Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels

Abstract

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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. This use of time as a parameter is central and means that a SKAN network utilizes a minimal connectivity that scales linearly with the number of neurons. The robustness to noise, low connectivity requirements, 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).

Keywords

• STDP
• neuromorphic engineering
• unsupervised learning
• spiking neural networks
• dendritic computations
• stochastic computation