Recent advances in neuromorphic systems design have made it possible to model spiking neurons or neural networks by means of complementary metal oxide semiconductor (CMOS) circuits operating in current mode, which can be fabricated using analog very-large-scale-integrated circuit (analog VLSI) technology. This enabling technology has spawned a variety of emerging biomedical applications including high-speed simulation of neuronal networks, real-time dynamic current-clamp for neuron-computer interface and brain-implantable neural prosthetic devices. A technological hurdle for the practical use of such analog VLSI neuromorphic systems to emulate the structure-function relationships in the brain is the current lack of an effective means for these silicon neural systems to learn and to adapt on similar CMOS simulation platforms. Learning and memory in the mammalian brain are widely assumed to result from synaptic modifications such as long-term potentiation (LTP) and depression (LTD) in excitatory (glutamatergic) or inhibitory (GABAergic) synapses. This exploratory/developmental (R21) project attempts to model these synaptic events using CMOS analog VLSI technology by reverse engineering the basic cellular processes underlying various forms of LTP and LTD in excitatory/inhibitory synapses and their interactions in dendritic networks. The resulting neuromorphic analog VLSI models of synaptic LTP and LTD will provide the basic system building blocks for continuing future development of large-scale neuronal network models that emulate learning and memory functions in health and in mental disease. ? ?
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