NDLI: Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

Content Provider	PubMed Central
Author	Narayan, Srinivasa Cho, Youngkwan
Copyright Year	2014
Abstract	A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.
Related Links	http://dx.doi.org/10.3389/fncom.2014.00159
Starting Page	159
File Format	PDF
ISSN	16625188
e-ISSN	16625188
Journal	Frontiers in Computational Neuroscience
Volume Number	8
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2014-12-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Cellular and Molecular Neuroscience Neuroscience (miscellaneous) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Cellular and Molecular Neuroscience

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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