scholarly journals Stability of Perceptual Learning and Excitatory-inhibitory Ratio in the Brain

2019 ◽  
Vol 26 (1-2) ◽  
pp. 3-9
Author(s):  
Kazuhisa Shibata
Keyword(s):  
Perceptual Learning ◽  
Inhibitory Ratio ◽  
The Brain

scholarly journals A theory of cortical responses

2005 ◽  
Vol 360 (1456) ◽  
pp. 815-836 ◽  
Author(s):  
Karl Friston
Keyword(s):  
Free Energy ◽  
Perceptual Learning ◽  
Hierarchical Models ◽  
Empirical Bayes ◽  
Sensory Input ◽  
Theoretical Treatment ◽  
Perceptual Inference ◽  
Repetition Suppression ◽  
Cortical Responses ◽  
The Brain

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

scholarly journals Reverse hierarchies and sensory learning

2008 ◽  
Vol 364 (1515) ◽  
pp. 285-299 ◽  
Author(s):  
Merav Ahissar ◽  
Mor Nahum ◽  
Israel Nelken ◽  
Shaul Hochstein
Keyword(s):  
General Population ◽  
Perceptual Learning ◽  
Special Populations ◽  
Hierarchy Theory ◽  
Training Techniques ◽  
Sensory Learning ◽  
Perceptual Representations ◽  
The Brain

Revealing the relationships between perceptual representations in the brain and mechanisms of adult perceptual learning is of great importance, potentially leading to significantly improved training techniques both for improving skills in the general population and for ameliorating deficits in special populations. In this review, we summarize the essentials of reverse hierarchy theory for perceptual learning in the visual and auditory modalities and describe the theory's implications for designing improved training procedures, for a variety of goals and populations.

Perceptual-learning machines and the brain

Stochastics ◽  
1975 ◽  
Vol 1 (1-4) ◽  
pp. 301-314 ◽  
Author(s):  
Stubbs D.F
Keyword(s):  
Perceptual Learning ◽  
Learning Machines ◽  
The Brain

Perceptual Systems, an Inexhaustible Reservoir of Information and the Importance of Art

2016 ◽  
Vol 4 (4) ◽  
pp. 265-279
Author(s):  
Sargy Mann
Keyword(s):  
Perceptual Learning ◽  
The Other ◽  
The Third ◽  
Figurative Art ◽  
Perceptual Systems ◽  
The Brain

There is my developing experience as a painter going blind which is unusual and interesting and as you know I am interested in that. But I am equally interested, possibly more interested in a conception of what figurative art can be as a way of mining new experience and in some sense or other recording it so it’s communicable. Now essentially all my drafts [of this paper] are trying to put those two together and it seems at first like a paradox, but it’s a paradox that I think I can perfectly resolve… and it’s what I want to do… the third element which is very hard to separate from the other two, is the perceptual learning applied to the perceptual systems, made possible through consciousness… That does require an analysis to do with things to do with the anatomy of the eye and the brain, which most people haven’t got a clue about but which is absolutely crucial.

Countercurrents in Matter and Mind

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 177-177
Author(s):  
S Hochstein ◽  
M Ahissar
Keyword(s):  
Perceptual Learning ◽  
Sensory Information ◽  
External World ◽  
Learning Effects ◽  
Efficient Manner ◽  
Cortical Hierarchy ◽  
Internal Information ◽  
Task Conditions ◽  
Perceptual Systems ◽  
The Brain

An especially efficient manner of transmission of matter or energy, employed by numerous biological systems, is the countercurrent mechanism. Transfer is effected between two closely aligned streaming currents where the currents flow in opposite directions. Final transfer can be 100% rather than the 50% ceiling of concurrent streams. We now report that perceptual systems may employ a similar mechanism. Information derived from the external world by the senses is transferred to the perceptual system in a hierarchy of processing areas. Simultaneously, this information is intermixed with previously stored internal information. The degree of mixture of previously existing information, with new, unprocessed information is titrated along the hierarchy. The brain may tap various points along the countercurrents to obtain the mixtures required for different tasks. Perceptual learning affects first the inner levels of this cortical hierarchy and only later descends to their input levels to achieve better performance with more difficult task conditions. Learning effects discussed at ECVP over the last two decades are reviewed in the light of this cortical scheme. Many seemingly contradictory findings are reconciled when put in the framework of countercurrent streams which respectively process sensory information and guide perceptual learning.

scholarly journals Lexical Information Guides Retuning of Neural Patterns in Perceptual Learning for Speech

2020 ◽  
Vol 32 (10) ◽  
pp. 2001-2012 ◽  
Author(s):  
Sahil Luthra ◽  
João M. Correia ◽  
Dave F. Kleinschmidt ◽  
Laura Mesite ◽  
Emily B. Myers
Keyword(s):  
Perceptual Learning ◽  
Pattern Analysis ◽  
Speech Sound ◽  
Neural Systems ◽  
Multivoxel Pattern Analysis ◽  
Lexical Information ◽  
Study Participants ◽  
Phonetic Categorization ◽  
The Brain ◽  
The Continuum

A listener's interpretation of a given speech sound can vary probabilistically from moment to moment. Previous experience (i.e., the contexts in which one has encountered an ambiguous sound) can further influence the interpretation of speech, a phenomenon known as perceptual learning for speech. This study used multivoxel pattern analysis to query how neural patterns reflect perceptual learning, leveraging archival fMRI data from a lexically guided perceptual learning study conducted by Myers and Mesite [Myers, E. B., & Mesite, L. M. Neural systems underlying perceptual adjustment to non-standard speech tokens. Journal of Memory and Language, 76, 80–93, 2014]. In that study, participants first heard ambiguous /s/–/∫/ blends in either /s/-biased lexical contexts ( epi_ ode) or /∫/-biased contexts ( refre_ing); subsequently, they performed a phonetic categorization task on tokens from an /asi/–/a∫i/ continuum. In the current work, a classifier was trained to distinguish between phonetic categorization trials in which participants heard unambiguous productions of /s/ and those in which they heard unambiguous productions of /∫/. The classifier was able to generalize this training to ambiguous tokens from the middle of the continuum on the basis of individual participants' trial-by-trial perception. We take these findings as evidence that perceptual learning for speech involves neural recalibration, such that the pattern of activation approximates the perceived category. Exploratory analyses showed that left parietal regions (supramarginal and angular gyri) and right temporal regions (superior, middle, and transverse temporal gyri) were most informative for categorization. Overall, our results inform an understanding of how moment-to-moment variability in speech perception is encoded in the brain.

scholarly journals Mecanismos glutamatérgicos de Aprendizagem Perceptual: um ensaio em Neuroepistemology

1969 ◽  
Vol 5 (1) ◽  
Author(s):  
Alfredo Pereira Junior
Keyword(s):  
Signaling Pathways ◽  
Perceptual Learning ◽  
Dendritic Spines ◽  
Building Blocks ◽  
Membrane Receptors ◽  
Molecular Computing ◽  
Computing Device ◽  
Thalamic Projections ◽  
Synaptic Structures ◽  
The Brain

Introducing the project of an area of study called Neuroepistemology, I argue that perceptual learning - the presentation of an attended stimulus eliciting the register of a corresponding informational pattern in the brain - is supported by glutamatergic synaptic and post-synaptic structures receiving afferent signals from thalamic projections. Glutamate membrane receptors (AMPA, NMDA and metabotropic) control signaling pathways, targeting a molecular computing device in dendritic spines that registers the relevant afferent patterns. From the study of these biological structures and functions, I criticize the neuroepistemological version of Transcendental Idealism proposed by Behrendt (2003), and suggest - following the classical Empiricist hypothesis - that the building-blocks of our mental universe are impressed in the brain following the presentation of attended stimuli.

scholarly journals Neural surprise in somatosensory Bayesian learning

2020 ◽  
Author(s):  
Sam Gijsen ◽  
Miro Grundei ◽  
Robert T. Lange ◽  
Dirk Ostwald ◽  
Felix Blankenburg
Keyword(s):  
Perceptual Learning ◽  
Bayesian Learning ◽  
Model Updating ◽  
Selection Procedure ◽  
Learning Model ◽  
Learning System ◽  
Somatosensory Processing ◽  
Eeg Data ◽  
Statistical Regularities ◽  
The Brain

AbstractTracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Primary somatosensory cortex is found to encode Bayesian surprise, reflecting model updating, from around 140ms. As such, this dissociation indicates that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its precise mechanisms.Author summaryOur environment features statistical regularities, such as a drop of rain predicting imminent rainfall. Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, particularly for somatosensation. As surprise signalling about novel observations indicates a mismatch between one’s beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning. By analyzing EEG data from human participants receiving sequences of tactile stimulation, we compare different formulations of surprise and investigate the employed underlying learning model. Our results indicate that the brain estimates transitions between observations. Furthermore, we identified different signatures of surprise computation and thereby provide a dissociation of the neural correlates of belief inadequacy and belief updating. Specifically, early surprise responses from around 70ms were found to signal the need for changes to the model, with encoding of its subsequent updating occurring from around 140ms. These results provide insights into how somatosensory surprise signals may contribute to the learning of environmental statistics.

scholarly journals Perceptual learning evidence for an interval- and modality-invariant representation of subsecond time

2019 ◽  
Author(s):  
Ying-Zi Xiong ◽  
Shu-Chen Guan ◽  
Cong Yu
Keyword(s):  
Perceptual Learning ◽  
Time Perception ◽  
Temporal Dynamics ◽  
Visual Modality ◽  
Central Theme ◽  
Invariant Representation ◽  
Timing Mechanisms ◽  
Modality Specificity ◽  
The Brain ◽  
Transfer Interval

AbstractA central theme in time perception research is whether subsecond timing relies on a dedicated centralized clock, or on distributed neural temporal dynamics. A fundamental constraint is the interval- and modality-specificity in perceptual learning of temporal interval discrimination (TID), which argues against a dedicated centralized clock, but is more consistent with multiple distributed mechanisms. Here we demonstrated an abstract, interval- and modality-invariant, representation of subsecond time in the brain. Participants practiced TID at a specific interval (100 ms), and received exposure to a transfer interval (200 ms), or to a different auditory/visual modality, through training of an orthogonal task. This double training enabled complete transfer of TID learning to the untrained interval, and mutual complete transfer between visual and auditory modalities. These results demonstrate an interval- and modality-invariant representation of subsecond time, which resembles a centralized clock, on top of the known distributed timing mechanisms and their readout and integration.

Neuronal Bases of Perceptual Learning Revealed by a Synaptic Balance Scheme

2004 ◽  
Vol 16 (3) ◽  
pp. 563-594 ◽  
Author(s):  
Osamu Hoshino
Keyword(s):  
Perceptual Learning ◽  
Synaptic Connection ◽  
Signal To Noise Ratio ◽  
Perceptual Task ◽  
Specific Cell ◽  
Sensory Stimuli ◽  
Sensory Feature ◽  
Sensory Features ◽  
Pulse Timing ◽  
The Brain

Our ability to perceive external sensory stimuli improves as we experience the same stimulus repeatedly. This perceptual enhancement, called perceptual learning, has been demonstrated for various sensory systems, such as vision, audition, and somatosensation. I investigated the contribution of lateral excitatory and inhibitory synaptic balance to perceptual learning. I constructed a simple associative neural network model in which sensory features were expressed by the activities of specific cell assemblies. Each neuron is sensitive to a specific sensory feature, and the neurons belonging to the same cell assembly are sensitive to the same feature. During perceptual learning processes, the network was presented repeatedly with a stimulus that was composed of a sensory feature and noise, and the lateral excitatory and inhibitory synaptic connection strengths between neurons were modified according to a pulse-timing-based Hebbian rule. Perceptual learning enhanced the cognitive performance of the network, increasing the signal-to-noise ratio of neuronal activity. I suggest here that the alteration of the synaptic balance may be essential for perceptual learning, especially when the brain tries to adopt the most suitable strategy—signal enhancement, noise reduction, or both—for a given perceptual task.

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