An LGN Inspired Detect/Transmit Framework for High Fidelity Relay of Visual Information with Limited Bandwidth

Lesica, Nicholas A.; Stanley, Garrett B.

doi:10.1007/11565123_18

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In the context of adaptation, with an increase in contrast in a switching paradigm, the adapted linear filters for LGN neurons show faster temporal dynamics and a decrease in the gain of the input-output function relative to a low contrast condition (Figure 6B) (Lesica et al, 2007; Mante et al, 2005). However, it is difficult to estimate dynamic feature selectivity to robustly capture adaptive behaviors across stimulus conditions (Lesica and Stanley, 2005, 2006; Stanley, 2002) because the feature selectivity of a neuron (Fritz et al, 2003; Lesica et al, 2007; Sharpee et al, 2006; Ulanovsky et al, 2003) can change rapidly with only a short adapting stimulus. This suggests that the fundamental features of a stimulus that drive a neuron to fire are flexible on rapid timescales such that adaptation can reshape what information is transmitted through the sensory pathway, which has even more profound effects if there is differential adaptation across excitatory and inhibitory inputs.…”

Section: Differential Adaptation Of Excitatory and Inhibitory Neuronsmentioning

confidence: 99%

Rapid Sensory Adaptation Redux: A Circuit Perspective

Whitmire

Stanley

2016

Neuron

122

116

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Summary Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well-documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell-types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

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Section: Differential Adaptation Of Excitatory and Inhibitory Neuronsmentioning

confidence: 99%

Rapid Sensory Adaptation Redux: A Circuit Perspective

Whitmire

Stanley

2016

Neuron

122

116

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“…Visual information processing is the core function of the human brain, and the area of the cerebral cortex about 1/4 is involved in this work [7][8][9]. At present, the brain's processing of visual information follows 3 principles: one is distributed, that is, different functional brain areas perform their duties, such as object orientation, motion direction, relative depth, color and shape information, etc., which are handled by different brain regions [6].…”

Section: Introductionmentioning

confidence: 99%

Interdisciplinary Evolution of the Machine Brain

Wang

Cai

Deng

et al. 2021

Interdisciplinary Evolution of the Machine Brain

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This chapter finally presents a characterization of interdisciplinary evolution of the machine brain. Perspective schemes for rebuilding a real vision brain in the future are analyzed, along with the major principles to construct the machine brain, are presented, which include memory, thinking, imagination, feeling, speaking and other aspects associated with machine vision, machine touch and machine minds. This explicitly developed the theoretical framework of brain-inspired intelligence from the vision brain hypothesis, the vision-minds hypothesis and the skin brain hypothesis. Based on Chaps. 2-5, development of machine intelligence during the past decades have experienced three stages-machine computation, learning and understanding. Machine leaning includes data mining. Environmental sensing helps to acquire the data. Pattern analysis and scene understanding are significant parts of machine understanding. Scientists have taught machine how to collect and treat data and discover knowledge from the data. Evolution of machine brain will experience another two stages-machine meta-learning (learning to learn) and selfdirected development (improving the capability of machine brain utilizing the learned knowledge). There are still great challenges in realization of the dream.

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An LGN Inspired Detect/Transmit Framework for High Fidelity Relay of Visual Information with Limited Bandwidth

Cited by 2 publications

References 11 publications

Rapid Sensory Adaptation Redux: A Circuit Perspective

Rapid Sensory Adaptation Redux: A Circuit Perspective

Interdisciplinary Evolution of the Machine Brain

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