Learning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity

Neuromorphic engineering;Analog computing;Spike timing dependent plasticity;Floating gate;Transistor;Adaptive circuits;Bi-directional tunneling

Brink, Stephen Isaac ; Hasler, Jennifer Biomedical Engineering Anderson, David Bhatti, Pamela DeWeerth, Steve Liu, Robert ; Hasler, Jennifer

University:Georgia Institute of Technology

Department:Biomedical Engineering

关键词: Neuromorphic engineering;    Analog computing;    Spike timing dependent plasticity;    Floating gate;    Transistor;    Adaptive circuits;    Bi-directional tunneling;   

Others  :  https://smartech.gatech.edu/bitstream/1853/50143/1/brink_stephen_i_201212_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.