Connectionists: Papers on networks of neurons with nonlinear dendrites and Binary Synapses trained by structural plasticity

[Arindam Basu]

Dear all

Let me draw your attention to some work done by our group over the past few years in developing networks of neurons with

lumped dendritic nonlinearity and binary synapses. The connections on each dendritic branch is sparse and the neurons learn

to classify patterns when trained using different structural plasticity rules. Thus learning happens by modiying network connection matrix and not by weight change. These networks have advantage for hardware implementations--reduced memory and resistance to mismatch due to binary synapses.

We have demonstrated supervised and unsupervised spike time based learning rules and have used these networks as readout of

liquid state machines, to improve the reservoir in a liquid state machine, to classify spike latency patterns like

tempotron or to classify MNIST images. I summarize the papers below and also include arxiv links for your reading pleasure.


1. S. Hussain, S. C. Liu and A. Basu, "Biologically plausible, Hardware-friendly Structural Learning for Spike-based

pattern classification using a simple model of Active Dendrites,"   Neural Computation , vol. 27, no. 4, pp. 845-897, April

2015
link: http://arxiv.org/pdf/1411.5881.pdf

** Margin based learning, spike time based supervised learning, results on UCI dataset

2. S. Roy, A. Banerjee and A. Basu, "Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic

VLSI Implementations,"   IEEE Trans. on Biomedical Circuits & Systems , vol. 8, no. 5, pp. 681-695, 2014.

link:http://arxiv.org/pdf/1411.5458.pdf

** Readout of LSM, 30X less binary synapses compared to parallel perceptron


3. S. Roy, P. P. San, S. Hussain, Lee Wang Wei and A. Basu, "Learning Spike Time Codes through Morphological Learning with

Binary Synapses,"  IEEE Trans. on Neural Networks & Learning Systems , vol. 27, no. 7, July 2016.

link: http://arxiv.org/pdf/1506.05212.pdf

** Classifying spike time latency patterns, comparison with tempotron


4. S. Hussain and A. Basu, "Multi-class Classification by Adaptive Network of Dendritic Neurons with Binary Synapses using

Structural Plasticity,"  Frontiers in Neuroscience , Mar, 2016. doi: 10.3389/fnins.2016.00113

link: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814530/

** Ensemble learning, adaptive allocation of dendrites, performance on MNIST, hardware savings analysis

5. S. Roy and A. Basu, "An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks,"  IEEE Trans.

on Neural Networks and Learning Systems , accepted, 2016.

link: http://arxiv.org/pdf/1512.01314.pdf

** unsupervised spike time based learning, sequence learning

6.S. Roy and A. Basu, "An online structural plasticity rule for generating better reservoirs,"  Neural Computation ,

accepted, 2016.

link: http://arxiv.org/pdf/1604.05459.pdf

** Improving separation property of reservoirs in LSM

Thanks

Arindam Basu
Assistant Professor
School of EEE
Nanyang Technological University
http://www3.ntu.edu.sg/home/arindam.basu/
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