Getting Started with Lava

This guide to programming Lava will provide a growing collection of learning resources to help you become a Lava developer! It will cover all aspects of Lava enabling you to create, compile, execute and understand Lava Processes. Review the Lava Architecture section for an introduction to the fundamental architectural concepts of Lava.

• Installing Lava
  • 1. System Requirements
  • 2. Getting Started
    • 2.1 Cloning Lava and Running from Source
      • 2.1.1 [Linux/MacOS]
      • 2.1.2 [Windows]
    • 2.2 [Alternative] Installing Lava from Binaries
      • [Windows]
      • [MacOS/Linux]
  • 3. Running Lava on Intel Loihi
  • 4. Lava Developer Guide
  • 5. Tutorials
  • How to learn more?
• Walk through Lava
  • 1. Usage of the Process Library
    • Processes
    • Ports and connections
    • Variables
    • Record internal Vars over time
    • Execution
      • Run Conditions
      • RunConfigs
      • Execute
    • Retrieve recorded data
    • Summary
    • Learn more about
  • 2. Create a custom Process
    • Create a new ProcessModel
    • Use the custom SpikeGenerator
    • Execute and plot
    • Summary
    • Learn more about
    • How to learn more?
• Processes
  • Recommended tutorials before starting:
  • What is a Process?
  • How to build a Process?
    • Overall architecture
    • AbstractProcess: Defining Vars, Ports, and the API
    • ProcessModel: Defining the behavior of a Process
    • Instantiating the Process
  • Interacting with Processes
    • Accessing Vars
    • Using custom APIs
    • Executing a Process
    • Update Vars
  • How to learn more?
• ProcessModels
  • Recommended tutorials before starting:
  • Create a LIF Process
  • Create a Python LeafProcessModel that implements the LIF Process
    • Setup
    • Defining a PyLifModel for LIF
    • Compile and run PyLifModel
  • Selecting 1 ProcessModel: More on LeafProcessModel attributes and relations
  • How to learn more?
• Execution
  • Recommended tutorials before starting:
  • Configuring and starting execution
    • Run conditions
    • Run configurations
  • Running multiple Processes
  • Pausing, resuming, and stopping execution
  • Manual compilation and execution
  • How to learn more?
• Connect processes
  • Recommended tutorials before starting:
  • Building a network of Processes
  • Create a connection
  • Possible connections
    • There are some things to consider though:
  • Connect multiple InPorts from a single OutPort
  • Connecting multiple InPorts to a single OutPort
  • How to learn more?
• Hierarchical Processes and SubProcessModels
  • Recommended tutorials before starting:
  • Create LIF and Dense Processes and ProcessModels
    • Create a Dense connection Process
    • Create a Python Dense connection ProcessModel implementing the Loihi Sync Protocol and requiring a CPU compute resource
    • Create a LIF neuron Process
    • Create a Python LIF neuron ProcessModel implementing the Loihi Sync Protocol and requiring a CPU compute resource
  • Create a DenseLayer Hierarchical Process that encompasses Dense and LIF Process behavior
  • Create a SubProcessModel that implements the DenseLayer Process using Dense and LIF child Processes
  • Run the DenseLayer Process
    • Run Connected DenseLayer Processes
  • How to learn more?
• Remote Memory Access
  • Recommended tutorials before starting:
  • Create a minimal Process and ProcessModel with a RefPort
    • Create a Python Process Model implementing the Loihi Sync Protocol and requiring a CPU compute resource
  • Run the Processes
  • Implicit and explicit VarPorts
  • Options to connect RefPorts and VarPorts
  • How to learn more?
• MNIST Digit Classification with Lava
  • This tutorial assumes that you:
  • This tutorial gives a bird’s-eye view of
  • Our MNIST Classifier
  • General Imports
  • Lava Processes
  • ProcessModels for Python execution
  • Connecting Processes
  • Execution and results
    • How to learn more?
  • Follow the links below for deep-dive tutorials on the concepts in this tutorial:
• Excitatory-Inhibitory Neural Network with Lava
  • This tutorial assumes that you:
  • This tutorial gives a high level view of
  • E/I Network
  • General imports
  • E/I Network Lava Process
  • ProcessModels for Python execution
    • Rate neurons
  • Defining the parameters for the network
  • Execution and Results
  • Visualizing the activity
  • Further analysis
  • Controlling the network
    • LIF Neurons
  • Execution and Results
  • Visualizing the activity
  • Controlling the network
  • DIfferent recurrent activation regimes
    • Running a ProcessModel bit-accurate with Loihi
  • Execution of bit accurate model
  • Follow the links below for deep-dive tutorials on the concepts in this tutorial:
• Spike-timing Dependent Plasticity (STDP)
  • This tutorial assumes that you:
    • STDP from Lavas Process Library
      • Parameters
      • Define STDP learning rule
      • Create Network
  • The plastic connection Process
  • Plot spike trains
  • Plot traces
  • Plot STDP learning window and weight changes
• Custom Learning Rules
  • This tutorial assumes that you:
    • 2. Loihi’s learning engine
  • Epoch-based updates
  • Synaptic variables
  • Learning rules
  • Dependencies
  • Scaling factors
  • Factors
  • Traces
  • Example: Basic pair-based STDP
  • Instantiating LearningRule
  • The plastic connection Process
  • Plot spike trains
  • Plot traces
  • Plot STDP learning window and weight changes
  • Follow the links below for deep-dive tutorials on the concepts in this tutorial:
• Three Factor Learning with Lava
  • This tutorial assumes that you:
    • Defining three-factor learning rule interfaces in Lava
  • Reward-modulated Spike-Timing Dependent Plasticity (R-STDP) learning rule
    • Defining a simple learning network with localized reward signals
  • Initialize network parameters and weights
  • Generate binary input and graded reward spikes
  • Initialize Network Processes
    • Connect Network Processes
    • Create monitors to observe the weight and trace dynamics during learning
    • Run the network
    • Visualize the learning results
  • Plot eligibility trace dynamics
  • Plot reward trace dynamics
    • Advanced Topic: Implementing custom learning rule interfaces
    • How to learn more?
  • Follow the links below for deep-dive tutorials on the concepts in this tutorial: