A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation

Amir Zjajjo, Jaco Hofmann, Gerrit Jan Christiaanse, Martijn van Eijk, Georgios Smaragdos, Christos Strydis, Carlo Galuzzi, Rene van Leuken, Alexander de Graaf

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.

Original languageEnglish
Pages (from-to)326-337
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume12
Issue number2
DOIs
Publication statusPublished - 2018

Keywords

  • Biophysically accurate neuron simulation
  • multi-chip data-flow architecture
  • neuron network

Fingerprint

Dive into the research topics of 'A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation'. Together they form a unique fingerprint.

Cite this