O'Reilly logo

Event-Based Neuromorphic Systems by Rodney Douglas, Adrian Whatley, Giacomo Indiveri, Tobi Delbruck, Shih-Chii Liu

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

7

Silicon Neurons

images

The event-based communication circuits of Chapter 2, the sensors described in Chapters 3 and 4, and the learning rules described in Chapter 6 all involved the use of spiking neurons. There are many different models of spiking neurons and many ways of implementing them using electronic circuits. In this chapter we present a representative subset of such neuromorphic circuits, showing implementations of both simple models and biologically faithful ones, following different circuit design approaches.

7.1 Introduction

Biological neurons are the primary components of networks in the brain. The neuronal membranes of these cells have active conductances which control the flow of ionic current between the various ionic reversal potentials and the membrane voltage on the membrane capacitance. These active conductances are usually sensitive to either the trans-membrane potential or the concentration of a specific ion. If these concentrations or voltages change by a large enough amount, a voltage pulse is generated at the axon hillock, a specialized region of the soma that connects to the axon. This pulse, called a ‘spike’ or ‘action potential,’ is propagated along the cell’s axon and activates synaptic connections with other neurons as it reaches the pre-synaptic terminals. Neuromorphic silicon neurons (SiNs) (Indiveri et al. 2011) are complementary metal oxide semiconductor ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required