Efficiency and ambiguity in an adaptive neural code

By Adrienne L. Fairhall, G. Lewen, William Bialek, R. D. V. Steveninck. Nature, 2001

868 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/54959bbd25fe303a4cb9aa661c9e8463884a96c7

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Citations in the corpus

Visual adaptation: neural, psychological and computational aspects. (2007, background): “…elements of adaptation operate on diﬀerent time scales, reﬂecting a continuum from near instantaneous gain con-trol (Chander & Chichilnisky, 2001; Fairhall et al., 2001; Nagel & Doupe, 2006; Nowak et al., 2003) to slowly developing modulations in encoding properties (Baccus & Meister, 2002;…”
Spike-triggered neural characterization. (2006, background): “Also, neurons adapt to stimuli over multiple timescales (Brenner, Bialek & de Ruyter van Steveninck, 2000; Fairhall, Lewen, Bialek, & de Ruyter van Steveninck, 2001 ), and it would be interesting to extend current approaches to incorporate adaptation.”
Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions (2002, background): “…to be predicted from a combination of responses to simple stimuli; for example neurons have been shown to adapt to the distribution of sensory inputs, so that any characterization of these responses will depend on context (Smirnakis et al., 1996; Brenner et al., 2000a; Fairhall et al., 2001).”
Dopamine in motivational control: rewarding, aversive, and alerting (2010, background): “All of these phenomena form a remarkable parallel to similar effects seen in sensory and motor adaptation (Braun et al., 2010; Fairhall et al., 2001; Shadmehr et al., 2010), suggesting that they may reflect a general neural mechanism for predictive learning.”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “An efficient discrimination mechanism would restrict neuronal processing to the rewards that are currently available in a given environment, similar to sensory adaptation to probability distributions of ambient visual intensities (148, 235, 307).”
Bayesian surprise attracts human attention (2005, background): “Hence, sensory cortex may have evolved to adapt to, to predict, and to quiet down the expected statistical regularities of the world (Olshausen & Field, 1996; Muller, Metha, Krauskopf, & Lennie, 1999; Dragoi, Sharma, Miller, & Sur, 2002; David, Vinje, & Gallant, 2004), focusing instead on events tha”
Processing of low-probability sounds by cortical neurons (2003, background): “and maximizes information transmission by matching the coding strategy to stimulus statistic”
Multiple Time Scales of Adaptation in Auditory Cortex Neurons (2004, methodology): “This sensitivity can also be used for optimizing the coding of sounds by matching the neuronal firing to the stimulus statistics (Brenner et al., 2000; Fairhall et al., 2001) and for forming a sensory memory trace that may capture the complexity of past auditory stimulation (Näätänen et al., 2001).”
Space and time in visual context (2007, background): “Electrophysiological and perceptual data on contextual effects are conventionally linked by what we call a coding 'catastrophe' or decoding ambiguit”
Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning (2006, background): “Alternatively, a step change in a stimulus’ properties may produce changes in ﬁring rates of single cells that are not step-like or single exponential, but show adaptation with multiple timescales [33].”