Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
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DOI：
10.1109/jproc.2014.2313954
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发表时间：
2014-09-01
影响因子：
20.6
通讯作者：
Indiveri, Giacomo
中科院分区：
文献类型：
--
作者：
Chicca, Elisabetta;Stefanini, Fabio;Indiveri, Giacomo
最近提出了几种模拟和数字脑启发电子系统，作为快速模拟脉冲神经网络的专用解决方案。虽然这些架构对于探索大规模神经系统模型的计算特性很有用，但构建低功耗紧凑的物理人工制品，使其在现实世界中表现出智能并表现出认知能力的挑战仍然是开放的。在本文中，我们提出了一套神经形态工程解决方案来应对这一挑战。特别地，我们回顾了实时模拟神经和突触动力学的神经形态电路，并讨论了生物物理逼真的时间动力学在硬件神经处理架构中的作用；我们回顾了在真实物理系统中实现基于峰值的可塑性机制的挑战，并提出了实现它们的模拟电子电路的例子；我们描述了递归神经网络的计算特性，并展示了神经形态赢家通吃电路如何实现工作记忆和决策机制。我们用从我们自己的电路和系统中获得的实验结果验证了提出的神经形态方法，并论证了这项工作中提出的电路和网络如何代表一组有用的组件，以有效和优雅地实现神经形态认知。
Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.