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Abstract
This paper presents a reconfigurable hardware neuro-simulator specifically designed to emulate biophysically accurate and biologically compatible neural networks. The platform is based on FPGA technology which is used to create real-time custom neuroprocessors with floating point accuracy and a novel hybrid time-event driven architecture for synaptic integration. Through a series of experiments the dynamics of the neuroprocessors are evaluated and compared with real neuron responses. The problem of interconnecting neurons with individual synapses is tackled with a novel synaptic architecture where all incoming synapses are merged efficiently in one single accumulative process without losing biological information. The case studies demonstrate the suitability of conductance-based models and FPGA platforms to simulate living organisms' behaviour in a biological compatible context.
Original language | English |
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Pages (from-to) | 693-703 |
Number of pages | 11 |
Journal | Microprocessors and Microsystems |
Volume | 39 |
Issue number | 8 |
Early online date | 13 Oct 2015 |
DOIs | |
Publication status | Published - 1 Nov 2015 |
Keywords
- Biological compatible neurons
- Biophysically accurate model
- FPGA neuro-simulator
- Hardware neuro modelling
- Pinsky-Rinzel model
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Dive into the research topics of 'Biologically compatible neural networks with reconfigurable hardware'. Together they form a unique fingerprint.Projects
- 1 Finished
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DYNAMICALLY RECONFIGURABLE HARDWARE ARCHITECTURES FOR CONTEXT-BASED STATISTICAL COMPRESSION OF VISUAL AND DATA CONTENT
Nunez-Yanez, J. L.
22/02/06 → 22/02/09
Project: Research