Silicon Models of the Auditory Pathway

Schaik, André van; Hamilton, Tara Julia; Jin, Craig

doi:10.1007/978-1-4419-5934-8_10

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…A lot of previous work has focused on front-end sensory and motor systems, including retina models (e.g., Mead, 1989; Boahen and Andreou, 1992; Delbruck and Mead, 1994; Delbruck, 1994; Marwick and Andreou, 2006; Lichtsteiner et al, 2008) cochlea models (e.g., Mead, 1989; van Schaik et al, 1996; Sarpeshkar et al, 1998, 2005a,b; Ravindran et al, 2005; Hamilton et al, 2008; Odame and Hasler, 2008; Rumberg et al, 2008; van Schaik et al, 2010; Rumberg and Graham, 2012) as well as others (e.g., LeMoncheck, 1992). Although these input representations are important for neural computation, and some have done some interesting engineering work based on these front-end systems (Riesenhuber and Poggio, 2000; Fu et al, 2008; Schaik et al, 2009; Chakrabartty and Liu, 2010; Liu and Delbruck, 2010; Farabet et al, 2011; Sejnowski and Delbruck, 2012), our focus will be on the computation using these front-end structures in the highly modular cortical structure (Eliasmith and Anderson, 2003).…”

Section: Large-scale Neuromorphic Systemsmentioning

confidence: 99%

Finding a roadmap to achieve large neuromorphic hardware systems

2013

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Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.

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Section: Large-scale Neuromorphic Systemsmentioning

confidence: 99%

Finding a roadmap to achieve large neuromorphic hardware systems

2013

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“…LIF coding is used for example in speech recognition in [1] and [45]. LIF coding is also used to transduce the inner hair cell signal into spikes in the silicon cochleae proposed in [20,21,39].…”

Section: Lif Codingmentioning

confidence: 99%

Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory

Ferdaoussi

Rouat

Plourde

2023

Neuromorph. Comput. Eng.

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Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency measures based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency measures are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. The results show that Leaky Integrate-and-Fire coding has the overall best performance in terms of coding, computational, and energy efficiency.

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“…In [34] and [35], LIF coding is used for speech recognition. LIF coding is also the standard way to transduce the inner hair cell signal into spikes in silicon cochleae [36], [37].…”

Section: Spike Encodingmentioning

confidence: 99%

Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory

Ferdaoussi¹,

Plourde²,

Rouat³

2022

Preprint

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The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage of processing. Many algorithms have been proposed to perform spike encoding of sound. However, a systematic approach to quantitatively evaluate their performance is currently lacking. We propose the use of an information-theoretic framework to solve this problem. Specifically, we evaluate the coding efficiency of four spike encoding algorithms on two coding tasks that consist of coding the fundamental characteristics of sound: frequency and amplitude. The algorithms investigated are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. Using the tools of information theory, we estimate the information that the spikes carry on relevant aspects of an input stimulus. We find disparities in the coding efficiencies of the algorithms, where Leaky Integrate-and-Fire coding performs best. The information-theoretic analysis of their performance on these coding tasks provides insight on the encoding of richer and more complex sound stimuli.

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Silicon Models of the Auditory Pathway

Cited by 7 publications

References 29 publications

Finding a roadmap to achieve large neuromorphic hardware systems

Finding a roadmap to achieve large neuromorphic hardware systems

Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory

Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory

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