Abstract:
In this thesis, a novel technique to embed synaptic plasticity in neuromorphic hardware is
proposed named as Frequency Dependent Synaptic Plasticity. This technique provides an
alternate interpretation for plasticity which was conventionally modeled as weight value. In the
proposed model of neuromorphic hardware, plasticity is implemented in frequency domain by
considering a synaptic connection performing bandpass filtering operation. Currently most of the
neuromorphic hardware are based on time domain based plasticity techniques. The proposed
model attempts to contribute and suggest an out of the box solution for neurocomputational
applications. It has been established through this thesis that the proposed model is biologically
plausible in terms of implementation of different phenomena observed in a biological brain such
as rate-encoding by Class I type of neuron, decoding of rate-encoded information by selective
triggering at post synaptic side, resonance aware synaptic plasticity, population coding, role and
interpretation of tuning curve, and frequency based neuronal communication. The proposed
hardware operates in analog domain which is closer to the operational domain of brain as
compared to its digital counterpart. This thesis also proposes a novel architecture to embed
population coding on neuromorphic hardware.
Further, it is explained in this thesis that a synaptic junction based on proposed model has a non
linear transfer function which omits the requirement of hidden layer in classifying non-linear
problems. This will allow applications to use least possible resources, employing input and
output nodes only, as compared to a network based on linear weight values.
