Biologically compatible neural networks with reconfigurable hardware

Juan C Moctezuma Eugenio, Joe P McGeehan, Jose L Nunez-Yanez*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)
448 Downloads (Pure)


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 languageEnglish
Pages (from-to)693-703
Number of pages11
JournalMicroprocessors and Microsystems
Issue number8
Early online date13 Oct 2015
Publication statusPublished - 1 Nov 2015


  • Biological compatible neurons
  • Biophysically accurate model
  • FPGA neuro-simulator
  • Hardware neuro modelling
  • Pinsky-Rinzel model


Dive into the research topics of 'Biologically compatible neural networks with reconfigurable hardware'. Together they form a unique fingerprint.

Cite this