Assessing Alpha Band Event-related Synchronisation/Desynchronisation Using a Bio-Inspired Computational Model

This paper describes a study of the effects of variation of synaptic connectivity in a thalamo-cortical circuitry using a neural mass model. The oscillatory behaviour of the model output is assessed within the alpha frequency band. The model presented here is a modification of an existing model involving the introduction of biologically plausible synaptic connectivities as well as synaptic structure. Our goal is to study altered event related desynchronisation/synchronisation (ERD/ERS) patterns within the alpha band in Alzheimers disease as observed in experimental studies. ERD is an amplitude attenuation of certain EEG rhythms when an event is initiated or while a certain event is taking place in the brain. ERS is an amplitude enhancement of a certain EEG rhythm when cortical areas are not specifically engaged in a given mode of activity at a certain instant of time. EEG desynchronisation normally blocks alpha rhythms in the EEG due to sensory processing or behaviour. The results show that a decrease in synaptic connectivity induces a time lag in both ERD and ERS peaks in the model output. Furthermore, a deficiency induced in the inhibitory cholinergic pathway results in a distinct effect on time to peak in the ERD/ERS response. These observations are consistent with experimental findings in AD. Variation of the level of interconnectivity has a pronounced effect on the ERS behaviour of the model while the excitatory connectivity in the retino-geniculate pathway during the resting state is more influential on the ERD behaviour

EXTRACTION OF SOUND LOCALIZATION CUE UTILIZING PITCH CUE FOR MODELLING AUDITORY SYSTEM

(Abstract to follow

A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN

SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure

This paper presents an enhanced rank - order based learning algorithm, called SpikeTemp, for Spiking Neural Networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, and an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and thus reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark datasets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms and the results demonstrate the ability of SpikeTemp to classify different datasets after just one presentation of the training samples with comparable classification performance