Roshan Gopalakrishnan

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This brief describes the neuromorphic very large scale integration implementation of a synapse utilizing a single floating-gate (FG) transistor that can be used to store a weight in a nonvolatile manner and demonstrate biological learning rules such as spike-timing-dependent plasticity (STDP). The experimental STDP plot (change in weight against ∆t=tpost -(More)
— Synapse plays an important role of learning in a neural network; the learning rules which modify the synap-tic strength based on the timing difference between the pre-and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). The most commonly used rule posits weight change based on time difference between one pre and one post(More)
— Synapse plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre-and postsynaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). The most commonly used rule posits weight change based on time difference between one presynaptic spike(More)
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