scholarly journals Sequence learning recodes cortical representations instead of strengthening initial ones

2018 ◽  
Author(s):  
Kristjan Kalm ◽  
Dennis Norris
Keyword(s):  
Neural Activity ◽  
Sequence Learning ◽  
Visual Processing ◽  
Computational Models ◽  
Activity Patterns ◽  
Recall Task ◽  
Neural Representation ◽  
Processing Stream ◽  
Initial Sequence ◽  
Cortical Representations

AbstractWe contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patters from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.

scholarly journals Sequence learning recodes cortical representations instead of strengthening initial ones

2021 ◽  
Vol 17 (5) ◽  
pp. e1008969
Author(s):  
Kristjan Kalm ◽  
Dennis Norris
Keyword(s):  
Neural Activity ◽  
Sequence Learning ◽  
Visual Processing ◽  
Computational Models ◽  
Activity Patterns ◽  
Recall Task ◽  
Neural Representation ◽  
Processing Stream ◽  
Initial Sequence ◽  
Cortical Representations

We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patters from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.

scholarly journals Defining the Units of Competition: Influences of Perceptual Organization on Competitive Interactions in Human Visual Cortex

2010 ◽  
Vol 22 (11) ◽  
pp. 2417-2426 ◽  
Author(s):  
Stephanie A. McMains ◽  
Sabine Kastner
Keyword(s):  
Visual Cortex ◽  
Perceptual Organization ◽  
Visual Processing ◽  
Perceptual Grouping ◽  
Neural Representation ◽  
Competitive Interactions ◽  
Illusory Figure ◽  
Processing Stream ◽  
Neural Processes ◽  
Early Visual Cortex

Multiple stimuli that are present simultaneously in the visual field compete for neural representation. At the same time, however, multiple stimuli in cluttered scenes also undergo perceptual organization according to certain rules originally defined by the Gestalt psychologists such as similarity or proximity, thereby segmenting scenes into candidate objects. How can these two seemingly orthogonal neural processes that occur early in the visual processing stream be reconciled? One possibility is that competition occurs among perceptual groups rather than at the level of elements within a group. We probed this idea using fMRI by assessing competitive interactions across visual cortex in displays containing varying degrees of perceptual organization or perceptual grouping (Grp). In strong Grp displays, elements were arranged such that either an illusory figure or a group of collinear elements were present, whereas in weak Grp displays the same elements were arranged randomly. Competitive interactions among stimuli were overcome throughout early visual cortex and V4, when elements were grouped regardless of Grp type. Our findings suggest that context-dependent grouping mechanisms and competitive interactions are linked to provide a bottom–up bias toward candidate objects in cluttered scenes.

scholarly journals Beyond cones: an improved model of whisker bending based on measured mechanics and tapering

2016 ◽  
Vol 116 (2) ◽  
pp. 812-824 ◽  
Author(s):  
Samuel Andrew Hires ◽  
Adam Schuyler ◽  
Jonathan Sy ◽  
Vincent Huang ◽  
Isis Wyche ◽  
...  
Keyword(s):  
Neural Activity ◽  
Computational Models ◽  
Sensory Input ◽  
Activity Patterns ◽  
Object Localization ◽  
Homogeneous Cone ◽  
Key Drivers ◽  
Improved Model ◽  
Sense Of Touch ◽  
Object Interactions

The sense of touch is represented by neural activity patterns evoked by mechanosensory input forces. The rodent whisker system is exceptional for studying the neurophysiology of touch in part because these forces can be precisely computed from video of whisker deformation. We evaluate the accuracy of a standard model of whisker bending, which assumes quasi-static dynamics and a linearly tapered conical profile, using controlled whisker deflections. We find significant discrepancies between model and experiment: real whiskers bend more than predicted upon contact at locations in the middle of the whisker and less at distal locations. Thus whiskers behave as if their stiffness near the base and near the tip is larger than expected for a homogeneous cone. We assess whether contact direction, friction, inhomogeneous elasticity, whisker orientation, or nonconical shape could explain these deviations. We show that a thin-middle taper of mouse whisker shape accounts for the majority of this behavior. This taper is conserved across rows and columns of the whisker array. The taper has a large effect on the touch-evoked forces and the ease with which whiskers slip past objects, which are key drivers of neural activity in tactile object localization and identification. This holds for orientations with intrinsic whisker curvature pointed toward, away from, or down from objects, validating two-dimensional models of simple whisker-object interactions. The precision of computational models relating sensory input forces to neural activity patterns can be quantitatively enhanced by taking thin-middle taper into account with a simple corrective function that we provide.

scholarly journals Human face-selective cortex does not distinguish between members of a racial outgroup

2019 ◽  
Author(s):  
Niv Reggev ◽  
Kirstan Brodie ◽  
Mina Cikara ◽  
Jason Mitchell
Keyword(s):  
Neural Activity ◽  
Visual Processing ◽  
Neural Representation ◽  
Human Face ◽  
Fusiform Face Area ◽  
Total N ◽  
Face Area ◽  
Repetition Suppression ◽  
Homogeneity Effect ◽  
Outgroup Homogeneity

People often fail to individuate members of social outgroups, a phenomenon known as the outgroup homogeneity effect. Here, we used fMRI repetition suppression to investigate the neural representation underlying this effect. In a pre-registered study, White human perceivers (N = 29) responded to pairs of faces depicting White or Black targets. In each pair, the second face depicted either the same target as the first face, a different target from the same race, or a scrambled face outline. We localized face-selective neural regions via an independent task, and demonstrated that neural activity in the fusiform face area distinguished different faces only when targets belonged to the perceivers’ racial ingroup (White). By contrast, face-selective cortex did not discriminate between other-race individuals. Moreover, across two studies (total N = 67) perceivers were slower to discriminate between different outgroup members and remembered them to a lesser extent. Together, these results suggest that the outgroup homogeneity effect arises when early-to-mid-level visual processing results in an erroneous overlap of representations of outgroup members.

scholarly journals Independent Neural Activity Patterns for Sensory- and Confidence-Based Information Maintenance during Category-Selective Visual Processing

eNeuro ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. ENEURO.0268-18.2018 ◽  
Author(s):  
Matthew D. Weaver ◽  
Johannes J. Fahrenfort ◽  
Artem Belopolsky ◽  
Simon van Gaal
Keyword(s):  
Neural Activity ◽  
Visual Processing ◽  
Activity Patterns

scholarly journals Brain neural activity patterns yielding numbers are operators, not representations

2009 ◽  
Vol 32 (3-4) ◽  
pp. 336-337
Author(s):  
Walter J. Freeman ◽  
Robert Kozma
Keyword(s):  
Prefrontal Cortex ◽  
Neural Activity ◽  
Computational Models ◽  
Dynamic Models ◽  
Activity Patterns ◽  
Cortical Activity ◽  
Cortical Surface ◽  
Symbolic Representations ◽  
Scalp Eeg

AbstractWe contrapose computational models using representations of numbers in parietal cortical activity patterns (abstract or not) with dynamic models, whereby prefrontal cortex (PFC) orchestrates neural operators. The neural operators under PFC control are activity patterns that mobilize synaptic matrices formed by learning into textured oscillations we observe through the electroencephalogram from the scalp (EEG) and the electrocorticogram from the cortical surface (ECoG). We postulate that specialized operators produce symbolic representations existing only outside of brains.

scholarly journals Neural differentiation of incorrectly predicted memories

2016 ◽  
Author(s):  
Ghootae Kim ◽  
Kenneth A. Norman ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Neural Differentiation ◽  
Computational Models ◽  
Activity Patterns ◽  
Neural Representation ◽  
Memory Representation ◽  
Neural Connections ◽  
Sequence Of Events ◽  
Network Simulations ◽  
The Brain ◽  
Target Memory

AbstractWhen an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here we explore what happens when such previously mispredicted items are later re-encountered. According to prior neural network simulations, this sequence of events - misprediction and subsequent restudy - should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context, and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item‘s neural representation away from the neural representation of the previous context. We tested this hypothesis using fMRI, by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared to items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Thus, the consequences of prediction error go beyond memory weakening: If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening.SignificanceCompetition between overlapping memories leads to weakening of non-target memories over time, making it easier to access target memories. However, a non-target memory in one context might become a target memory in another context. How do such memories get re-strengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.

scholarly journals Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md Moin Uddin Atique ◽  
Joseph Thachil Francis
Keyword(s):  
Neural Activity ◽  
Visual Information ◽  
Mirror Neurons ◽  
Grip Force ◽  
Neural Representation ◽  
Virtual Object ◽  
Time Lags ◽  
Subjective Value ◽  
Machine Interface ◽  
Definition Of

AbstractMirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

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