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 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 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 Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

scholarly journals Multiple states in ongoing neural activity in the rat visual cortex

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256791
Author(s):  
Daichi Konno ◽  
Shinji Nishimoto ◽  
Takafumi Suzuki ◽  
Yuji Ikegaya ◽  
Nobuyoshi Matsumoto
Keyword(s):  
Visual Cortex ◽  
Neural Activity ◽  
Activity Patterns ◽  
Electrode Array ◽  
Nonlinear Dimensionality Reduction ◽  
Neural Responses ◽  
Spontaneous Neural Activity ◽  
Custom Made ◽  
Visual Cortices ◽  
Freely Behaving

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

scholarly journals Spatiotemporal correlation of spinal network dynamics underlying spasms in chronic spinalized mice

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Carmelo Bellardita ◽  
Vittorio Caggiano ◽  
Roberto Leiras ◽  
Vanessa Caldeira ◽  
Andrea Fuchs ◽  
...  
Keyword(s):  
Motor Neurons ◽  
Neural Activity ◽  
Sensory Input ◽  
Mouse Genetics ◽  
Spinal Interneurons ◽  
Neuronal Mechanisms ◽  
Cord Injury ◽  
Motor Neuron Excitability ◽  
Spinal Network ◽  
Opening Up

Spasms after spinal cord injury (SCI) are debilitating involuntary muscle contractions that have been associated with increased motor neuron excitability and decreased inhibition. However, whether spasms involve activation of premotor spinal excitatory neuronal circuits is unknown. Here we use mouse genetics, electrophysiology, imaging and optogenetics to directly target major classes of spinal interneurons as well as motor neurons during spasms in a mouse model of chronic SCI. We find that assemblies of excitatory spinal interneurons are recruited by sensory input into functional circuits to generate persistent neural activity, which interacts with both the graded expression of plateau potentials in motor neurons to generate spasms, and inhibitory interneurons to curtail them. Our study reveals hitherto unrecognized neuronal mechanisms for the generation of persistent neural activity under pathophysiological conditions, opening up new targets for treatment of muscle spasms after SCI.

scholarly journals Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

2020 ◽  
Author(s):  
KW Scangos ◽  
AN Khambhati ◽  
PM Daly ◽  
LW Owen ◽  
JR Manning ◽  
...  
Keyword(s):  
Neural Activity ◽  
Functional Organization ◽  
Activity Patterns ◽  
Computational Approach ◽  
Beta Activity ◽  
Targeted Intervention ◽  
Left Temporal Lobe ◽  
Spatiotemporal Resolution ◽  
Neural Recordings ◽  
Biological Substrates

AbstractQuantitative biological substrates of depression remain elusive. We carried out this study to determine whether application of a novel computational approach to high spatiotemporal resolution direct neural recordings may unlock the functional organization and coordinated activity patterns of depression networks. We identified two subnetworks conserved across the majority of individuals studied. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings identify distributed circuit activity associated with depression, link neural activity with functional connectivity profiles, and inform strategies for personalized targeted intervention.

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