Plasticity in CMOS Neuromorphic Circuits

A thesis exploring first-order in-silico modeling of changes (plasticity) in biological neurons is presented. In biological neural networks there is an intricate feedback relationship between neurons that results in synaptic and structural plasticity, including alterations in the connectivity and signaling between cells. Plasticity is widely believed to be key to memory, learning and intelligence. For engineers trying to implement biological neural networks in silicon, changes in neural functionality and rewiring could be essential mechanisms to examine. Modeling an electronic neural network that restructures itself could demonstrate learning and memory, and could lead to a better understanding of the way neural pathways implement learning and memory. This thesis looks at three aspects of neuromorphic design in parallel: functional complexity, scalability and plasticity.;We have modeled plastic excitatory and inhibitory synapses wherein changes in neurotransmitter and receptor concentrations can be emulated to a first-order. Results show that we can use these synapses in different combinations to demonstrate different kinds of behavior such as spike-timing-dependant plasticity and the effect of astrocytes (glial cells) on synaptic efficacy. We have also shown a circuit that emulates a spiking axon hillock. It can be tuned using an external voltage to produce different spiking patterns of variable duration. A design approach has been discussed towards building large scalable nueromorphic networks.;To demonstrate a plausible biological example of structural plasticity, a first-order neuromorphic analog circuit implementation of upper layers of the rodent barrel (somatosensory) cortex has been implemented. We have modeled spiking behavior of somatosensory cortical neurons in layers 2-4 and neuronal receptive fields while replicating biological observations on experience-dependent changes in receptive field organization, network topology and synaptic connectivity in silico. Anatomical and functional changes in synaptic connectivity have been modeled using analog switching, based on change in neural network activity. We demonstrate the effects of loss of inhibition and the loss of sensory (whisker) inputs with our circuits, and show that the self-organizing neuromorphic analog circuit in silico exhibits structural plasticity.;Future applications of this thesis would include intelligent prosthetic devices, autonomous robotic vehicles and systems that are capable of handling sensor damage.