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<!DOCTYPE html>
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<title>Dynamic Neural Fields — Lava documentation</title>
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<li class="toctree-l1"><a class="reference internal" href="lava_architecture_overview.html">Lava Architecture</a><ul>
<li class="toctree-l2"><a class="reference internal" href="lava_architecture_overview.html#key-attributes">Key attributes</a></li>
<li class="toctree-l2"><a class="reference internal" href="lava_architecture_overview.html#why-do-we-need-lava">Why do we need Lava?</a></li>
<li class="toctree-l2"><a class="reference internal" href="lava_architecture_overview.html#lava-s-foundational-concepts">Lava’s foundational concepts</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava_architecture_overview.html#processes">1. Processes</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava_architecture_overview.html#behavioral-implementations-via-processmodels">2. Behavioral implementations via ProcessModels</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava_architecture_overview.html#composability-and-connectivity">3. Composability and connectivity</a></li>
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<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html">Installing Lava</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#1.-System-Requirements">1. System Requirements</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#2.-Getting-Started">2. Getting Started</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#2.1-Cloning-Lava-and-Running-from-Source">2.1 Cloning Lava and Running from Source</a></li>
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<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#4.-Lava-Developer-Guide">4. Lava Developer Guide</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#5.-Tutorials">5. Tutorials</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial01_installing_lava.html#How-to-learn-more?">How to learn more?</a></li>
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<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html">Walk through Lava</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#1.-Usage-of-the-Process-Library">1. Usage of the Process Library</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Processes">Processes</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Ports-and-connections">Ports and connections</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Variables">Variables</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Record-internal-Vars-over-time">Record internal Vars over time</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Execution">Execution</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Retrieve-recorded-data">Retrieve recorded data</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Summary">Summary</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Learn-more-about">Learn more about</a></li>
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<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#2.-Create-a-custom-Process">2. Create a custom Process</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Create-a-new-ProcessModel">Create a new ProcessModel</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Use-the-custom-SpikeGenerator">Use the custom SpikeGenerator</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#Execute-and-plot">Execute and plot</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#id1">Summary</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#id2">Learn more about</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial00_tour_through_lava.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html">Processes</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#What-is-a-Process?">What is a <em>Process</em>?</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#How-to-build-a-Process?">How to build a <em>Process</em>?</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Overall-architecture">Overall architecture</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#AbstractProcess:-Defining-Vars,-Ports,-and-the-API"><em>AbstractProcess</em>: Defining <em>Vars</em>, <em>Ports</em>, and the API</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#ProcessModel:-Defining-the-behavior-of-a-Process"><em>ProcessModel</em>: Defining the behavior of a <em>Process</em></a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Instantiating-the-Process">Instantiating the <em>Process</em></a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Interacting-with-Processes">Interacting with <em>Processes</em></a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Accessing-Vars">Accessing <em>Vars</em></a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Using-custom-APIs">Using custom APIs</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Executing-a-Process">Executing a <em>Process</em></a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#Update-Vars">Update <em>Vars</em></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial02_processes.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html"><em>ProcessModels</em></a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Create-a-LIF-Process">Create a LIF <em>Process</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Create-a-Python-LeafProcessModel-that-implements-the-LIF-Process">Create a Python <em>LeafProcessModel</em> that implements the LIF <em>Process</em></a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Setup">Setup</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Defining-a-PyLifModel-for-LIF">Defining a <em>PyLifModel</em> for LIF</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Compile-and-run-PyLifModel">Compile and run <em>PyLifModel</em></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#Selecting-1-ProcessModel:-More-on-LeafProcessModel-attributes-and-relations">Selecting 1 <em>ProcessModel</em>: More on <em>LeafProcessModel</em> attributes and relations</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial03_process_models.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html">Execution</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Configuring-and-starting-execution">Configuring and starting execution</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Run-conditions">Run conditions</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Run-configurations">Run configurations</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Running-multiple-Processes">Running multiple <em>Processes</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Pausing,-resuming,-and-stopping-execution">Pausing, resuming, and stopping execution</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#Manual-compilation-and-execution">Manual compilation and execution</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial04_execution.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html">Connect processes</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Building-a-network-of-Processes">Building a network of <em>Processes</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Create-a-connection">Create a connection</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Possible-connections">Possible connections</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#There-are-some-things-to-consider-though:">There are some things to consider though:</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Connect-multiple-InPorts-from-a-single-OutPort">Connect multiple <em>InPorts</em> from a single <em>OutPort</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#Connecting-multiple-InPorts-to-a-single-OutPort">Connecting multiple <em>InPorts</em> to a single <em>OutPort</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial05_connect_processes.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html">Hierarchical <em>Processes</em> and <em>SubProcessModels</em></a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-LIF-and-Dense-Processes-and-ProcessModels">Create LIF and Dense <em>Processes</em> and <em>ProcessModels</em></a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-Dense-connection-Process">Create a Dense connection <em>Process</em></a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-Python-Dense-connection-ProcessModel-implementing-the-Loihi-Sync-Protocol-and-requiring-a-CPU-compute-resource">Create a Python Dense connection <em>ProcessModel</em> implementing the Loihi Sync Protocol and requiring a CPU compute resource</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-LIF-neuron-Process">Create a LIF neuron <em>Process</em></a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-Python-LIF-neuron-ProcessModel-implementing-the-Loihi-Sync-Protocol-and-requiring-a-CPU-compute-resource">Create a Python LIF neuron <em>ProcessModel</em> implementing the Loihi Sync Protocol and requiring a CPU compute resource</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-DenseLayer-Hierarchical-Process-that-encompasses-Dense-and-LIF-Process-behavior">Create a DenseLayer Hierarchical <em>Process</em> that encompasses Dense and LIF <em>Process</em> behavior</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Create-a-SubProcessModel-that-implements-the-DenseLayer-Process-using-Dense-and-LIF-child-Processes">Create a <em>SubProcessModel</em> that implements the DenseLayer <em>Process</em> using Dense and LIF child <em>Processes</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Run-the-DenseLayer-Process">Run the DenseLayer <em>Process</em></a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#Run-Connected-DenseLayer-Processes">Run Connected DenseLayer <em>Processes</em></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial06_hierarchical_processes.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html">Remote Memory Access</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Recommended-tutorials-before-starting:">Recommended tutorials before starting:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Create-a-minimal-Process-and-ProcessModel-with-a-RefPort">Create a minimal <em>Process</em> and <em>ProcessModel</em> with a <em>RefPort</em></a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Create-a-Python-Process-Model-implementing-the-Loihi-Sync-Protocol-and-requiring-a-CPU-compute-resource">Create a Python Process Model implementing the Loihi Sync Protocol and requiring a CPU compute resource</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Run-the-Processes">Run the <em>Processes</em></a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Implicit-and-explicit-VarPorts">Implicit and explicit VarPorts</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#Options-to-connect-RefPorts-and-VarPorts">Options to connect RefPorts and VarPorts</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial07_remote_memory_access.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html">MNIST Digit Classification with Lava</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#This-tutorial-assumes-that-you:">This tutorial assumes that you:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#This-tutorial-gives-a-bird’s-eye-view-of">This tutorial gives a bird’s-eye view of</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#Our-MNIST-Classifier">Our MNIST Classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#General-Imports">General Imports</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#Lava-Processes">Lava Processes</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#ProcessModels-for-Python-execution">ProcessModels for Python execution</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#Connecting-Processes">Connecting Processes</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#Execution-and-results">Execution and results</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial01_mnist_digit_classification.html#Follow-the-links-below-for-deep-dive-tutorials-on-the-concepts-in-this-tutorial:">Follow the links below for deep-dive tutorials on the concepts in this tutorial:</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html">Excitatory-Inhibitory Neural Network with Lava</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#This-tutorial-assumes-that-you:">This tutorial assumes that you:</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#This-tutorial-gives-a-high-level-view-of">This tutorial gives a high level view of</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#E/I-Network">E/I Network</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#General-imports">General imports</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#E/I-Network-Lava-Process">E/I Network Lava Process</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#ProcessModels-for-Python-execution">ProcessModels for Python execution</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Rate-neurons">Rate neurons</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Defining-the-parameters-for-the-network">Defining the parameters for the network</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Execution-and-Results">Execution and Results</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Visualizing-the-activity">Visualizing the activity</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Further-analysis">Further analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Controlling-the-network">Controlling the network</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#LIF-Neurons">LIF Neurons</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#id7">Execution and Results</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#id8">Visualizing the activity</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#id9">Controlling the network</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#DIfferent-recurrent-activation-regimes">DIfferent recurrent activation regimes</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Running-a-ProcessModel-bit-accurate-with-Loihi">Running a ProcessModel bit-accurate with Loihi</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Execution-of-bit-accurate-model">Execution of bit accurate model</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/end_to_end/tutorial02_excitatory_inhibitory_network.html#Follow-the-links-below-for-deep-dive-tutorials-on-the-concepts-in-this-tutorial:">Follow the links below for deep-dive tutorials on the concepts in this tutorial:</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html">Spike-timing Dependent Plasticity (STDP)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#This-tutorial-assumes-that-you:">This tutorial assumes that you:</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#STDP-from-Lavas-Process-Library">STDP from Lavas Process Library</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#The-plastic-connection-Process">The plastic connection Process</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#Plot-spike-trains">Plot spike trains</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#Plot-traces">Plot traces</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial08_stdp.html#Plot-STDP-learning-window-and-weight-changes">Plot STDP learning window and weight changes</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html">Custom Learning Rules</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#This-tutorial-assumes-that-you:">This tutorial assumes that you:</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#2.-Loihi’s-learning-engine">2. Loihi’s learning engine</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Epoch-based-updates">Epoch-based updates</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Synaptic-variables">Synaptic variables</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Learning-rules">Learning rules</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Dependencies">Dependencies</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Scaling-factors">Scaling factors</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Factors">Factors</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Traces">Traces</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Example:-Basic-pair-based-STDP">Example: Basic pair-based STDP</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Instantiating-LearningRule">Instantiating LearningRule</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#The-plastic-connection-Process">The plastic connection Process</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Plot-spike-trains">Plot spike trains</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Plot-traces">Plot traces</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Plot-STDP-learning-window-and-weight-changes">Plot STDP learning window and weight changes</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/tutorial09_custom_learning_rules.html#Follow-the-links-below-for-deep-dive-tutorials-on-the-concepts-in-this-tutorial:">Follow the links below for deep-dive tutorials on the concepts in this tutorial:</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html">Three Factor Learning with Lava</a><ul>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#This-tutorial-assumes-that-you:">This tutorial assumes that you:</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Defining-three-factor-learning-rule-interfaces-in-Lava">Defining three-factor learning rule interfaces in Lava</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Reward-modulated-Spike-Timing-Dependent-Plasticity-(R-STDP)-learning-rule">Reward-modulated Spike-Timing Dependent Plasticity (R-STDP) learning rule</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Defining-a-simple-learning-network-with-localized-reward-signals">Defining a simple learning network with localized reward signals</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Initialize-network-parameters-and-weights">Initialize network parameters and weights</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Generate-binary-input-and-graded-reward-spikes">Generate binary input and graded reward spikes</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Initialize-Network-Processes">Initialize Network Processes</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Connect-Network-Processes">Connect Network Processes</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Create-monitors-to-observe-the-weight-and-trace-dynamics-during-learning">Create monitors to observe the weight and trace dynamics during learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Run-the-network">Run the network</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Visualize-the-learning-results">Visualize the learning results</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Plot-eligibility-trace-dynamics">Plot eligibility trace dynamics</a></li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Plot-reward-trace-dynamics">Plot reward trace dynamics</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Advanced-Topic:-Implementing-custom-learning-rule-interfaces">Advanced Topic: Implementing custom learning rule interfaces</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#How-to-learn-more?">How to learn more?</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="lava/notebooks/in_depth/three_factor_learning/tutorial01_Reward_Modulated_STDP.html#Follow-the-links-below-for-deep-dive-tutorials-on-the-concepts-in-this-tutorial:">Follow the links below for deep-dive tutorials on the concepts in this tutorial:</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1 current"><a class="reference internal" href="algorithms.html">Algorithms and Application Libraries</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="dl.html">Deep Learning</a><ul>
<li class="toctree-l3"><a class="reference internal" href="dl.html#introduction">Introduction</a></li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#lava-dl-workflow">Lava-DL Workflow</a></li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#getting-started">Getting Started</a></li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#slayer-2-0">SLAYER 2.0</a><ul>
<li class="toctree-l4"><a class="reference internal" href="dl.html#example-code">Example Code</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#bootstrap">Bootstrap</a><ul>
<li class="toctree-l4"><a class="reference internal" href="dl.html#example-code-1">Example Code</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#network-exchange-netx-library">Network Exchange (NetX) Library</a><ul>
<li class="toctree-l4"><a class="reference internal" href="dl.html#example-code-2">Example Code</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="dl.html#detailed-description">Detailed Description</a><ul>
<li class="toctree-l4"><a class="reference internal" href="lava-lib-dl/slayer/slayer.html">Lava-DL SLAYER</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava-lib-dl/bootstrap/bootstrap.html">Lava-DL Bootstrap</a></li>
<li class="toctree-l4"><a class="reference internal" href="lava-lib-dl/netx/netx.html">Lava-DL NetX</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Dynamic Neural Fields</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#introduction">Introduction</a></li>
<li class="toctree-l3"><a class="reference internal" href="#what-is-lava-dnf">What is lava-dnf?</a></li>
<li class="toctree-l3"><a class="reference internal" href="#key-features">Key features</a></li>
<li class="toctree-l3"><a class="reference internal" href="#example">Example</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="optimization.html">Neuromorphic Constrained Optimization Library</a><ul>
<li class="toctree-l3"><a class="reference internal" href="optimization.html#about-the-project">About the Project</a><ul>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#taxonomy-of-optimization-problems">Taxonomy of Optimization Problems</a></li>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#optimizationsolver-and-optimizationproblem-classes">OptimizationSolver and OptimizationProblem Classes</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="optimization.html#tutorials">Tutorials</a><ul>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#quadratic-programming">Quadratic Programming</a></li>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#quadratic-uncosntrained-binary-optimization">Quadratic Uncosntrained Binary Optimization</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="optimization.html#examples">Examples</a><ul>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#solving-qp-problems">Solving QP problems</a></li>
<li class="toctree-l4"><a class="reference internal" href="optimization.html#solving-qubo">Solving QUBO</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="optimization.html#getting-started">Getting Started</a><ul>
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<section id="dynamic-neural-fields">
<h1>Dynamic Neural Fields<a class="headerlink" href="#dynamic-neural-fields" title="Permalink to this heading"></a></h1>
<section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h2>
<p>Dynamic Neural Fields (DNF) are neural attractor networks that generate
stabilized activity patterns in recurrently connected populations of neurons.
These activity patterns form the basis of neural representations, decision
making, working memory, and learning. DNFs are the fundamental
building block of <a class="reference external" href="https://dynamicfieldtheory.org">dynamic field theory</a>
a mathematical and conceptual framework for modeling cognitive processes in
a closed behavioral loop.</p>
<p><em>Voltage of a selective dynamic neural field tracking moving input</em></p>
<a class="reference external image-reference" href="https://user-images.githubusercontent.com/5708333/135443996-7492b968-277a-4397-9b1c-597b7af4a699.gif"><img alt="2D DNF tracking bias input" src="https://user-images.githubusercontent.com/5708333/135443996-7492b968-277a-4397-9b1c-597b7af4a699.gif" /></a>
<div class="line-block">
<div class="line"><br /></div>
</div>
</section>
<section id="what-is-lava-dnf">
<h2>What is lava-dnf?<a class="headerlink" href="#what-is-lava-dnf" title="Permalink to this heading"></a></h2>
<p>lava-dnf is a library within the Lava software framework. The main building
blocks in Lava are processes. lava-dnf provides
processes and other software infrastructure to build architectures composed of
DNFs. In particular, it provides functions that generate connectivity patterns
common to DNF architectures.</p>
<p>The primary focus of lava-dnf today is on robotic applications: sensing and
perception, motion control, behavioral organization, map formation, and
autonomous (continual) learning. Neuromorphic hardware provides significant
gains in both processing speed and energy efficiency compared to conventional
implementations of DNFs on a CPU or GPU (e.g., using
<a class="reference external" href="https://cedar.ini.rub.de">cedar</a> or <a class="reference external" href="https://github.com/cosivina">cosivina</a>).</p>
</section>
<section id="key-features">
<h2>Key features<a class="headerlink" href="#key-features" title="Permalink to this heading"></a></h2>
<p>Building DNF architectures</p>
<ol class="arabic simple">
<li><p>Based on spiking neurons</p></li>
<li><p>DNF dimensionality support for 0D, 1D, 2D, and 3D</p></li>
<li><p>Recurrent connectivity based on kernel functions</p></li>
<li><p>Forward connectivity to connect multiple DNFs</p></li>
<li><p>Structured input from spike generators</p></li>
</ol>
<p>Running DNF architectures</p>
<ol class="arabic simple">
<li><p>On CPU (Python simulation)</p></li>
<li><p>On Loihi 2</p></li>
</ol>
<p>Examples demonstrating basic DNF regimes and instabilities</p>
<ol class="arabic simple">
<li><p>Detection of input</p></li>
<li><p>Selection of input</p></li>
<li><p>Working memory of input</p></li>
<li><p>Relational networks</p></li>
</ol>
</section>
<section id="example">
<h2>Example<a class="headerlink" href="#example" title="Permalink to this heading"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lava.proc.lif.process</span> <span class="kn">import</span> <span class="n">LIF</span>
<span class="kn">from</span> <span class="nn">lava.lib.dnf.kernels.kernels</span> <span class="kn">import</span> <span class="n">SelectiveKernel</span>
<span class="kn">from</span> <span class="nn">lava.lib.dnf.connect.connect</span> <span class="kn">import</span> <span class="n">connect</span>
<span class="kn">from</span> <span class="nn">lava.lib.dnf.operations.operations</span> <span class="kn">import</span> <span class="n">Convolution</span>
<span class="c1"># Create a population of 20x20 spiking neurons.</span>
<span class="n">dnf</span> <span class="o">=</span> <span class="n">LIF</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
<span class="c1"># Create a selective kernel.</span>
<span class="n">kernel</span> <span class="o">=</span> <span class="n">SelectiveKernel</span><span class="p">(</span><span class="n">amp_exc</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">width_exc</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">global_inh</span><span class="o">=-</span><span class="mi">15</span><span class="p">)</span>
<span class="c1"># Apply the kernel to the population to create a DNF with a selective</span>
<span class="c1"># regime.</span>
<span class="n">connect</span><span class="p">(</span><span class="n">dnf</span><span class="o">.</span><span class="n">s_out</span><span class="p">,</span> <span class="n">dnf</span><span class="o">.</span><span class="n">a_in</span><span class="p">,</span> <span class="p">[</span><span class="n">Convolution</span><span class="p">(</span><span class="n">kernel</span><span class="p">)])</span>
</pre></div>
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