Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier One of the modern trends in the design of humanxe2x80x93machine 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 temporalrate 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 pairbased and tripletbased spike timingdependent 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 socalled forgetting function that is dependent on neuron activity. We show that coherent use of the tripletbased 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 xe2x80x9cwinner takes allxe2x80x9d 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 multilayer perceptron learned by back propagation of an error algorithm.

Information Transmitted From Bioinspired Neuronxe2x80x93Astrocyte Network Improves Cortical Spiking Networkxe2x80x99s Pattern Recognition Performance We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuronxe2x80x93astrocyte network (CNAN), using a spikebased unsupervised method, on the MNIST and alphadigit data sets and achieve an accuracy of 96.1% and 77.35%, respectively. We then connected CNAN to CSN by preserving maximum synchronization between them thanks to the concept of prolate spheroidal wave functions (PSWF). As a result, CSN receives additional information from CNAN without retraining. The important outcome is that CSN reaches 70.57% correct classification rate on capital letters without being trained on them. The overall contribution of transfer is 87.47%. We observed that for CSN the classifying neurons that relate to digits 0xe2x80x939 of the alphadigit data set are completely supported by the ones that relate to digits 0xe2x80x939 of the MNIST data set. This means that CSN recognizes the similarity between the digits of the MNIST and alphadigit data sets and classifies each digit of both data sets in the same class.