A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons.

ABSTRACT: Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed t

SUBMITTER: Akbarzadeh-Sherbaf K 

PROVIDER: S-EPMC6190648 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature