Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 USA.
IEEE Transactions on Neural Networks (Impact Factor: 2.95). 11/2010; 21(11):1697-709. DOI: 10.1109/TNN.2010.2050600
Source: PubMed


In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.

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    • "Dynamic behavior of neuron is widely studied to explore the chief role of neuronal spiking for effective neurotransmission and brain signal processing [1] [2] [3] [4] [5]. External electrical stimulation (EES), like deep brain stimulation, is a therapy for cognitive disorders such as Parkinson's disease, epilepsy and dystonia [6]. "
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    • "Other mathematical models of biologically inspired neurons are optimized for real time response of the neural network [7], [8] and [9] or for biological plausibility such as Izhikevich models [10] and [11]. A simple but biologically plausible rule for modeling the synaptic mechanisms of learning (SAPR) is presented in [12] and [13]. More complex models of neurons which mimic rigorously the synaptic chemical and physical processes were developed and simulated by Henry Markram et. "
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