Name
Abstract | ||
|---|---|---|
The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques, and even when this is possible it is generally time consuming, complex and expensive. Recent work has shown how a simplified behavioural approach to modelling neurons can allow "virtual" experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are numerous. The first is the difficulty of simulating realistic aggregates efficiently, the second is making sense of the results and finally, the models often take days to run therefore it would be advantageous to have a model which can be synthesized onto hardware. In this paper we present a synthesizable VHDL implementation of Neuron models that allow large aggregates to be simulated. The models are demonstrated using a post synthesis system level VHDL model of the C. Elegans locomotory system. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BMAS.2008.4751231 | BMAS |
Keywords | Field | DocType |
electric potential,oscillators,neurophysiology,vhdl,biology,invertebrates,hardware description languages,generation time | Cellular biophysics,Computer science,Theoretical computer science,Control engineering,Artificial intelligence,VHDL,Hardware description language,System level | Conference |
Citations | PageRank | References |
2 | 0.58 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julian A. Bailey | 1 | 2 | 0.58 |
| Peter R. Wilson | 2 | 54 | 27.86 |
| Andrew D. Brown | 3 | 216 | 43.94 |
| John E. Chad | 4 | 35 | 6.27 |