Recent development in analog computation: a brief overview

Xue, Yang

doi:10.1007/s10470-015-0668-y

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…We implemented the DFI algorithm using both the spike-mode and amplitude-mode neuron models, which were described by Eqs. (2) and 3, and numerical simulation results are presented in this section. In both cases, the DFI input images are described by signals v On and v O f f , which were estimated from an electrical model of the retina.…”

Section: Resultsmentioning

confidence: 99%

“…Studying cortical circuits is important to understand the brain, but it also leads to inspiration for the development of efficient signal processing systems [1], [2]. The computational burden associated with cortical circuit simulation using conventional hardware becomes prohibitive, as the simulations start to address realistic neural circuits [3].…”

Section: Introductionmentioning

confidence: 99%

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A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

Santos

Gomes

2019

RITA

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To run cortical circuit simulations in spike mode, i.e. taking into account the neural representation of information in terms of sequences of electrical pulses (also known as spikes), the use of customized hardware, which is specific for this purpose, is recommended. Simulations using more traditional hardware can be prohibitive. In this context, theoretical predictions are important for customized hardware design. For example, theoretical predictions lead to an adequate neuron model choice. To make such theoretical predictions, the cortical circuit simulations are carried out in amplitude mode. Differently from the spike mode, in amplitude mode information is represented by sequences of scalar values that describe neural input and output spike rates. In this paper, it was proposed amplitude and spike mode simulations of a cortical algorithm, namely the diffusive filling-in algorithm, to investigate whether predictions based on the amplitude-mode results approximate well the behavior of the customized hardware (spike mode results). The diffusive filling-in algorithm was chosen because it is simple enough for spike-mode simulation in a conventional computer, but the proposed amplitude-mode prediction method is the same for more complex algorithms or circuits. We provide a highly realistic comparison between amplitude-mode and spike-mode in the diffusive filling-in case, which suggests that the amplitude mode is reliable for theoretical predictions useful for customized hardware design for cortical circuit simulation. The goal of this paper is not to bring closure to these discussions but to suggest a way of avoiding possible issues that could compromise the success of the customized device design.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

Santos

Gomes

2019

RITA

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“…Xue [115] presented the recent developments in analog computation. These systems were explained from their basic elements, such as memory and arithmetic elements, to architecture and system design.…”

Section: Neuromorphic Chipsmentioning

confidence: 99%

Parallel Computing for Brain Simulation

Pastur-Romay

Porto-Pazos

Cedrón

2017

CTMC

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This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.

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“…1 References to analog representation occur in the philosophy of science (Papayannopoulos 2020, Sterrett 2017, Trenholme 1994, philosophy of art (Goodman 1968, Lopes 2018, Thomson-Jones 2015, epistemology (Dretske 1981), philosophy of perception (Beck 2019, Block forthcoming, Peacocke 1986, cognitive science (Dehaene 1997, Feigenson et al 2002, Shagrir 2010), computer science (Vergis et al 1986, Siegelmann & Fishman 1998, Xue 2016, and electrical engineering (Couch 2013). 2 The terms 'iconic', 'pictorial', and 'imagistic' are sometimes used instead of 'analog' and the terms 'digital' and 'discursive' are sometimes used instead of 'symbolic'.…”

Section: Introductionmentioning

confidence: 99%

The structure of analog representation

Lee

Myers

Rabin

2022

Nous

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This paper develops a theory of analog representation. We first argue that the mark of the analog is to be found in the nature of a representational system's interpretation function, rather than in its vehicles or contents alone. We then develop the rulebound structure theory of analog representation, according to which analog systems are those marked by the use of interpretive rules that map syntactic structural features onto semantic structural features. The theory involves three degree-theoretic measures that capture three independent ways in which a system can be more or less analog. We explain how our theory improves upon prior accounts of analog representation, provides plausible diagnoses for novel challenge cases, extends to hybrid systems that are partially analog and partially symbolic, and accounts for some of the advantages and disadvantages of representing analogically versus symbolically.

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Recent development in analog computation: a brief overview

Cited by 8 publications

References 52 publications

A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

Parallel Computing for Brain Simulation

The structure of analog representation

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