Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
This addresses the problem of designing efficient and biologically plausible neuron models for computational neuroscience and neuromorphic hardware, though it appears incremental as it builds on existing spiking neuron concepts.
The paper introduces the Synapto-dendritic Kernel Adapting Neuron (SKAN), a spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns, demonstrating its computational power with dynamic synapto-dendritic kernels and robustness to noise, and shows implementation in an FPGA for neuromorphic systems.
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).