Dataset Information

Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

ABSTRACT: The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionality of the brain, methodologies operating over both the space and time domains are thus essential. Implementing spatiotemporal functions within nanoscale devices capable of synaptic plasticity would contribute a significant step toward constructing a large-scale neuromorphic system that emulates the computing and energy performances of the human brain. We present a neuromorphic approach to brain-like spatiotemporal computing using resistive switching synapses. To process the spatiotemporal spike pattern, time-coded spikes are reshaped into exponentially decaying signals that are fed to a McCulloch-Pitts neuron. Recognition of spike sequences is demonstrated after supervised training of a multiple-neuron network with resistive switching synapses. Finally, we show that, due to the sensitivity to precise spike timing, the spatiotemporal neural network is able to mimic the sound azimuth detection of the human brain.

SUBMITTER: Wang W

PROVIDER: S-EPMC6135543 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

Wang Wei W Pedretti Giacomo G Milo Valerio V Carboni Roberto R Calderoni Alessandro A Ramaswamy Nirmal N Spinelli Alessandro S AS Ielmini Daniele D

Science advances 20180912 9

The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionality of the brain, methodologies operating over both the space and time domains are thus essential. Implementing spatiotemporal functions within nanoscale devices capable of synaptic plasticity would contribute a significant step toward constructing a l ...[more]

PMID: 30214936

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Project description:One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the "winner takes all" principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

Dataset Information

Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

Publications

Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

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