This work lays out the development of a reconfigurable electronic system, which is composed of biologically relevant circuits. This system has been termed a Field-Programmable Neuron Array (FPNA) and is analogous to the more familiar Field-Programmable Gate Array (FPGA) and Field-Programmable Analog Array (FPAA). At the core of the system is an array of output somas based on previously developed bio-physically based channel models. Linking them together is a complex 2D dendrite matrix, FPAA-like floating-gate routing, and associated support circuitry.
Several levels of generality give this system unprecedented re-configurability. The dendrite matrix can be arbitrarily configured so that many different topologies of dendrites can be investigated. Different soma circuits can be connected / disconnected to / from the dendrite matrix. Outputs from the somas can be arbitrarily routed to input synapses that exist at each dendrite node as well as the soma nodes. Lastly, the dynamics of each node consist of a mixture of individually tunable parts and global biases. All of this can be configured in concert to investigate neural circuits that exist in biological systems.
This chip will have a significant impact on research in many fields including neuroscience, neuromorphic engineering, and robotics. This chip will allow for rapid prototyping of spinal circuits. Since the fundamental circuits of the system are chosen to be biologically relevant, outputs from the various nodes should also be relevant, thus yielding itself to use by neuroscientists. This system also provides a tool by where biological systems can be emulated in real-world electronic systems. Solutions to many problems faced by roboticists (such as bi-pedal standing / walking / running / jumping / climbing and the transitions between states) are present in biology. By providing a chip that can duplicate the same neural circuits that are responsible for these processes in the biology, the hypothesis is that researchers can begin to solve some of the same types of problems in artificial systems.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/14003 |
| Date | 28 November 2005 |
| Creators | Farquhar, Ethan David |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 7848023 bytes, application/pdf |
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