ZgBpPgyF69jZe/Xc5l/VpwTLNavuOIRDQzY8x56ZjBXZNycE59LaqCvmOLIiWljpETs6mbmshtZ6kLuUhVaFIrmVvP7MB9U+cyNWsd5x7T2HekiFfRC43OAOYD1QPLPH3HFn5e+34FWau3cdegztRI0jlrIlXliv4t6dq0JmOmzGXbniLfcSJeRbv7/wL4AbgQ+AXwvZldEMxgUrnythfw8IcLOKFdPc7t2sh3HJGoEhtjjBuaweZdhTzy0QLfcSJeRb9jGwn0ds5d7pz7NdCHwxyeNLNmZjbdzOaaWa6Z3RQYv8fM1pjZ7MAy8Of9FaQi7ntvHoUlpTpnTcST9CY1ubx/S179fgWzV231HSeiVbSwxTjn8srd31SB1xYDtzrnOgP9gBvMrHPgscedc90Cy/tHFlmO1BcL85kyZy03nNyWVvVSfMcRiVq3nNGe+tUTuXN8NsUlpb7jRKyKFrYPzewjM7vCzK4A3qOsEfJBOefWOedmBW7vAOYBTX5OWDlyBUUl3DUph9b1Uvjtya19xxGJatWT4rnn3C7MXbedl75d4TtOxDpkYTOztmZ2nHPuT5RdPbtrYPkWeKaiKzGzlkB34PvA0I1mlmVmz5tZ7aNKLhXy98+XsGLTbu4bkk5inK6zJuLb2ekNOaVDGo99vIB12/b4jhORDrfH9gT/f9Xs8c65W5xztwATAo8dlpmlAu8Cf3TObQf+AbQBugHrgEcP8rprzSzTzDLz8/MrsirZz5L8nTz9+RKGdGvMcW3r+Y4jIpSd23bveekUlzrGTNZ124LhcIWtgXMue//BwFjLw/1wM4unrKi95pwbH3jtBudciXOulLLL4PQ50Gudc88453o553qlpaUdblWyH+ccd03MITE+hpHndD78C0SkyjSrU40/nNaOD3PX67ptQXC4wlbrEI8dsnOulU29e46yc94eKzdefq75UCDnMBnkKEyavZZvlmzijrM7klY90XccEdnPNSe0pl39VEZPymXP3hLfcSLK4Qpbpplds/+gmV0NzDzMa48DLgNO3W9q/5/NLNvMsoBTgJuPJrgc3LbdRYx9by7dmtXikj5q8SkSihLiYhg7JJ01W/fwl2mLfMeJKIdrqfVHYIKZ/Yr/L2S9gATK9rYOyjn3FXCgE6Y0vT/I/vzRfDbv2stLV/YhJkbnrImEqr6t63Jhz6Y8++VShnZvQoeG1X1HigiH3GMLfB/WHxgDLA8sY5xzxzrn1gc/nhypWSu38PoPK/nNca3o0lgXNxQJdSMGdqJ6UhwjJ2RTWqomyZWhor0ipzvnngwsnwU7lByd4pJSRk7IoUH1JG4+o73vOCJSAXVSEhgxsBOZK7bw9sxVvuNEBF2MK4K8+M1y5q3bzj2DO5OaWNELN4iIbxf2bEqflnV44IP5bNpZ6DtO2FNhixBrt+7hsU8WcmrH+pzVpaHvOCJyBMyM+4ems7OgmHHvz/cdJ+ypsEWIe6fMpdQ5xgzuoibHImGoXYPqXHtia96dtZpvl2zyHSesqbBFgGnzNvBh7nr+cFo7mtWp5juOiByl35/ajmZ1khk1MZu9xWqSfLRU2MLc7r3FjJ6US7v6qVx9vJoci4Sz5IRY7h2czpL8XTzzxRLfccKWCluY++u0xazZuoexQ9JJiNM/p0i4O6VjfQZmNOTJzxazYtMu33HCkn4ThrEF63fw7JdLubBnU/q2rus7johUktGDuhAXY4yelItzOrftSKmwhanSUseoidmkJsUxYmAn33FEpBI1rJnErWd2YMbCfN7LXuc7TthRYQtT78xczY/Lt3DngE7USUnwHUdEKtmvj21BepMa3DtlLtsLinzHCSsqbGFo8669jPtgHr1b1uaCnk19xxGRIIiLjeH+IRnk7yzksY8X+o4TVlTYwtAD789jZ0ExY4dkqMmxSAQ7plktLuvXgpe+XU7W6q2+44QNFbYw8/3STbw9czVXn9BancBFosBtZ3WgXmoid07IpkRNkitEhS2M7C0uZdTEHJrUSuYPp7X1HUdEqkCNpHhGD+pMzprtvPztct9xwoIKWxh59qulLMrbyb3ndaFagpoci0SLQV0bcWL7NB79eCEbthf4jhPyVNjCxKrNu/nrtEWc1aUBp3Vq4DuOiFQhM+PewV3YW1LKvVPn+o4T8lTYwoBzjtGTcogx4+5zu/iOIyIetKyXwo2ntOW9rHV8viDPd5yQpsIWBj7KXc/0BfncckZ7GtdK9h1HRDy57qTWtK6XwuhJuRQUlfiOE7JU2ELczsJi7pk8l06NanBF/5a+44iIR4lxsYwdks7Kzbt5avpi33FClgpbiHv8k4Vs2FHA/UPTiYvVP5dItOvfth5Duzfh6RlLWJy303eckKTflCEsZ802Xvh6GRf3aU6P5rV9xxGREHHnwE4kx8cyamK2miQfgApbiCopdYycmEPtagnccVZH33FEJISkVU/kjgEd+W7pZibOXuM7TshRYQtR//5hJXNWbWXUoE7UrBbvO46IhJiLezene/NajJ06j2271SS5PBW2EJS/o5CHPpzPsa3rMqRbE99xRCQExcQY9w/JYOueIh76aL7vOCFFhS0E3f/eXAqLShk7NB0zNTkWkQPr3LhstvTr369k5ootvuOEDBW2EPP14o1MnL2W357UmjZpqb7jiEiIu/mM9jSskcTICdkUl5T6jhMSVNhCSEFRCaMm5tCibjV+d4qaHIvI4aUmxnHP4M7MX7+DF79Z7jtOSAhaYTOzZmY23czmmlmumd0UGK9jZp+Y2aLAn5rHHvDPGUtZtnEX956XTlJ8rO84IhImzurSkFM71uexTxayduse33G8C+YeWzFwq3OuM9APuMHMOgPDgWnOuXbAtMD9qLds4y6e+nwxg7o24qT2ab7jiEgYMTPGDO5CqXOMmZLrO453QStszrl1zrlZgds7gHlAE+A84KXA014ChgQrQ7jY1+Q4MTaGuwZ19h1HRMJQszrV+MNp7fgodwPT5m3wHcerKvmOzcxaAt2B74EGzrl1gYfWA1F/DZYpWev4ctFGbjurAw1qJPmOIyJh6urjW9OufiqjJ+WyZ2/0NkkOemEzs1TgXeCPzrnt5R9zZb1gDtgPxsyuNbNMM8vMz88Pdkxvtu0p4r6pc8loUpNL+7XwHUdEwlhCXAxjh6SzZuse/vrZIt9xvAlqYTOzeMqK2mvOufGB4Q1m1ijweCPggBcWcs4945zr5ZzrlZYWud85PfrxAjbtLGTc0AxiY3TOmoj8PH1b1+XCnk351xdLWbhhh+84XgRzVqQBzwHznHOPlXtoMnB54PblwKRgZQh1c1Zt5ZXvVvDrY1uS0bSm7zgiEiFGDOxEalIcIydkU1oafU2Sg7nHdhxwGXCqmc0OLAOBB4EzzGwRcHrgftQpLinlzgnZpKUmcsuZ7X3HEZEIUiclgREDOvLj8i28M2u17zhVLi5YP9g59xVwsGNrpwVrveHile9WkLt2O3+7pDs1ktTkWEQq14U9m/F25moeeH8ep3dqQJ2UBN+Rqow6j3iwflsBj368kBPbp3FORiPfcUQkAsXEGGOHprOjoJgHP5jnO06VUmHz4L6pc9lbUsp953VRk2MRCZqODWtw1QmteCtzNT8u3+w7TpVRYati0xfk8V72On5/Slta1E3xHUdEItxNp7WjSa1kRk7IpihKmiSrsFWhgqISRk/KoXVaCtee1Np3HBGJAtUS4hgzuAsLN+zkua+W+Y5TJVTYqtDfPlvMqs17GDskncQ4NTkWkapxeucGnNm5AU98upBVm3f7jhN0KmxVZHHeDv75xRKGdW9C/zb1fMcRkShz9+AuxJhxz+Rcypo+RS4VtirgnGPUxByS42O585xOvuOISBRqUiuZm09vz7T5eXw8N7KbJKuwVYF3Z63hu6WbuWNAR+qlJvqOIyJR6orjWtKxYXXumZzLrsJi33GCRoUtyLbs2su49+fRo3ktLu7d3HccEYli8bEx3D80nXXbCnji04W+4wSNCluQPfjBfLbtKeL+oRnEqMmxiHjWs0UdLu7TjOe/Xs7ctdsP/4IwpMIWRD8s28ybmau4+vhWdGpUw3ccEREA7ji7IzWT4xk1MTKbJKuwBcne4lJGTcymSa1kbjq9ne84IiL/UataAiMHdmLWyq28mbnKd5xKp8IWJM9+tZSFG3YyZnAXqiUErde0iMhRGdajCX1b1eHBD+azcWeh7ziVSoUtCFZt3s1fpy3irC4NOL1zA99xRET+h5lx/9B0du8tZtz7kdUkWYWtkjnnuGtSDrFm3DO4i+84IiIH1bZ+da47sQ3jZ63hmyUbfcepNCpsleyDnPV8viCfm89oT6Oayb7jiIgc0o2ntqV5nWqMmphDYXGJ7ziVQoWtEu0oKGLMlFw6N6rBFf1b+o4jInJYSfGxjDmvC0vzd/GvL5b6jlMpVNgq0aMfLyRvRyHjhmUQF6tNKyLh4ZQO9RmY0ZAnP1vMik27fMf52fTbt5Jkr97Gy98u59K+LejWrJbvOCIiR2T0oC7ExRijJ4V/k2QVtkpQUuq4c0I2dVMT+dPZHXzHERE5Yg1rJnHrmR2YsTCf97PX+47zs6iwVYJXvl1O9pptjB7UmRpJ8b7jiIgclV8f24IujWtw79RcdhQU+Y5z1FTYfqb12wp45OOFnNg+jUFdG/mOIyJy1OJiY7h/aAZ5Owp57JPwbZKswvYz3Td1LkUlpdx3XhfM1ORYRMJbt2a1uLRvC176Zjk5a7b5jnNUVNh+hukL8ngvex2/P7UtLeqm+I4jIlIpbjurA3VSEhk5IZuSMGySrMJ2lPbsLeGuiTm0rZ/KtSe28R1HRKTS1EyO565BnZizehuvf7/Cd5wjpsJ2lJ78bBGrt+xh7JB0EuK0GUUksgw+pjHHta3Lnz9cQN6OAt9xjoh+Ix+FhRt28MwXS7mgZ1P6ta7rO46ISKUzM+47L53C4lLGTg2vJskqbEeotNQxckI21ZPiuHNgJ99xRESCpnVaKtef3IbJc9by5aJ833EqLGiFzcyeN7M8M8spN3aPma0xs9mBZWCw1h8s78xczY/LtzBiYCfqpCT4jiMiElTXn9yGlnWrMXpSLgVF4dEkOZh7bC8CZx9g/HHnXLfA8n4Q11/pNu0sZNwH8+jTsg4X9mzqO46ISNAlxcdy35B0lm3cxdMzlviOUyFBK2zOuS+AzcH6+T6Me38+uwqLuX9ous5ZE5GocUK7NAYf05i/T1/Cso2h3yTZx3dsN5pZVuBQZW0P6z8q3y7ZxLuzVnPtia1p16C67zgiIlVq1KBOJMbHcNfEnJBvklzVhe0fQBugG7AOePRgTzSza80s08wy8/P9fmlZWFzCqInZNKuTzI2ntPOaRUTEh/rVk7j9rA58tXgjk+es9R3nkKq0sDnnNjjnSpxzpcC/gD6HeO4zzrlezrleaWlpVRfyAJ6ZsZQl+bu477x0khNivWYREfHlkr4tOKZpTe6bOpetu/f6jnNQVVrYzKx8l+ChQM7Bnhsqlm/cxZPTF3NO10ac3KG+7zgiIt7ExhjjhmWwZXcRD34w33ecgwrmdP9/A98CHcxstZldBfzZzLLNLAs4Bbg5WOuvDM457pqUQ2JsDKMHdfYdR0TEuy6Na3LV8a1448dV/LAsNOcHxgXrBzvnLj7A8HPBWl8wlJ2UuJExg7vQoEaS7zgiIiHhj6e3472sdYwYn8X7N51AYlxofUWjziMHsXX3Xu6dMrfsEg79WviOIyISMqolxDF2aDpL8nfx9OdLfcf5HypsBzHu/Xls21PEA8MyiI3ROWsiIuWd0qE+g7o24qnpi1mSv9N3nP+iwnYA3y7ZxFuZq7nmxNZ0alTDdxwRkZA0+tzOJMXHMHJCdkid26bCtp+CohJGTsimeZ1q3HSazlkTETmY+tWTGD6gE98t3czbM1f7jvMfKmz7+fvnS1i6cRf3D00nKT60vhAVEQk1F/VuRq8WtRn3/jw27Sz0HQdQYfsvi/N28I/PFzO0exNOaOf3pHARkXAQE2M8MCyDXYXFjH0vNK7bpsIWUFrqGDE+m5TEOEado+usiYhUVLsG1fntSW2Y8NOakLhumwpbwJuZq/hx+RbuHNiJuqmJvuOIiISVG05pS6t6KYyamOP9um0qbEDejgIeeH8e/VrrOmsiIkcjKT6W+4eks2LTbv46bZHXLCpswL1T5lJQXMq4oRm6zpqIyFHq37Ye5/doyjNfLGX++u3eckR9YZu+II+pWeu48ZS2tE5L9R1HRCSsjTynE9WT4rhzfDalpX7ObYvqwrZ7bzGjJuTQrn4qvz2pje84IiJhr05KAqPO6cyslVt5/YeVXjJEdWF7/JOFrNm6h3HDMkiIi+pNISJSaYb1aMJxbevy0IfzydteUOXrj9rf5jlrtvH818u5uE9zeres4zuOiEjEMDPGDsmgsLiUMVPmVvn6o7KwlQTOWauTksDwAR19xxERiTit6qXwh1Pb8l72Oj6bv6FK1x2Vhe3Fb5aTvWYbd5/bmZrJ8b7jiIhEpGtPbEP7BqncNTGXXYXFVbbeqCtsa7bu4dGPF3BKhzTOyWjkO46ISMRKiIth3NAM1mzdw+OfLKyy9UZVYXPOMXpiDs7BfUPSdc6aiEiQ9WpZh0v6Nuf5r5eRs2ZblawzqgrbBznrmTY/j1vPbE/T2tV8xxERiQp3nN2RuqmJDB+fRXFJadDXFzWFbXtBEfdMziW9SQ2u6N/SdxwRkahRMzmee87tQs6a7bzw9fKgry9qCtufP5zPxp2FPDC0K3GxUfPXFhEJCQMzGnJ6p/o89slCVm3eHdR1RcVv+NVbdvPvH1bxm+NakdG0pu84IiJRx8y497x0Ygyemr44qOuKC+pPDxFNa1fj3ev7066+ekGKiPjSuFYyL1/Vly6NawR1PVFR2AC6NavlO4KISNTr2aJ20NcRFYciRUQkeqiwiYhIRFFhExGRiKLCJiIiEUWFTUREIkrQCpuZPW9meWaWU26sjpl9YmaLAn8Gf3qMiIhElWDusb0InL3f2HBgmnOuHTAtcF9ERKTSBK2wOee+ADbvN3we8FLg9kvAkGCtX0REolNVf8fWwDm3LnB7PdDgYE80s2vNLNPMMvPz86smnYiIhD1vk0eccw5wh3j8GedcL+dcr7S0tCpMJiIi4ayqW2ptMLNGzrl1ZtYIyKvIi2bOnLnRzFZUwvrrARsr4edUFeUNvnDLHG55IfwyK2/wVVbmFgcarOrCNhm4HHgw8OekirzIOVcpu2xmlumc61UZP6sqKG/whVvmcMsL4ZdZeYMv2JmDOd3/38C3QAczW21mV1FW0M4ws0XA6YH7IiIilSZoe2zOuYsP8tBpwVqniIhItHUeecZ3gCOkvMEXbpnDLS+EX2blDb6gZrayyYkiIiKRIdr22EREJMJFZGEzswvNLNfMSs2s136PjTCzxWa2wMzOKjd+dmBssZl5bfVlZm+a2ezAstzMZgfGW5rZnnKPPe0z5z5mdo+ZrSmXa2C5xw64vX0ys4fNbL6ZZZnZBDOrFRgPye27Tyi9Rw/EzJqZ2XQzmxv4/3dTYPyg749QEPg/lh3IlhkYC8m+tmbWodx2nG1m283sj6G0jY+kT7CV+WvgPZ1lZj0qJYRzLuIWoBPQAfgc6FVuvDMwB0gEWgFLgNjAsgRoDSQEntPZ998jkPlRYHTgdksgx3emA2S8B7jtAOMH3N4hkPdMIC5w+yHgoVDevoFsIfseLZexEdAjcLs6sDDwHjjg+yNUFmA5UG+/sT8DwwO3h+97j4TSEnhPrKfsXK6Q2cbAiUCP8v+XDrY9gYHAB4AB/YDvKyNDRO6xOefmOecWHOCh84A3nHOFzrllwGKgT2BZ7Jxb6pzbC7wReK5XZmbAL4B/+85ylA62vb1yzn3snCsO3P0OaOozTwWF5Hu0POfcOufcrMDtHcA8oInfVEctHPrangYscc5VRvOKSuOOrE/wecDLrsx3QK1A846fJSIL2yE0AVaVu786MHawcd9OADY45xaVG2tlZj+Z2QwzO8FXsAO4MXAo4flyh21CdbuWdyVlnxj3CdXtGw7b8j/MrCXQHfg+MHSg90eocMDHZjbTzK4NjFW4r61HF/HfH3pDeRsfbHsG5X0dtoXNzD41s5wDLCH1KfZgKpj/Yv77jbsOaO6c6w7cArxuZjVCIO8/gDZAt0DGR6si06FUZPua2UigGHgtMORt+0YSM0sF3gX+6JzbTgi+P/ZzvHOuBzAAuMHMTiz/oCs7ZhZS08fNLAEYDLwdGAr1bfwfVbE9q7qlVqVxzp1+FC9bAzQrd79pYIxDjAfF4fKbWRwwDOhZ7jWFQGHg9kwzWwK0BzKDGHXfuiu0vc3sX8DUwN1Dbe+gqsD2vQIYBJwW+I/mdftWgLdteSTMLJ6yovaac248gHNuQ7nHy78/QoJzbk3gzzwzm0DZYd+j6mtbhQYAs/Zt21Dfxhx8ewblfR22e2xHaTJwkZklmlkroB3wA/Aj0M7MWgU+CV0UeK5PpwPznXOr9w2YWZqZxQZut6Ys/1JP+f5jv2PiQ4F9s6EOtr29MrOzgduBwc653eXGQ3L7BoTie/S/BL4Tfg6Y55x7rNz4wd4f3plZiplV33ebsolFOfx/X1s4gr62Vei/juaE8jYOONj2nAz8OjA7sh+wrdwhy6PnewZNMBbK/mFXU/bpewPwUbnHRlI2u2wBMKDc+EDKZnEtAUaGwN/hReC3+42dD+QCs4FZwLm+cwZyvQJkA1mBN2qjw21vz3kXU3Zcf3ZgeTqUt2+53CH1Hj1AvuMpO8SUVW7bDjzU+8P3Qtks0zmBJXffdgXqAtOARcCnQB3fWctlTgE2ATXLjYXMNqas4K4DigK/h6862PakbDbkU4H3dDblZrH/nEWdR0REJKJE26FIERGJcCpsIiISUVTYREQkoqiwiYhIRFFhExGRiKLCJhJCzGyn7wwi4U6FTUREIooKm0iIM7Nzzez7QHPmT82sQWA8LXBtq1wze9bMVphZPd95RXxTYRMJfV8B/VxZc+Y3KGsHBnA38JlzrgvwDtDcUz6RkBK2TZBFokhT4M1AP8AEYFlg/HjK2sfhnPvQzLZ4yicSUrTHJhL6ngT+5pzLAK4DkjznEQlpKmwioa8m/38pj8vLjX9N2RXWMbMzgVC7uKSIF2qCLBJCzKwUWFtu6DHKOp8/DmwBPgN6O+dONrP6lHVSbwB8S9n15Vq6suvKiUQtFTaRMGVmiUCJc67YzI4F/uGc6+Y5loh3mjwiEr6aA2+ZWQywF7jGcx6RkKA9NhERiSiaPCIiIhFFhU1ERCKKCpuIiEQUFTYREYkoKmwiIhJRVNhERCSi/B9qNjdnOkrYIwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lags, ac_fct_critical = auto_cov_fct(acts=states_critical)\n", "\n", "# Plotting the auto-correlation function.\n", "plt.figure(figsize=(7,5))\n", "plt.xlabel('Lag')\n", "plt.ylabel('Correlation')\n", "plt.plot(lags, ac_fct_critical)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4341b0b7", "metadata": {}, "source": [ "We moreover see that for positive time lags the auto-covariance function still is large.
\n", "This means that the network has memory of its previous states: The state at a given point in time influences strongly the subsequent path of the trajectories of the neurons.
\n", "Such a network can perform meaningful computations." ] }, { "cell_type": "markdown", "id": "596b5dab", "metadata": {}, "source": [ "### LIF Neurons\n", "We now turn to a E/I networks implementing its dynamic behavior with leaky integrate-and-fire neurons.
\n", "For this, we harness the concepts of Hierarchical Lava Processes and SubProcessModels. These allow us to avoid implementing everything ourselves, but rather to use already defined Processes and their ProcessModels to build more complicated programs.
\n", "We here use the behavior defined for the [LIF](https://github.com/lava-nc/lava/tree/main/src/lava/proc/lif \"Lava's LIF neuron\") and [Dense](https://github.com/lava-nc/lava/tree/main/src/lava/proc/dense \"Lava's Dense Connectivity\") Processes, we define the behavior of the E/I Network Process.
\n", "Moreover, we would like to place the LIF E/I network in a similar dynamical regime as the rate network. This is a difficult task since the underlying single neurons dynamics are quite different. We here provide an approximate conversion function that allows for a parameter mapping and especially qualitatively retains properties of the auto-covariance function.
\n", "With the implementation below, we may either pass LIF specific parameters directly **or** use the same parameters needed for instantiating the rate E/I network and then convert them automatically.
" ] }, { "cell_type": "code", "execution_count": 15, "id": "6dc54408", "metadata": {}, "outputs": [], "source": [ "from lava.proc.dense.process import Dense\n", "from lava.proc.lif.process import LIF\n", "from convert_params import convert_rate_to_lif_params\n", "\n", "@implements(proc=EINetwork, protocol=LoihiProtocol)\n", "@tag('lif_neurons')\n", "class SubEINetworkModel(AbstractSubProcessModel):\n", " def __init__(self, proc):\n", " \n", " convert = proc.proc_params.get('convert', False)\n", " \n", " if convert:\n", " proc_params = proc.proc_params._parameters\n", " # Convert rate parameters to LIF parameters.\n", " # The mapping is based on:\n", " # A unified view on weakly correlated recurrent network, Grytskyy et al., 2013.\n", " lif_params = convert_rate_to_lif_params(**proc_params)\n", " \n", " for key, val in lif_params.items():\n", " try:\n", " proc.proc_params.__setitem__(key, val)\n", " except KeyError:\n", " if key == 'weights':\n", " # Weights need to be updated.\n", " proc.proc_params._parameters[key] = val\n", " else:\n", " continue\n", " \n", " # Fetch values for excitatory neurons or set default.\n", " shape_exc = proc.proc_params.get('shape_exc')\n", " shape_inh = proc.proc_params.get('shape_inh')\n", " du_exc = proc.proc_params.get('du_exc')\n", " dv_exc = proc.proc_params.get('dv_exc')\n", " vth_exc = proc.proc_params.get('vth_exc')\n", " bias_mant_exc = proc.proc_params.get('bias_mant_exc')\n", " bias_exp_exc = proc.proc_params.get('bias_exp_exc', 0)\n", " \n", " \n", " # Fetch values for inhibitory neurons or set default.\n", " du_inh = proc.proc_params.get('du_inh')\n", " dv_inh = proc.proc_params.get('dv_inh')\n", " vth_inh = proc.proc_params.get('vth_inh')\n", " bias_mant_inh = proc.proc_params.get('bias_mant_inh')\n", " bias_exp_inh = proc.proc_params.get('bias_exp_inh', 0)\n", " \n", " # Create parameters for full network.\n", " du_full = np.array([du_exc] * shape_exc\n", " + [du_inh] * shape_inh)\n", " dv_full = np.array([dv_exc] * shape_exc\n", " + [dv_inh] * shape_inh)\n", " vth_full = np.array([vth_exc] * shape_exc \n", " + [vth_inh] * shape_inh)\n", " bias_mant_full = np.array([bias_mant_exc] * shape_exc \n", " + [bias_mant_inh] * shape_inh)\n", " bias_exp_full = np.array([bias_exp_exc] * shape_exc +\n", " [bias_exp_inh] * shape_inh)\n", " weights = proc.proc_params.get('weights')\n", " weight_exp = proc.proc_params.get('weight_exp', 0)\n", " \n", " full_shape = shape_exc + shape_inh\n", "\n", " # Instantiate LIF and Dense Lava Processes.\n", " self.lif = LIF(shape=(full_shape,),\n", " du=du_full,\n", " dv=dv_full,\n", " vth=vth_full,\n", " bias_mant=bias_mant_full,\n", " bias_exp=bias_exp_full)\n", "\n", " self.dense = Dense(weights=weights,\n", " weight_exp=weight_exp)\n", " \n", " \n", " # Recurrently connect neurons to E/I Network.\n", " self.lif.s_out.connect(self.dense.s_in)\n", " self.dense.a_out.connect(self.lif.a_in)\n", "\n", " # Connect incoming activation to neurons and elicited spikes to ouport.\n", " proc.inport.connect(self.lif.a_in)\n", " self.lif.s_out.connect(proc.outport)\n", " \n", " # Alias v with state and u with state_alt.\n", " proc.vars.state.alias(self.lif.vars.v)\n", " proc.vars.state_alt.alias(self.lif.vars.u)" ] }, { "cell_type": "markdown", "id": "bd11399c", "metadata": {}, "source": [ "#### Execution and Results\n", "In order to execute the LIF E/I network and the infrastructure to monitor the activity, we introduce a ```CustomRunConfig``` where we specify which ProcessModel we select for execution." ] }, { "cell_type": "code", "execution_count": 16, "id": "37865890", "metadata": {}, "outputs": [], "source": [ "from lava.magma.core.run_conditions import RunSteps\n", "from lava.magma.core.run_configs import Loihi1SimCfg\n", "# Import io processes.\n", "from lava.proc import io\n", "\n", "from lava.proc.dense.models import PyDenseModelFloat\n", "from lava.proc.lif.models import PyLifModelFloat\n", "\n", "\n", "# Configurations for execution.\n", "num_steps = 1000\n", "run_cond = RunSteps(num_steps=num_steps)\n", "\n", "class CustomRunConfigFloat(Loihi1SimCfg):\n", " def select(self, proc, proc_models):\n", " # Customize run config to always use float model for io.sink.RingBuffer.\n", " if isinstance(proc, io.sink.RingBuffer):\n", " return io.sink.PyReceiveModelFloat\n", " if isinstance(proc, LIF):\n", " return PyLifModelFloat\n", " elif isinstance(proc, Dense):\n", " return PyDenseModelFloat\n", " else:\n", " return super().select(proc, proc_models)\n", " \n", "rcfg = CustomRunConfigFloat(select_tag='lif_neurons', select_sub_proc_model=True)\n", "\n", "# Instantiating network and IO processes.\n", "lif_network_balanced = EINetwork( **network_params_balanced, convert=True)\n", "outport_plug = io.sink.RingBuffer(shape=shape, buffer=num_steps)\n", "\n", "# Instantiate Monitors to record the voltage and the current of the LIF neurons.\n", "monitor_v = Monitor()\n", "monitor_u = Monitor()\n", "\n", "lif_network_balanced.outport.connect(outport_plug.a_in)\n", "monitor_v.probe(target=lif_network_balanced.state, num_steps=num_steps)\n", "monitor_u.probe(target=lif_network_balanced.state_alt, num_steps=num_steps)\n", "\n", "lif_network_balanced.run(condition=run_cond, run_cfg=rcfg)\n", "\n", "# Fetching spiking activity.\n", "spks_balanced = outport_plug.data.get()\n", "data_v_balanced = monitor_v.get_data()[lif_network_balanced.name][lif_network_balanced.state.name]\n", "data_u_balanced = monitor_u.get_data()[lif_network_balanced.name][lif_network_balanced.state_alt.name]\n", "\n", "lif_network_balanced.stop()" ] }, { "cell_type": "markdown", "id": "3c02ce1d", "metadata": {}, "source": [ "#### Visualizing the activity\n", "First, we visually inspect to spiking activity of the neurons in the network.
\n", "To this end, we display neurons on the vertical axis and mark the time step when a neuron spiked." ] }, { "cell_type": "code", "execution_count": 17, "id": "abac04ec", "metadata": {}, "outputs": [], "source": [ "def raster_plot(spks, stride=6, fig=None, color='b', alpha=1):\n", " \"\"\"Generate raster plot of spiking activity.\n", " \n", " Parameters\n", " ----------\n", " \n", " spks : np.ndarray shape (num_neurons, timesteps)\n", " Spiking activity of neurons, a spike is indicated by a one \n", " stride : int\n", " Stride for plotting neurons\n", " \"\"\"\n", " num_time_steps = spks.shape[1]\n", " assert stride < num_time_steps, \"Stride must be smaller than number of time steps\"\n", " \n", " time_steps = np.arange(0, num_time_steps, 1)\n", " if fig is None:\n", " fig = plt.figure(figsize=(10,5))\n", " timesteps = spks.shape[1]\n", " \n", " plt.xlim(-1, num_time_steps)\n", " plt.yticks([])\n", " \n", " plt.xlabel('Time steps')\n", " plt.ylabel('Neurons')\n", " \n", " for i in range(0, dim, stride):\n", " spike_times = time_steps[spks[i] == 1]\n", " plt.plot(spike_times,\n", " i * np.ones(spike_times.shape),\n", " linestyle=' ',\n", " marker='o',\n", " markersize=1.5,\n", " color=color,\n", " alpha=alpha)\n", " \n", " return fig " ] }, { "cell_type": "code", "execution_count": 18, "id": "80307fe2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = raster_plot(spks=spks_balanced)" ] }, { "cell_type": "markdown", "id": "81e1c3a9", "metadata": {}, "source": [ "After an initial synchronous burst (all neurons are simultaneously driven to the threshold by the external current), we observe an immediate decoupling of the single neuron activities due to the recurrent connectivity.
\n", "Overall, we see a heterogeneous network state with asynchronous as well as synchronous spiking across neurons.
\n", "This network state resembles qualitatively the fixed point observed above for the rate network.
\n", "Before we turn to the study of the auto-covariance we need to address a subtlety in the comparison of spiking and rate network.
\n", "Comparing spike trains and rates directly is difficult due dynamics of single spiking neurons: Most of the time, a neuron does not spike!
\n", "To overcome this problem and meaningfully compare quantities like the auto-covariance function, we follow the usual approach and bin the spikes. This means, we apply a sliding box-car window of a given length and count at each time step the spikes in that window to obtain an estimate of the rate.
" ] }, { "cell_type": "code", "execution_count": 19, "id": "f5cc39b8", "metadata": {}, "outputs": [], "source": [ "window_size = 25\n", "window = np.ones(window_size) # Window size of 25 time steps for binning.\n", "binned_sps_balanced = np.asarray([np.convolve(spks_balanced[i], window) for i in range(dim)])[:, :-window_size + 1]" ] }, { "cell_type": "markdown", "id": "5b858cd5", "metadata": {}, "source": [ "After having an estimate of the rate, we compare the temporally-averaged mean rate of both networks in the first state.
\n", "To avoid boundary effects of the binning, we disregard time steps at the beginning and the end." ] }, { "cell_type": "code", "execution_count": 20, "id": "d6c68e05", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "offset = 50\n", "timesteps = np.arange(0,1000, 1)[offset: -offset]\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,5))\n", "ax1.set_title('Mean rate of Rate network')\n", "ax1.plot(timesteps,\n", " (states_balanced - states_balanced.mean(axis=0)[np.newaxis,:]).mean(axis=1)[offset: -offset])\n", "ax1.set_ylabel('Mean rate')\n", "ax1.set_xlabel('Time Step')\n", "ax2.set_title('Mean rate of LIF network')\n", "ax2.plot(timesteps,\n", " (binned_sps_balanced - np.mean(binned_sps_balanced, axis=1)[:, np.newaxis]).T.mean(axis=1)[offset: -offset])\n", "ax2.set_xlabel('Time Step')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "db10dec2", "metadata": {}, "source": [ "Both networks behave similarly inasmuch the rates are stationary with only very small fluctuations around the baseline in the LIF case.
\n", "Next, we turn to the auto-covariance function." ] }, { "cell_type": "code", "execution_count": 21, "id": "9036e802", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lags, ac_fct = auto_cov_fct(acts=binned_sps_balanced.T)\n", "\n", "# Plotting the auto-covariance function.\n", "plt.figure(figsize=(7,5))\n", "plt.xlabel('Lag')\n", "plt.ylabel('Covariance')\n", "plt.plot(lags, ac_fct)" ] }, { "cell_type": "markdown", "id": "52339454", "metadata": {}, "source": [ "Examining the auto-covariance function, we first note that again the overall values are small. Moreover, we see that for non-vanishing time lags the auto-covariance function quickly decays.
\n", "This means that the network has no memory of its previous states: Already after few time step we lost almost all information of the previous network state, former states leave little trace in the overall network activity.
\n", "Such a network is unfit to perform meaningful computation." ] }, { "cell_type": "markdown", "id": "91374486", "metadata": {}, "source": [ "#### Controlling the network\n", "Next, we pass the rate network parameters for which we increased the `q_factor` to the spiking E/I network.
\n", "Dynamically, this increase again should result in a fundamentally different network state." ] }, { "cell_type": "code", "execution_count": 22, "id": "78d091ea", "metadata": {}, "outputs": [], "source": [ "num_steps = 1000\n", "rcfg = CustomRunConfigFloat(select_tag='lif_neurons', select_sub_proc_model=True)\n", "run_cond = RunSteps(num_steps=num_steps)\n", "\n", "# Creating new new network with changed weights.\n", "lif_network_critical = EINetwork(**network_params_critical, convert=True)\n", "outport_plug = io.sink.RingBuffer(shape=shape, buffer=num_steps)\n", "\n", "# Instantiate Monitors to record the voltage and the current of the LIF neurons.\n", "monitor_v = Monitor()\n", "monitor_u = Monitor()\n", "\n", "lif_network_critical.outport.connect(outport_plug.a_in)\n", "monitor_v.probe(target=lif_network_critical.state, num_steps=num_steps)\n", "monitor_u.probe(target=lif_network_critical.state_alt, num_steps=num_steps)\n", "\n", "lif_network_critical.run(condition=run_cond, run_cfg=rcfg)\n", "\n", "# Fetching spiking activity.\n", "spks_critical = outport_plug.data.get()\n", "data_v_critical = monitor_v.get_data()[lif_network_critical.name][lif_network_critical.state.name]\n", "data_u_critical = monitor_u.get_data()[lif_network_critical.name][lif_network_critical.state_alt.name]\n", "\n", "lif_network_critical.stop()" ] }, { "cell_type": "code", "execution_count": 23, "id": "bd6aba46", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = raster_plot(spks=spks_critical)" ] }, { "cell_type": "markdown", "id": "3fcf5169", "metadata": {}, "source": [ "Here we see a qualitatively different network activity where the recurrent connections play a more dominant role:
\n", "At seemingly random times, single neurons enter an active states of variable length.
\n", "Next, we have a look at the auto-covariance function of the network, especially in direct comparison with the respective function of the rate network." ] }, { "cell_type": "code", "execution_count": 24, "id": "36559ace", "metadata": {}, "outputs": [], "source": [ "window = np.ones(window_size)\n", "binned_sps_critical = np.asarray([np.convolve(spks_critical[i], window) for i in range(dim)])[:, :-window_size + 1]\n", "lags, ac_fct_lif_critical = auto_cov_fct(acts=binned_sps_critical.T)" ] }, { "cell_type": "markdown", "id": "94745334", "metadata": {}, "source": [ "We again compare the rate of both networks in the same state." ] }, { "cell_type": "code", "execution_count": 25, "id": "25e27549", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "offset = 50\n", "timesteps = np.arange(0,1000, 1)[offset: -offset]\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,5))\n", "ax1.set_title('Mean rate of Rate network')\n", "ax1.plot(timesteps,\n", " (states_critical - states_critical.mean(axis=0)[np.newaxis,:]).mean(axis=1)[offset: -offset])\n", "ax1.set_ylabel('Mean rate')\n", "ax1.set_xlabel('Time Step')\n", "ax2.set_title('Mean rate of LIF network')\n", "ax2.plot(timesteps,\n", " (binned_sps_critical - np.mean(binned_sps_critical, axis=1)[:, np.newaxis]).T.mean(axis=1)[offset: -offset])\n", "ax2.set_xlabel('Time Step')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a1134ac2", "metadata": {}, "source": [ "Again, we observe a similar behavior on the rate level:
\n", "In both networks the mean rate fluctuates on a longer time scale with larger values around the baseline in a similar range.
\n", "Next we compare the auto-covariance functions:" ] }, { "cell_type": "code", "execution_count": 26, "id": "2c79c458", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", "ax1.plot(lags, ac_fct_lif_critical)\n", "ax1.set_title('Auto-Cov function: LIF network')\n", "ax1.set_xlabel('Lag')\n", "ax1.set_ylabel('Covariance')\n", "ax2.plot(lags, ac_fct_critical)\n", "ax2.set_title('Auto-Cov function: Rate network')\n", "ax2.set_xlabel('Lag')\n", "ax2.set_ylabel('Covariance')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "302dc265", "metadata": {}, "source": [ "We observe in the auto-covariance function of the LIF network a slowly decay, akin to the rate network.
\n", "Even though both auto-covariance functions are not identical, they qualitatively match in that both networks exhibit long-lasting temporal correlations and an activity at the edge of chaos.
\n", "This implies that both network are in a suitable regime for computation, e.g. in the context of reservoir computing." ] }, { "cell_type": "markdown", "id": "c4278dc3", "metadata": {}, "source": [ "#### DIfferent recurrent activation regimes\n", "After having observed these two radically different dynamical states also in the LIF network, we next turn to the question how they come about.
\n", "The difference between both version of LIF E/I networks is in the recurrently provided activations.
\n", "We now examine these activations by having look at the excitatory, inhibitory as well as total activation provided to each neuron in both networks." ] }, { "cell_type": "code", "execution_count": 27, "id": "a9f2f809", "metadata": {}, "outputs": [], "source": [ "def calculate_activation(weights, spks, num_exc_neurons):\n", " \"\"\"Calculate excitatory, inhibitory and total activation to neurons.\n", " \n", " Parameters\n", " ----------\n", " \n", " weights : np.ndarray (num_neurons, num_neurons)\n", " Weights of recurrent connections\n", " spks : np.ndarray (num_neurons, num_time_steps)\n", " Spike times of neurons, 0 if neuron did not spike, 1 otherwise\n", " num_exc_neurons : int\n", " Number of excitatory neurons\n", " \n", " Returns\n", " -------\n", " \n", " activation_exc : np.ndarray (num_neurons, num_time_steps)\n", " Excitatory activation provided to neurons\n", " activation_inh : np.ndarray (num_neurons, num_time_steps)\n", " Inhibitory activation provided to neurons\n", " activations_total : np.ndarray (num_neurons, num_time_steps)\n", " Total activation provided to neurons\n", " \"\"\"\n", " \n", " weights_exc = weights[:, :num_exc_neurons]\n", " weights_inh = weights[:, num_exc_neurons:]\n", " \n", " spks_exc = spks[:num_exc_neurons]\n", " spks_inh = spks[num_exc_neurons:]\n", " \n", " activation_exc = np.matmul(weights_exc, spks_exc)\n", " activation_inh = np.matmul(weights_inh, spks_inh)\n", " \n", " activation_total = activation_exc + activation_inh\n", " \n", " return activation_exc, activation_inh, activation_total" ] }, { "cell_type": "markdown", "id": "3cc203e4", "metadata": {}, "source": [ "Since the network needs some time to settle in it's dynamical state, we discard the first $200$ time steps." ] }, { "cell_type": "code", "execution_count": 28, "id": "f199fbe1", "metadata": {}, "outputs": [], "source": [ "offset = 200\n", "\n", "act_exc_balanced, act_inh_balanced, act_tot_balanced \\\n", " = calculate_activation(lif_network_balanced.proc_params.get('weights'),\n", " spks_balanced[:,offset:],\n", " network_params_balanced['shape_exc'])\n", "\n", "act_exc_critical, act_inh_critical, act_tot_critical \\\n", " = calculate_activation(lif_network_critical.proc_params.get('weights'),\n", " spks_critical[:,offset:],\n", " network_params_balanced['shape_exc'])" ] }, { "cell_type": "markdown", "id": "ac240c27", "metadata": {}, "source": [ "First, we look at the distribution of activation of a random neuron in both network states." ] }, { "cell_type": "code", "execution_count": 29, "id": "fb934b23-0bdd-41fe-a798-493e0fd75024", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rnd_neuron = 4\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", "ax1.set_title('Low weights')\n", "ax1.set_xlabel('Activation')\n", "ax1.set_ylabel('Density')\n", "ax1.hist(act_exc_balanced[rnd_neuron], bins=10, alpha=0.5, density=True, label='E')\n", "ax1.hist(act_inh_balanced[rnd_neuron], bins=10, alpha=0.5, density=True, label='I'),\n", "ax1.hist(act_tot_balanced[rnd_neuron], bins=10, alpha=0.5, density=True, label='Total')\n", "ax1.legend()\n", "\n", "ax2.set_title('High weights')\n", "ax2.set_xlabel('Activation')\n", "ax2.set_ylabel('Density')\n", "ax2.hist(act_exc_critical[rnd_neuron], bins=10, alpha=0.5, density=True, label='E')\n", "ax2.hist(act_inh_critical[rnd_neuron], bins=10, alpha=0.5, density=True, label='I')\n", "ax2.hist(act_tot_critical[rnd_neuron], bins=10, alpha=0.5, density=True, label='Total')\n", "ax2.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a3afd82d", "metadata": {}, "source": [ "Next, we plot the distribution of the temporal average:" ] }, { "cell_type": "code", "execution_count": 30, "id": "22933de6", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", "ax1.set_title('Low weights')\n", "ax1.set_xlabel('Activation')\n", "ax1.set_ylabel('Density')\n", "ax1.hist(act_exc_balanced.mean(axis=0), bins=10, alpha=0.5, density=True, label='E')\n", "ax1.hist(act_inh_balanced.mean(axis=0), bins=10, alpha=0.5, density=True, label='I'),\n", "ax1.hist(act_tot_balanced.mean(axis=0), bins=10, alpha=0.5, density=True, label='Total')\n", "ax1.legend()\n", "\n", "ax2.set_title('High weights')\n", "ax2.set_xlabel('Activation')\n", "ax2.set_ylabel('Density')\n", "ax2.hist(act_exc_critical.mean(axis=0), bins=10, alpha=0.5, density=True, label='E')\n", "ax2.hist(act_inh_critical.mean(axis=0), bins=10, alpha=0.5, density=True, label='I')\n", "ax2.hist(act_tot_critical.mean(axis=0), bins=10, alpha=0.5, density=True, label='Total')\n", "ax2.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a22be55c", "metadata": {}, "source": [ "We first note that the the total activation is close to zero with a slight shift to negative values, this prevents the divergence of activity.
\n", "Secondly, we observe that the width of the distributions is orders of magnitude larger in the high weight case as compared to the low weight network.
\n", "Finally, we look at the evolution of the mean activation over time. To this end we plot three random sample:" ] }, { "cell_type": "code", "execution_count": 31, "id": "d7d38f3d", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "time_steps = np.arange(offset, num_steps, 1)\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,4))\n", "ax1.set_title('Low weights')\n", "ax1.set_xlabel('Time step')\n", "ax1.set_ylabel('Activation')\n", "for i in range(3):\n", " ax1.plot(time_steps, act_tot_balanced[i], alpha=0.7)\n", "\n", "\n", "ax2.set_title('High weights')\n", "ax2.set_xlabel('Time step')\n", "ax2.set_ylabel('Activation')\n", "for i in range(3):\n", " ax2.plot(time_steps, act_tot_critical[i], alpha=0.7)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "88197c24", "metadata": {}, "source": [ "We see that the temporal evolution of the total activation in the low weights case is much narrower than in the high weights network.
\n", "Moreover, we see that in the high weights network, the fluctuations of the activations evolve on a very long time scale as compared to the other network.
\n", "This implies that a neuron can sustain it's active, bursting state over longer periods of time leading to memory in the network as well as activity at the edge of chaos.
" ] }, { "cell_type": "markdown", "id": "a3358a9d", "metadata": {}, "source": [ "### Running a ProcessModel bit-accurate with Loihi\n", "So far, we have used neuron models and weights that are internally represented as floating point numbers.
\n", "Next, we turn to bit-accurate implementations of the LIF and Dense process where only a fixed precision for the numerical values is allowed. Here, the parameters need to be mapped to retain the dynamical behavior of the network.
\n", "First, we define a method for mapping the parameters. It consists of finding an optimal scaling function that consistently maps all appearing floating-point numbers to fixed-point numbers." ] }, { "cell_type": "code", "execution_count": 32, "id": "3cf38774", "metadata": {}, "outputs": [], "source": [ "def _scaling_funct(params):\n", " '''Find optimal scaling function for float- to fixed-point mapping.\n", " \n", " Parameter\n", " ---------\n", " params : dict\n", " Dictionary containing information required for float- to fixed-point mapping\n", " \n", " Returns\n", " ------\n", " scaling_funct : callable\n", " Optimal scaling function for float- to fixed-point conversion\n", " ''' \n", " sorted_params = dict(sorted(params.items(), key=lambda x: np.max(np.abs(x[1]['val'])), reverse=True))\n", " \n", " # Initialize scaling function.\n", " scaling_funct = None\n", " \n", " for key, val in sorted_params.items(): \n", " if val['signed'] == 's':\n", " signed_shift = 1\n", " else:\n", " signed_shift = 0\n", " \n", " if np.max(val['val']) == np.max(np.abs(val['val'])):\n", " max_abs = True\n", " max_abs_val = np.max(val['val'])\n", " else:\n", " max_abs = False\n", " max_abs_val = np.max(np.abs(val['val']))\n", " \n", " if max_abs:\n", " rep_val = 2**(val['bits'] - signed_shift) - 1\n", " else:\n", " rep_val = 2**(val['bits'] - signed_shift)\n", " \n", " max_shift = np.max(val['shift'])\n", " \n", " max_rep_val = rep_val * 2**max_shift\n", " \n", " if scaling_funct:\n", " scaled_vals = scaling_funct(val['val'])\n", "\n", " max_abs_scaled_vals = np.max(np.abs(scaled_vals))\n", " if max_abs_scaled_vals <= max_rep_val:\n", " continue\n", " else:\n", " p1 = max_rep_val\n", " p2 = max_abs_val\n", " \n", " else:\n", " p1 = max_rep_val\n", " p2 = max_abs_val \n", " \n", " scaling_funct = lambda x: p1 / p2 * x\n", " \n", " return scaling_funct\n", "\n", "def float2fixed_lif_parameter(lif_params):\n", " '''Float- to fixed-point mapping for LIF parameters.\n", " \n", " Parameters\n", " ---------\n", " lif_params : dict\n", " Dictionary with parameters for LIF network with floating-point ProcModel\n", " \n", " Returns\n", " ------\n", " lif_params_fixed : dict\n", " Dictionary with parameters for LIF network with fixed-point ProcModel\n", " '''\n", " \n", " scaling_funct = _scaling_funct(params)\n", " \n", " bias_mant_bits = params['bias']['bits']\n", " scaled_bias = scaling_funct(params['bias']['val'])[0]\n", " bias_exp = int(np.ceil(np.log2(scaled_bias) - bias_mant_bits + 1))\n", " if bias_exp <=0:\n", " bias_exp = 0\n", " \n", " \n", " weight_mant_bits = params['weights']['bits'] \n", " scaled_weights = np.round(scaling_funct(params['weights']['val']))\n", " weight_exp = int(np.ceil(np.log2(scaled_bias) - weight_mant_bits + 1))\n", " weight_exp = np.max(weight_exp) - 6\n", " if weight_exp <=0:\n", " diff = weight_exp\n", " weight_exp = 0\n", "\n", " \n", " bias_mant = int(scaled_bias // 2**bias_exp)\n", " weights = scaled_weights.astype(np.int32)\n", " \n", " lif_params_fixed = {'vth' : int(scaling_funct(params['vth']['val']) // 2**params['vth']['shift'][0]),\n", " 'bias_mant': bias_mant,\n", " 'bias_exp': bias_exp,\n", " 'weights': np.round(scaled_weights / (2 ** params['weights']['shift'][0])).astype(np.int32),\n", " 'weight_exp': weight_exp}\n", " \n", " return lif_params_fixed\n", "\n", "def scaling_funct_dudv(val):\n", " '''Scaling function for du, dv in LIF\n", " ''' \n", " assert val < 1, 'Passed value must be smaller than 1'\n", " \n", " return np.round(val * 2 ** 12).astype(np.int32)" ] }, { "cell_type": "markdown", "id": "4b43d69c", "metadata": {}, "source": [ "After having defined some primitive conversion functionality we next convert the parameters for the critical network.
\n", "To constrain the values that we need to represent in the bit-accurate model, we have to find the dynamical range of the state parameters of the network, namely ```u``` and ```v``` of the LIF neurons." ] }, { "cell_type": "code", "execution_count": 33, "id": "3d0043ce-2f6f-4f37-8b61-4a8607bac86b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", "ax1.set_title('u')\n", "ax1.set_xlabel('Current')\n", "ax1.set_ylabel('Density')\n", "ax1.hist(data_u_critical.flatten(), bins='auto', density=True)\n", "ax1.legend()\n", "\n", "ax2.set_title('v')\n", "ax2.set_xlabel('Voltage')\n", "ax2.set_ylabel('Density')\n", "ax2.hist(data_v_critical.flatten(), bins='auto', density=True)\n", "ax2.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "933c833f-f492-46ad-b2af-d101cb401f33", "metadata": {}, "source": [ "We note that for both variables the distributions attain large (small) values with low probability. We hence will remove them in the dynamical range to increase the precision of the overall representation. We do so by choosing $0.2$ and $0.8$ quantiles as minimal resp. maximal values for the dynamic ranges.
\n", "We finally also need to pass some information about the concrete implementation, e.g. the precision and the bit shifts performed.
" ] }, { "cell_type": "code", "execution_count": 34, "id": "daab0580-90a7-4e55-97c3-fcf596399f74", "metadata": {}, "outputs": [], "source": [ "u_low = np.quantile(data_u_critical.flatten(), 0.2)\n", "u_high = np.quantile(data_u_critical.flatten(), 0.8)\n", "v_low = np.quantile(data_v_critical.flatten(), 0.2)\n", "v_high = np.quantile(data_v_critical.flatten(), 0.8)\n", "\n", "lif_params_critical = convert_rate_to_lif_params(**network_params_critical)\n", "weights = lif_params_critical['weights']\n", "bias = lif_params_critical['bias_mant_exc']\n", "\n", "params = {'vth': {'bits': 17, 'signed': 'u', 'shift': np.array([6]), 'val': np.array([1])},\n", " 'u': {'bits': 24, 'signed': 's', 'shift': np.array([0]), 'val': np.array([u_low, u_high])},\n", " 'v': {'bits': 24, 'signed': 's', 'shift': np.array([0]), 'val': np.array([v_low, v_high])},\n", " 'bias': {'bits': 13, 'signed': 's', 'shift': np.arange(0, 3, 1), 'val': np.array([bias])},\n", " 'weights' : {'bits': 8, 'signed': 's', 'shift': np.arange(6,22,1), 'val': weights}}\n", "\n", "mapped_params = float2fixed_lif_parameter(params)" ] }, { "cell_type": "markdown", "id": "792f70a8-4ad6-4827-a131-9606df1026c7", "metadata": {}, "source": [ "Using the mapped parameters, we construct the fully-fledged parameter dictionary for the E/I network Process using the LIF SubProcessModel." ] }, { "cell_type": "code", "execution_count": 35, "id": "c3cfecc6", "metadata": {}, "outputs": [], "source": [ "# Set up parameters for bit accurate model\n", "lif_params_critical_fixed = {'shape_exc': lif_params_critical['shape_exc'],\n", " 'shape_inh': lif_params_critical['shape_inh'],\n", " 'g_factor': lif_params_critical['g_factor'],\n", " 'q_factor': lif_params_critical['q_factor'],\n", " 'vth_exc': mapped_params['vth'],\n", " 'vth_inh': mapped_params['vth'],\n", " 'bias_mant_exc': mapped_params['bias_mant'],\n", " 'bias_exp_exc': mapped_params['bias_exp'],\n", " 'bias_mant_inh': mapped_params['bias_mant'],\n", " 'bias_exp_inh': mapped_params['bias_exp'],\n", " 'weights': mapped_params['weights'],\n", " 'weight_exp': mapped_params['weight_exp'],\n", " 'du_exc': scaling_funct_dudv(lif_params_critical['du_exc']),\n", " 'dv_exc': scaling_funct_dudv(lif_params_critical['dv_exc']),\n", " 'du_inh': scaling_funct_dudv(lif_params_critical['du_inh']),\n", " 'dv_inh': scaling_funct_dudv(lif_params_critical['dv_inh'])}" ] }, { "cell_type": "markdown", "id": "a51256aa", "metadata": {}, "source": [ "#### Execution of bit accurate model" ] }, { "cell_type": "code", "execution_count": 36, "id": "8b10fe25", "metadata": {}, "outputs": [], "source": [ "# Import bit accurate ProcessModels.\n", "from lava.proc.dense.models import PyDenseModelBitAcc\n", "from lava.proc.lif.models import PyLifModelBitAcc\n", "\n", "# Configurations for execution.\n", "num_steps = 1000\n", "run_cond = RunSteps(num_steps=num_steps)\n", "\n", "# Define custom Run Config for execution of bit accurate models.\n", "class CustomRunConfigFixed(Loihi1SimCfg):\n", " def select(self, proc, proc_models):\n", " # Customize run config to always use float model for io.sink.RingBuffer.\n", " if isinstance(proc, io.sink.RingBuffer):\n", " return io.sink.PyReceiveModelFloat\n", " if isinstance(proc, LIF):\n", " return PyLifModelBitAcc\n", " elif isinstance(proc, Dense):\n", " return PyDenseModelBitAcc\n", " else:\n", " return super().select(proc, proc_models)\n", " \n", "rcfg = CustomRunConfigFixed(select_tag='lif_neurons', select_sub_proc_model=True)\n", "\n", "lif_network_critical_fixed = EINetwork(**lif_params_critical_fixed)\n", "outport_plug = io.sink.RingBuffer(shape=shape, buffer=num_steps)\n", "\n", "lif_network_critical_fixed.outport.connect(outport_plug.a_in)\n", "\n", "lif_network_critical_fixed.run(condition=run_cond, run_cfg=rcfg)\n", "\n", "# Fetching spiking activity.\n", "spks_critical_fixed = outport_plug.data.get()\n", "\n", "lif_network_critical_fixed.stop()" ] }, { "cell_type": "code", "execution_count": 37, "id": "a29e3abe", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = raster_plot(spks=spks_critical, color='orange', alpha=0.3)\n", "raster_plot(spks=spks_critical_fixed, fig=fig, alpha=0.3, color='b')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "836dc55a", "metadata": {}, "source": [ "Comparing the spike times after the parameter conversion, we find that after the first initial time steps, the spike times start diverging, even though certain structural similarities remain.
\n", "This, however, is expected: Since the systems is in a chaotic state, slight differences in the variables lead to a completely different output after some time steps. This is generally the behavior in spiking neural network.
\n", "**But** the network stays in a *very similar dynamical state* with *similar activity*, as can be seen when examining the overall behavior of the rate as well as auto-covariance function." ] }, { "cell_type": "code", "execution_count": 38, "id": "b5881949", "metadata": {}, "outputs": [], "source": [ "window = np.ones(window_size)\n", "binned_sps_critical_fixed = np.asarray([\n", " np.convolve(spks_critical_fixed[i], window) for i in range(dim)])[:,:-window_size +1]\n", "lags, ac_fct_lif_critical_fixed = auto_cov_fct(acts=binned_sps_critical_fixed.T)" ] }, { "cell_type": "code", "execution_count": 39, "id": "4fb468dd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "offset = 50\n", "timesteps = np.arange(0,1000, 1)[offset: -offset]\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,5))\n", "ax1.set_title('Mean rate of floating-point LIF network')\n", "ax1.plot(timesteps,\n", " (binned_sps_critical - np.mean(binned_sps_critical, axis=1)[:, np.newaxis]).T.mean(axis=1)[offset: -offset])\n", "ax1.set_ylabel('Mean rate')\n", "ax1.set_xlabel('Time Step')\n", "ax2.set_title('Mean rate of fixed-point LIF network')\n", "ax2.plot(timesteps,\n", " (binned_sps_critical_fixed\n", " - np.mean(binned_sps_critical_fixed, axis=1)[:, np.newaxis]).T.mean(axis=1)[offset: -offset])\n", "ax2.set_xlabel('Time Step')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "id": "f2b8de22", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plotting the auto-correlation function.\n", "plt.figure(figsize=(7,5))\n", "plt.xlabel('Lag')\n", "plt.ylabel('Covariance')\n", "plt.plot(lags, ac_fct_lif_critical_fixed, label='Bit accurate model')\n", "plt.plot(lags, ac_fct_lif_critical, label='Floating point model')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f142a6bd", "metadata": {}, "source": [ "## How to learn more?\n", "\n", "#### Follow the links below for deep-dive tutorials on the concepts in this tutorial:\n", "- [Processes](../in_depth/tutorial02_processes.ipynb \"Tutorial on Processes\")\n", "- [ProcessModel](../in_depth/tutorial03_process_models.ipynb \"Tutorial on ProcessModels\")\n", "- [Connections](../in_depth/tutorial05_connect_processes.ipynb \"Tutorial on Connecting Processe\")\n", "- [Execution](../in_depth/tutorial04_execution.ipynb \"Tutorial on Executing Processes\")\n", "- [SubProcessModels](../in_depth/tutorial06_hierarchical_processes.ipynb) or [Hierarchical Processes](../in_depth/tutorial06_hierarchical_processes.ipynb)\n", "\n", "If you want to find out more about Lava, have a look at the [Lava documentation](https://lava-nc.org/ \"Lava Documentation\") or dive into the [source code](https://github.com/lava-nc/lava/ \"Lava Source Code\").\n", "\n", "To receive regular updates on the latest developments and releases of the Lava Software Framework please subscribe to the [INRC newsletter](http://eepurl.com/hJCyhb \"INRC Newsletter\")." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }