A model of computation and representation in the brain

Albus, James S.

doi:10.1016/j.ins.2009.12.031

Cited by 29 publications

(13 citation statements)

References 65 publications

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“…Prostheses electrically stimulate the different parts (retina, optic nerve or cortex) of the visual pathway and elicit the dots of light perception called phosphenes [9]. Although researchers have tried to build intelligent model to mimic the brain [2,1], how the implant recipient interprets the information formed by phosphenes is still a problem of visual comprehension. Due to the limitations of the existing techniques, such as electrode size, power consumption, heat dissipation, biocompatibility etc., it will be difficult to increase the pixel number of the actual visual prosthesis.…”

Section: Introductionmentioning

confidence: 99%

Image processing based recognition of images with a limited number of pixels using simulated prosthetic vision

Zhao

Lü

Tian

et al. 2010

Information Sciences

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Section: Introductionmentioning

confidence: 99%

Image processing based recognition of images with a limited number of pixels using simulated prosthetic vision

Zhao

Lü

Tian

et al. 2010

Information Sciences

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“…Examples include CLARION by Ron Sun [41], HTM by George & Hawkins [16], GNOSIS by John Taylor et al [135], ARTSCAN by Stephen Grossberg et al [136], Albus [42], and Tenenbaum et al [43].…”

Section: ) Behavior-based Methodsmentioning

confidence: 99%

“…Jordan & Bishop [38] used "neural networks" to explicitly name symbolic graphical models. The long shortterm memory (LSTM) by Hochreiter & Schmidhuber 1997 [39], CLARION by Sun et al [40], [41], the hierarchical temporal memory (HTM) by George & Hawkins [16], the symbolic network scheme proposed by Albus [42], the symbolic Bayesian networks reviewed by Tenenbaum et al [43] are some examples among many. Lee & Mumford [44] used a vague, but intuitive term "feature" to refer to each symbolic variable during their use of the Bayesian rule for modeling cortex computation.…”

Section: B Developmental Program: Task Nonspecificitymentioning

confidence: 99%

Symbolic Models and Emergent Models: A Review

Weng

2012

IEEE Trans. Auton. Mental Dev.

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Abstract-There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent models is abstraction, which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain's outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain.

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“…In particular, there is an active research community proposing mobile sensor decision making solutions for target search [37,5,12,4,16,27,30,20,13,21,7] and tracking [15,8,17] problems. These solutions intrinsically exploit the action-perception loop [28,10] to design autonomous cognitive agents based on recent computational and decision models of the human brain [1,24]. This paper focuses on decision making (e.g., selecting the actions according to the perception) for target search problems.…”

Section: Introductionmentioning

confidence: 99%