On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.
On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.
复制标题
DOI：
10.3389/fnins.2011.00026
复制
发表时间：
2011
影响因子：
4.3
通讯作者：
Linares-Barranco B
中科院分区：
文献类型：
--
作者：
Zamarreño-Ramos C;Camuñas-Mesa LA;Pérez-Carrasco JA;Masquelier T;Serrano-Gotarredona T;Linares-Barranco B
在本文中，我们介绍了神经形态工程师所发现的新兴纳米技术与神经科学之间非常令人兴奋的交叉领域。具体而言，我们将一种忆阻器纳米技术器件与在真实生物突触中发现的被称为脉冲时间依赖可塑性（STDP）的生物突触更新规则联系起来。理解这种联系使神经形态工程师能够开发电路架构，利用这种忆阻器人工模拟视觉皮层的部分功能。我们专注于被称为电压或通量驱动的忆阻器类型，并将讨论重点放在此类器件的行为宏观模型上。这些实现产生了完全异步的架构，其中神经元不仅向前而且向后发送其动作电位。一个关键方面是使用产生特定形状脉冲的神经元。我们将看到通过改变神经元动作电位脉冲的形状，如何能够调整和操纵兴奋性和抑制性突触的STDP学习规则。我们将看到神经元和忆阻器如何相互连接以实现大规模的脉冲学习系统，该系统遵循一种乘法STDP学习规则。我们将简要扩展架构以使用具有类似忆阻行为的三端晶体管。我们将说明V1视觉皮层层如何组装以及它如何能够学习从观察现实生活场景的真实人工CMOS脉冲视网膜传来的视觉数据中提取方向。最后，我们将讨论当前可用忆阻器的局限性。所呈现的结果基于行为模拟，未考虑器件和互连的非理想性。本文的目的是以教程的方式呈现一个初步框架，用于可能开发利用二端或三端忆阻型器件的完全异步STDP学习神经形态架构。用于模拟的所有文件可通过期刊网站获取。
In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site1.
登录
查看更多内容
DOI：
10.1109/82.842110
发表时间：
2000-05-01
影响因子：
4.4
作者：
Boahen, KA
通讯作者：
Boahen, KA
影响因子：
--
作者：
Antonio Perez-Carrasco, Jose;Acha, Begona;Linares-Barranco, Bernabe
通讯作者：
Linares-Barranco, Bernabe
DOI：
10.1109/tcsi.2006.887979
发表时间：
2007-01-01
影响因子：
5.1
作者：
Chan, Vincent;Liu, Shih-Chii;van Schaik, Andre
通讯作者：
van Schaik, Andre
影响因子：
0.8
作者：
Afifi, Ahmad;Ayatollahi, Ahmad;Raissi, Farshid
通讯作者：
Raissi, Farshid
影响因子：
5.3
作者：
Bi, GQ;Poo, MM
通讯作者：
Poo, MM