Abstract
This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the MNIST dataset of handwritten digits. The research goal is to assess the classification power of a very simple biologically motivated mechanism. The network architecture is primarily a feedforward spiking neural network (SNN) composed of Izhikevich regular spiking (RS) neurons and conductance-based synapses. The weights are trained with the spike timing-dependent plasticity (STDP) learning rule. The proposed SNN architecture contains three neuron layers which are connected by both static and adaptive synapses. Visual input signals are processed by the first layer to generate input spike trains. The second and third layers contribute to spike train segmentation and STDP learning, respectively. The network is evaluated by classification accuracy on the handwritten digit images from the MNIST dataset. The simulation results show that although the proposed SNN is trained quickly without error-feedbacks in a few number of iterations, it results in desirable performance (97.6%) in the binary classification (0 and 1). In addition, the proposed SNN gives acceptable recognition accuracy in 10-digit (0-9) classification in comparison with statistical methods such as support vector machine (SVM) and multi-perceptron neural network.
Highlights
Neural networks that use biologically plausible neurons and learning mechanisms have become the focus of a number of recent pattern recognition studies [1, 2, 3]
Recent examples include the combination of rank order coding (ROC) and spike timing-dependent plasticity (STDP) learning [9], the calculation of temporal radial basis functions (RBFs) in the hidden layer of spiking neural network [10], and linear and non-linear pattern recognition by spiking neurons and firing rate distributions [11]
A subset of the MNIST machine learning data set consisting of handwritten digit images was used for evaluation of the proposed method [20]
Summary
Neural networks that use biologically plausible neurons and learning mechanisms have become the focus of a number of recent pattern recognition studies [1, 2, 3]. Recent examples include the combination of rank order coding (ROC) and spike timing-dependent plasticity (STDP) learning [9], the calculation of temporal radial basis functions (RBFs) in the hidden layer of spiking neural network [10], and linear and non-linear pattern recognition by spiking neurons and firing rate distributions [11]. The studies mentioned utilize spiking neurons, adaptive synapses, and biologically plausible learning for classification. Competitive learning takes the form of a winnertake-all (WTA) policy. This is a computational principle in neural networks which specifies the competition between the neurons in a layer for activation [13]. Masquelier and Thorpe (2007) developed a 5-layer spiking neural network (SNN) consisting of edge detectors, subsample mapping, intermediate-complexity visual feature extraction, object scaling and position adjustment, and categorization layers using STDP and WTA for image classification [15]
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