Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

Saeed Afshar,André Van Schaik,Tara J Hamilton,Jonathan Tapson,Libin George

doi:10.3389/fnins.2014.00377

Saeed Afshar, André Van Schaik + Show 3 more

Open Access

https://doi.org/10.3389/fnins.2014.00377

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Abstract

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

Highlights

PRIOR WORK Real neurons, the electrically excitable cells of the Eumetazoan, constitute an extremely diverse intractably complex community whose dynamic structures and functions defy all but the broadest generalizations (Herz et al, 2006; Llinas, 2008)
The greater efficiency, higher performance, and biologically realistic dynamics of temporal coding neural networks has motivated the development of synaptic weight adaptation schemes that operate on temporally coding Spiking Neural Networks (SNN) (Jaeger, 2001; Maass et al, 2002; Izhikevich, 2006; Kasabov et al, 2013; Tapson et al, 2013; Gütig, 2014)
For these tests likely spatiotemporal spike patterns, each with one spike per channel per presentation were presented in random sequences to the Synapto-dendritic Kernel Adapting Neuron (SKAN) network

Summary

Introduction

PRIOR WORK Real neurons, the electrically excitable cells of the Eumetazoan, constitute an extremely diverse intractably complex community whose dynamic structures and functions defy all but the broadest generalizations (Herz et al, 2006; Llinas, 2008). In order to minimize this complexity, the field of Artificial Neural Networks (ANN) has traditionally modeled neurons as deterministic, centrally clocked elements which operate on real valued signals (Yegnanarayana, 1999). These signals represent neuronal rate coding where the spiking rate of a neuron encodes useful information and the adjustment of synaptic weights results in learning. This scheme, while mathematically amenable incurs a significant energy cost by discarding the rich temporal information available in the real signals used by neurons to communicate (Levy and Baxter, 1996; Laughlin, 2001; Van Rullen and Thorpe, 2001). The greater efficiency, higher performance, and biologically realistic dynamics of temporal coding neural networks has motivated the development of synaptic weight adaptation schemes that operate on temporally coding Spiking Neural Networks (SNN) (Jaeger, 2001; Maass et al, 2002; Izhikevich, 2006; Kasabov et al, 2013; Tapson et al, 2013; Gütig, 2014)

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