Digital Multiplierless Implementation of Biological Adaptive-Exponential Neuron Model

作者：Gomar Shaghayegh; Ahmadi Arash^*

来源：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2014, 61(4): 1206-1219.

DOI：10.1109/TCSI.2013.2286030

• Adaptive exponential integrated and fire model (AdEx)
• field-programmable gate array (FPGA)
• neuromorphic
• piecewise linear (PL)
• piecewise linear exponential (PLE)

High-accuracy implementation of biological neural networks is a computationally expensive task, specially, for large-scale simulations of neuromorphic algorithms. This paper proposes a set of models for biological spiking neurons, which are efficiently implementable on digital platforms. Proposed models can reproduce different biological behaviors with a high precision. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting low-cost hardware implementation. Hardware synthesis and physical implementations on a field-programmable gate array show that the proposed models can produce biological behavior of different types of neurons with higher performance and considerably lower implementation costs compared with the original model.

• 出版日期2014-4