spikelearn 0.1.2

Spiking neural networks for AI workflows and neuromorphic computing

Spikelearn

Implementation of spiking neural networks capable of online learning tailored for machine learning workflows and neuromorphic computing applications.

Motivation

We needed a SNN model with the following requirements:

• Capable of handling traditional ML workflows
• Heterogeneous, with the ability to integrate both mathematical models and neurons or synapses inspired on neuromorphic computing and emergent devices
• That could be easily parametrizable, in order to explore a large number of configurations in high performance computing environments.
• That could reproduce models in existing neuromorphic chips such as Loihi.
• That could handle neuromodulators and other neuroscience-inspired goodies.
• That could be easily extensible.
• That is capable of online learning through a variety of synaptic plasticity rules.

Spikelearn intends to fill that role.

Status

Spikelearn is still in development. Please check spikelearn's documentation in readthedocs.

Quick install

Through pypi:

pip install spikelearn

Usage

from spikelearn import SpikingNet, SpikingLayer, StaticSynapse
import numpy as np

snn = SpikingNet()
sl = SpikingLayer(10, 4)
syn = StaticSynapse(10, 10, np.random.random((10,10)))

snn.add_input("input1")
snn.add_layer(sl, "l1")
snn.add_synapse("l1", syn, "input1")
snn.add_output("l1")

u = 2*np.random.random(10)
for i in range(10):
    s = snn(2*np.random.random(10))
    print(s)

Acknowledgements

• Threadwork, U.S. Department of Energy Office of Science, Microelectronics Program.

Copyright and license

Copyright © 2022, UChicago Argonne, LLC

Spikelearn is distributed under the terms of BSD License. See LICENSE

Argonne Patent & Intellectual Property File Number: SF-22-154