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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...
Synapses form the connections between biological neurons and so can form connections between the silicon neurons described in Chapter 7. They are fundamental elements for computation and information transfer in both real and artificial neural systems. While modeling, the nonlinear properties and the dynamics of real synapses can be extremely onerous for software simulations, neuromorphic very large...
This chapter goes into the details of some of the circuit blocks used in the silicon cochleas discussed in Chapter 4. It looks at some of the basic circuit structures used in the design of various one‐dimensional (1D) and two‐dimensional (2D) silicon cochleas. Nearly all silicon cochleas are built around second‐order low‐pass or band‐pass filters. These filters are usually described as second‐order...
A fundamental capability of any system, whether conventional or neuromorphic, is the ability to have long‐term memories whether for program control, parameter storage, or general configurability. This chapter provides an overview of a programmable analog technology based on floating‐gate circuits for reconfigurable platforms that can be used to implement such systems. It covers basic concepts of floating‐gate...
Neuromorphic chips often require a wide range of biasing currents which are independent of process and supply voltage, and which change with temperature appropriately to result in constant transconductance. These currents can span many decades, down to less than the transistor ‘off‐current’. This chapter describes how to design wide‐dynamic range configurable bias current references. The output of...
This chapter covers the on‐chip transistor circuits used for the communication fabric introduced in Chapter 2; in particular the asynchronous communication circuits for receiving and transmitting address events following the Address Event Representation (AER) protocol. AER circuits are needed for transmitters that emit address events, for example the sensors described in Chapters 3 and 4 and the multineuron...
To make use of custom address event (AE) based neuromorphic chips built using the circuits described in the previous chapters, they need to be embedded into a larger hardware infrastructure. This chapter describes some of the considerations which must be borne in mind when designing, building, and operating the printed circuit boards which form this infrastructure for relatively small‐scale, chiefly...
Software used in neuromorphic systems can be categorized according to the role(s) it performs. Each of these roles has particular features and presents particular challenges. Optimization, and Application Programming Interface (API) design are important, especially for software that is directly involved in processing streams of address‐events. Several example software systems are briefly presented.
This chapter describes event‐driven algorithmic processing of AE data streams on digital computers. Algorithms fall into categories such as noise‐reduction filters, event labelers, and trackers. The data structures and software architectures are also discussed as well as requirements for software and hardware infrastructure.1
This chapter describes four example neuromorphic systems: SpiNNaker, HiAER, Neurogrid and FACETS. These systems combine subsets of the principles outlined in previous chapters to build a large‐scale hardware platform for neuromorphic system engineering. Each of them represents a different point in the design space, and so it is instructive to examine the choices made in each system.
This chapter turns from the concrete instantiations of event‐based neuromophic systems discussed in previous chapters to look toward a possible future. It discusses physical computation in the context of today's technology in contrast with our understanding of cortical computation, and in particular, in relation to cognition. It then proposes approaches to understanding computation in brains and how...
This chapter focuses on the fundamentals of communication in event‐based neuromorphic electronic systems. Overall considerations on requirements for communication and circuit‐ versus packet‐switched systems are followed by an introduction to Address‐Event Representation (AER), asynchronous handshake protocols, address encoders, and address decoders. There follows a section on considerations regarding...
This chapter introduces biological and silicon retinas, focusing on recently developed silicon retina vision sensors with an asynchronous address‐event output. The first part of the chapter introduces biological retinas and four examples of Address‐Event Representation (AER) retinas. The second part of the chapter discusses the details of some of the pixel designs and the specifications of these sensors...
While the previous chapter was about neuromorphic silicon retinas, this one is on silicon cochleas. The cochlea is biology's sound sensor – it turns vibrations in the air into a neural signal. This chapter briefly explains the operation of the various components of the biological cochlea and introduces circuits that can simulate these components. Silicon cochlea designs typically divide the biological...
This chapter turns from the sensors discussed in Chapters 3 and 4 to the motor output side of neuromorphic systems. Understanding how to engineer biological motor systems has far reaching implications, particularly in the fields of medicine and robotics. However, there are many complexities when it comes to understanding biological design. This chapter describes ways in which scientists and engineers...
In this chapter, we address some general theoretical issues concerning synaptic plasticity as the mechanism underlying learning in neural networks, in the context of neuromorphic VLSI systems, and provide a few implementation examples to illustrate the principles. It ties to Chapters 3 and 4 on neuromorphic sensors by proposing theoretical means for utilizing events for learning. It is an interesting...
This book (Event‐Based Neuromorphic Systems) describes state‐of‐the‐art techniques for building neuromorphic electronic systems that sense, communicate, compute, and learn using asynchronous event‐based communication. The systems described in this book include sensors and neuronal processing circuits that implement models of the nervous systems. The origin of this field is described in this chapter.
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