Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systems
In this chapter 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 nano technology devices to the biological synaptic
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Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systems Teresa Serrano-Gotarredona and Bernabé Linares-Barranco
12.1 Introduction Neuromorphic engineering “http://en.wikipedia.org/wiki/Neuromorphic” is an interdisciplinary discipline that takes inspiration from biology, physics, mathematics, computer science, and engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, the physical architecture and design principles of which are based on those of biological nervous systems. The term neuromorphic was coined by Carver Mead, in the late 1980s [2] to describe very large-scale integration (VLSI) systems containing electronic analog circuits that mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe both analog, digital or mixed-mode analog/digital VLSI systems that implement models of neural systems (for perception, motor control, or sensory processing) and also software algorithms. A key aspect of neuromorphic engineering is understanding how the morphology and signal processing of neurons, circuits, and overall architectures creates desirable computations, influences robustness to damage, incorporates learning and development, and facilitates evolutionary change. It is obvious that interdisciplinary research broadens our view of particular problems yielding fresh and possibly unexpected insights. This is the case of
This chapter is a reproduction (with minor changes and with permission from IEEE) of publication [1] with IEEE copyright. T. Serrano-Gotarredona • B. Linares-Barranco () Instituto de Microelectrónica de Sevilla IMSE-CNM (CSIC and Universidad de Sevilla) e-mail: [email protected] R. Tetzlaff (ed.), Memristors and Memristive Systems, DOI 10.1007/978-1-4614-9068-5__12, © Springer Science+Business Media New York 2014
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T. Serrano-Gotarredona and B. Linares-Barranco
neuromorphic engineering, where technology and neuroscience cross-fertilize each other. One example of this is the recent impact of fabricated memristor devices [3–6], postulated since 1971 [7–9], thanks to research in nanotechnology electronics. Another is the mechanism known as Spike-Time-Dependent-Plasticity (STDP) [10–24] which describes a neuronal synaptic learning mechanism that refines the traditional Hebbian synaptic plasticity proposed in 1949 [25]. These are very different subjects from relatively unrelated disciplines (nanotechnology, biology, and computer science), which have nevertheless been drawn together by researchers in neuromorphic engineering [26–30]. STDP was originally postulated as a family of computer learning algorithms [10–12] and is being used by the machine intelligence and computational neuroscience community [15–24]. At the same time its biological and physiological foundations have been reasonably well established during the past decade [31–38]. If memristance and STDP can be related, then (a) recent discoveries
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