Training spiking neural networks to associate spatio-temporal input–output spike patterns

Abstract

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input-output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow-Hoff learning rule. In this article we present a mathematical formulation of the proposed learning rule. Furthermore, we extend the application of the algorithm to train a SNN consisting of multiple spiking neurons to perform spatiotemporal pattern classification and we show that the accuracy of classification is improved significantly over a single spiking neuron. We also investigate a number of possibilities to map the temporal output of the trained spiking neuron into a class label. Potential applications for motor control in neuro-rehabilitation and neuro-prosthetics are discussed as a future work.

Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks

“…The extension of signals in a multidimensional manner permits dealing with many spatiotemporal patterns in artificial and natural neural networks [4][5][6][7]. In the visual system in particular, directional receptive fields, as seen in mammalian simple cells, emerge by a minimum information criterion [8] and an independent component analysis [9] for natural and facial images, i.e., spatially independent basis functions are derived by self-organization.…”

Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

“…SNN have already proved that they are superior in learning and capturing spatiotemporal patterns from SSTD [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] (see also: http://ncs.ethz.ch/projects/evospike). SNN use temporal encoding of data as an internal mechanism to learn temporal relationships between input variables related to a spatio-temporal pattern that needs to be learned, classified and predicted.…”

“…-Simple eSNN [13,14,16]; -Dynamic eSNN (deSNN), as introduced in [15], where RO learning is used for initialisation of a synaptic weight based on the first incoming spike on this synapse, but than this weight is modified based on following spikes using spike time dependent plasticity (STDP) learning rule; -Spike pattern association neurons (SPAN) as classifiers, where as a reaction to a recognised input pattern, a precise time sequence of spikes is generated at the neuronal output [17,18]. The RO learning rule allows in principle for an eSNN to learn complex spatio-temporal patterns from data streams and then to recognise early an incoming pattern (therefore not necessarily 'waiting' for the whole pattern to be presented).…”

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

“…This spike timing-dependent plasticity (STDP) is known to be responsible for certain abilities observed across many animal species, including rapid response to threat stimuli and sound source localization [4]- [8]. Networks with STDP learning also have the ability to perform feature extraction and can learn to recognize and classify recurring temporal patterns and sequences [9]- [15]. Because these patterns may only occur in a subset of a given neuron's afferents (located at different points in space), it is referred to as spatio-temporal pattern recognition (STPR) [16]- [18].…”