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A Bayesian Model of Sensory Adaptation  [PDF]
Yoshiyuki Sato,Kazuyuki Aihara
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0019377
Abstract: Recent studies reported two opposite types of adaptation in temporal perception. Here, we propose a Bayesian model of sensory adaptation that exhibits both types of adaptation. We regard adaptation as the adaptive updating of estimations of time-evolving variables, which determine the mean value of the likelihood function and that of the prior distribution in a Bayesian model of temporal perception. On the basis of certain assumptions, we can analytically determine the mean behavior in our model and identify the parameters that determine the type of adaptation that actually occurs. The results of our model suggest that we can control the type of adaptation by controlling the statistical properties of the stimuli presented.
The interplay of plasticity and adaptation in neural circuits: a generative model  [PDF]
Alberto Bernacchia
Frontiers in Synaptic Neuroscience , 2014, DOI: 10.3389/fnsyn.2014.00026
Abstract: Multiple neural and synaptic phenomena take place in the brain. They operate over a broad range of timescales, and the consequences of their interplay are still unclear. In this work, I study a computational model of a recurrent neural network in which two dynamic processes take place: sensory adaptation and synaptic plasticity. Both phenomena are ubiquitous in the brain, but their dynamic interplay has not been investigated. I show that when both processes are included, the neural circuit is able to perform a specific computation: it becomes a generative model for certain distributions of input stimuli. The neural circuit is able to generate spontaneous patterns of activity that reproduce exactly the probability distribution of experienced stimuli. In particular, the landscape of the phase space includes a large number of stable states (attractors) that sample precisely this prior distribution. This work demonstrates that the interplay between distinct dynamical processes gives rise to useful computation, and proposes a framework in which neural circuit models for Bayesian inference may be developed in the future.
Anti-Hebbian Spike-Timing-Dependent Plasticity and Adaptive Sensory Processing  [PDF]
Patrick D. Roberts,Todd K. Leen
Frontiers in Computational Neuroscience , 2010, DOI: 10.3389/fncom.2010.00156
Abstract: Adaptive sensory processing influences the central nervous system’s interpretation of incoming sensory information. One of the functions of this adaptive sensory processing is to allow the nervous system to ignore predictable sensory information so that it may focus on important novel information needed to improve performance of specific tasks. The mechanism of spike-timing-dependent plasticity (STDP) has proven to be intriguing in this context because of its dual role in long-term memory and ongoing adaptation to maintain optimal tuning of neural responses. Some of the clearest links between STDP and adaptive sensory processing have come from in vitro, in vivo, and modeling studies of the electrosensory systems of weakly electric fish. Plasticity in these systems is anti-Hebbian, so that presynaptic inputs that repeatedly precede, and possibly could contribute to, a postsynaptic neuron’s firing are weakened. The learning dynamics of anti-Hebbian STDP learning rules are stable if the timing relations obey strict constraints. The stability of these learning rules leads to clear predictions of how functional consequences can arise from the detailed structure of the plasticity. Here we review the connection between theoretical predictions and functional consequences of anti-Hebbian STDP, focusing on adaptive processing in the electrosensory system of weakly electric fish. After introducing electrosensory adaptive processing and the dynamics of anti-Hebbian STDP learning rules, we address issues of predictive sensory cancelation and novelty detection, descending control of plasticity, synaptic scaling, and optimal sensory tuning. We conclude with examples in other systems where these principles may apply.
Thermodynamic Costs of Information Processing in Sensory Adaptation  [PDF]
Pablo Sartori ,Léo Granger,Chiu Fan Lee,Jordan M. Horowitz
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003974
Abstract: Biological sensory systems react to changes in their surroundings. They are characterized by fast response and slow adaptation to varying environmental cues. Insofar as sensory adaptive systems map environmental changes to changes of their internal degrees of freedom, they can be regarded as computational devices manipulating information. Landauer established that information is ultimately physical, and its manipulation subject to the entropic and energetic bounds of thermodynamics. Thus the fundamental costs of biological sensory adaptation can be elucidated by tracking how the information the system has about its environment is altered. These bounds are particularly relevant for small organisms, which unlike everyday computers, operate at very low energies. In this paper, we establish a general framework for the thermodynamics of information processing in sensing. With it, we quantify how during sensory adaptation information about the past is erased, while information about the present is gathered. This process produces entropy larger than the amount of old information erased and has an energetic cost bounded by the amount of new information written to memory. We apply these principles to the E. coli's chemotaxis pathway during binary ligand concentration changes. In this regime, we quantify the amount of information stored by each methyl group and show that receptors consume energy in the range of the information-theoretic minimum. Our work provides a basis for further inquiries into more complex phenomena, such as gradient sensing and frequency response.
Synchrony between orientation-selective neurons is modulated during adaptation-induced plasticity in cat visual cortex
Narcis Ghisovan, Abdellatif Nemri, Svetlana Shumikhina, Stephane Molotchnikoff
BMC Neuroscience , 2008, DOI: 10.1186/1471-2202-9-60
Abstract: Multi-unit activity from area 17 of anesthetized adult cats was recorded. Single cells were sorted out and (1) orientation tuning curves were measured before and following 12 min adaptation and 60 min after adaptation (2) pairwise synchrony was measured by an index that was normalized in relation to the cells' firing rate. We first observed that the prolonged presentation of a non-preferred stimulus produces attractive (58%) and repulsive (42%) shifts of cell's tuning curves. It follows that the adaptation-induced plasticity leads to changes in preferred orientation difference, i.e. increase or decrease in tuning properties between neurons. We report here that, after adaptation, the neuron pairs that shared closer tuning properties display a significant increase of synchronization. Recovery from adaptation was accompanied by a return to the initial synchrony level.We conclude that synchrony reflects the similarity in neurons' response properties, and varies accordingly when these properties change.From the primary visual cortex (area 17; V1), neurons acquire sensitivity and selectivity for orientation, motion direction and other visual features as emergent properties [1-3]. In the cat, more than 90% of V1 neurons are well tuned to stimulus orientation [4]. Such orientation selectivity is generally considered a fairly "hard-wired" property acquired before or at the time of eye opening and maintained by patterned visual experience [5]. However, it was reported in the adult cat that V1 neurons could temporarily shift their preferred orientation following prolonged exposure (adaptation) to a non-preferred orientation [6-8] – but see [9]. Plasticity in cat V1 was also reported for adaptation to spatial and temporal frequency [10-12] suggesting that it might be a general property at this level of sensory information processing. In mammalian cortex, tuning properties were also shown to change following adaptation to speed [13,14] and motion direction in MT [9] and V4 [15].
Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory  [PDF]
Luis-Miguel Chevin,Russell Lande,Georgina M. Mace
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.1000357
Abstract:
Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory  [PDF]
Luis-Miguel Chevin ,Russell Lande,Georgina M. Mace
PLOS Biology , 2010, DOI: 10.1371/journal.pbio.1000357
Abstract:
Perceptual Adaptation to the Correction of Natural Astigmatism  [PDF]
Maria Vinas, Lucie Sawides, Pablo de Gracia, Susana Marcos
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0046361
Abstract: Background The visual system adjusts to changes in the environment, as well as to changes within the observer, adapting continuously to maintain a match between visual coding and visual environment. We evaluated whether the perception of oriented blur is biased by the native astigmatism, and studied the time course of the after-effects following spectacle correction of astigmatism in habitually non-corrected astigmats. Methods and Findings We tested potential shifts of the perceptual judgments of blur orientation in 21 subjects. The psychophysical test consisted on a single interval orientation identification task in order to measure the perceived isotropic point (astigmatism level for which the image did not appear oriented to the subject) from images artificially blurred with constant blur strength (B = 1.5 D), while modifying the orientation of the blur according to the axis of natural astigmatism of the subjects. Measurements were performed after neutral (gray field) adaptation on naked eyes under full correction of low and high order aberrations. Longitudinal measurements (up to 6 months) were performed in three groups of subjects: non-astigmats and corrected and uncorrected astigmats. Uncorrected astigmats were provided with proper astigmatic correction immediately after the first session. Non-astigmats did not show significant bias in their perceived neutral point, while in astigmatic subjects the perceived neutral point was significantly biased, typically towards their axis of natural astigmatism. Previously uncorrected astigmats shifted significantly their perceived neutral point towards more isotropic images shortly (2 hours) after astigmatic correction wear, and, once stabilized, remained constant after 6 months. The shift of the perceived neutral point after correction of astigmatism was highly correlated with the amount of natural astigmatism. Conclusions Non-corrected astigmats appear to be naturally adapted to their astigmatism, and astigmatic correction significantly changes their perception of their neutral point, even after a brief period of adaptation.
Mirror Adaptation in Sensory-Motor Simultaneity  [PDF]
Masataka Watanabe, Shion Shinohara, Shinsuke Shimojo
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0028080
Abstract: Background When one watches a sports game, one may feel her/his own muscles moving in synchrony with the player's. Such parallels between observed actions of others and one's own has been well supported in the latest progress in neuroscience, and coined “mirror system.” It is likely that due to such phenomena, we are able to learn motor skills just by observing an expert's performance. Yet it is unknown whether such indirect learning occurs only at higher cognitive levels, or also at basic sensorimotor levels where sensorimotor delay is compensated and the timing of sensory feedback is constantly calibrated. Methodology/Principal Findings Here, we show that the subject's passive observation of an actor manipulating a computer mouse with delayed auditory feedback led to shifts in subjective simultaneity of self mouse manipulation and auditory stimulus in the observing subjects. Likewise, self adaptation to the delayed feedback modulated the simultaneity judgment of the other subjects manipulating a mouse and an auditory stimulus. Meanwhile, subjective simultaneity of a simple visual disc and the auditory stimulus (flash test) was not affected by observation of an actor nor self-adaptation. Conclusions/Significance The lack of shift in the flash test for both conditions indicates that the recalibration transfer is specific to the action domain, and is not due to a general sensory adaptation. This points to the involvement of a system for the temporal monitoring of actions, one that processes both one's own actions and those of others.
Learning from Sensory and Reward Prediction Errors during Motor Adaptation  [PDF]
Jun Izawa ,Reza Shadmehr
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1002012
Abstract: Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.
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