scholarly journals Gradual progression from sensory to task-related processing in cerebral cortex

2018 ◽  
Vol 115 (30) ◽  
pp. E7202-E7211 ◽  
Author(s):  
Scott L. Brincat ◽  
Markus Siegel ◽  
Constantin von Nicolai ◽  
Earl K. Miller
Keyword(s):  
Sensory Information ◽  
Relevant Information ◽  
Neural Population ◽  
Motion Direction ◽  
Population Activity ◽  
Shape Information ◽  
Sensory Inputs ◽  
Neural Representations ◽  
Current Task ◽  
Cortical Regions

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, and color). Representations in visual areas middle temporal (MT) and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, and color category). Intermediate-level areas, including posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF), exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions (MT carried stronger motion information, and V4 and PIT carried stronger color and shape information), categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.

scholarly journals Gradual progression from sensory to task-related processing in cerebral cortex

2017 ◽  
Author(s):  
Scott L. Brincat ◽  
Markus Siegel ◽  
Constantin von Nicolai ◽  
Earl K. Miller
Keyword(s):  
Cerebral Cortex ◽  
Prefrontal Cortex ◽  
Neural Population ◽  
Population Activity ◽  
Shape Information ◽  
Sensory Inputs ◽  
Neural Representations ◽  
Cortical Hierarchy ◽  
Multiple Levels ◽  
Neural Population Activity

AbstractSomewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, color). Representations in visual areas MT and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, color category). Intermediate-level areas—posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF)—exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions—MT carried stronger motion information, V4 and PIT carried stronger color and shape information—categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.Significance statementThe earliest stages of processing in cerebral cortex reflect a relatively faithful copy of sensory inputs, but intelligent behavior requires abstracting behaviorally relevant concepts and categories. We examined how this transformation progresses through multiple levels of the cortical hierarchy by comparing neural representations in six cortical areas in monkeys categorizing across three visual domains. We found that categorical abstraction occurred in a gradual fashion across the cortical hierarchy and reached an apex in prefrontal cortex. Categorical coding did not respect classical models of large-scale cortical organization. The dimensionality of neural population activity was reduced in parallel with these representational changes. Our results shed light on how raw sensory inputs are transformed into behaviorally relevant abstractions.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

SummaryPrevious research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as “stiff” dimensions, while it is insensitive to many others (“sloppy” dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Internal models for interpreting neural population activity during sensorimotor control

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Matthew D Golub ◽  
Byron M Yu ◽  
Steven M Chase
Keyword(s):  
Internal Model ◽  
Sensory Information ◽  
Internal Models ◽  
Neural Population ◽  
Prior Beliefs ◽  
Population Activity ◽  
Motor Commands ◽  
Machine Interface ◽  
The Brain ◽  
Neural Population Activity

To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

scholarly journals Learning shapes cortical dynamics to enhance integration of relevant sensory input

2021 ◽  
Author(s):  
Angus Chadwick ◽  
Adil Khan ◽  
Jasper Poort ◽  
Antonin Blot ◽  
Sonja Hofer ◽  
...  
Keyword(s):  
Neural Coding ◽  
Sensory Input ◽  
Temporal Dynamics ◽  
Network Dynamics ◽  
Sensory Information ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Network Output

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

scholarly journals Correlations enhance the behavioral readout of neural population activity in association cortex

2020 ◽  
Author(s):  
Martina Valente ◽  
Giuseppe Pica ◽  
Caroline A. Runyan ◽  
Ari S. Morcos ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Neural Population ◽  
Association Cortex ◽  
Perceptual Discrimination ◽  
Imaging Data ◽  
Stimulus Information ◽  
Population Activity ◽  
Behavioral Choice

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

scholarly journals Attention enhances LFP phase coherence in macaque visual cortex, improving sensory processing

2018 ◽  
Author(s):  
Behzad Zareian ◽  
Mohammad Reza Daliri ◽  
Kourosh Maboudi ◽  
Hamid Abrishami Moghaddam ◽  
Stefan Treue ◽  
...  
Keyword(s):  
Neural Mechanism ◽  
Relevant Information ◽  
Phase Coherence ◽  
Neural Population ◽  
Attentional Modulation ◽  
Low Frequencies ◽  
Optimal Behavior ◽  
Cortical Regions ◽  
Visual Area Mt ◽  
The Brain

Attention selectively routes the most behaviorally relevant information among the vast pool of sensory inputs through cortical regions. Previous studies have shown that visual attention samples the surrounding stimuli periodically. However, the neural mechanism underlying this sampling in the sensory cortex, and whether the brain actively uses these rhythms, has remained elusive. Here, we hypothesize that selective attention controls the phase of oscillatory synaptic activities to efficiently process the relevant information in the brain. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) at low frequencies in macaque visual area MT. Our data reveal that phase coherence increases when attention is deployed towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the attentional modulation of stimulus induced firing rate, and importantly, a higher phase coherence leads to a faster behavioral response. Our results suggest a functional utilization of intrinsic neural oscillatory activities for better processing upcoming environmental stimuli, generating the optimal behavior.

scholarly journals How Senses Work Together: Cross-Modal Interactions between Primary Sensory Cortices

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Manuel Teichert ◽  
Jürgen Bolz
Keyword(s):  
Cerebral Cortex ◽  
Current Literature ◽  
Sensory Information ◽  
Convincing Evidence ◽  
Modal Interactions ◽  
Sensory Inputs ◽  
Sensory Modalities ◽  
Sensory Cortices ◽  
Cortical Regions ◽  
Processing Mechanisms

On our way through a town, the things we see can make us change the way we go. The things that we hear can make us stop or walk on, or the things we feel can cause us to wear a warm jacket or just a t-shirt. All these behaviors are mediated by highly complex processing mechanisms in our brain and reflect responses to many important sensory inputs. The mammalian cerebral cortex, which processes the sensory information, consists of largely specialized sensory areas mainly receiving information from their corresponding sensory modalities. The first cortical regions receiving the input from the outer world are the so called primary sensory cortices. Strikingly, there is convincing evidence that primary sensory cortices do not work in isolation but are substantially affected by other sensory modalities. Here, we will review previous and current literature on this cross-modal interplay.

scholarly journals Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

2021 ◽  
Author(s):  
Thomas Pfeffer ◽  
Christian Keitel ◽  
Daniel S. Kluger ◽  
Anne Keitel ◽  
Alena Russmann ◽  
...  
Keyword(s):  
Low Frequency ◽  
Intermediate Frequency ◽  
Brain Regions ◽  
Neuronal Population ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Linear Relationships ◽  
Cortical Regions ◽  
Aperiodic Component

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has only been characterized for a few selected brain regions so far. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by linear and non-linear relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. The non-linearity resembled an inverted U-shape whereby intermediate pupil sizes coincided with maximum 8-32 Hz activity. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

scholarly journals Primary visual cortex straightens natural video trajectories

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olivier J. Hénaff ◽  
Yoon Bai ◽  
Julie A. Charlton ◽  
Ian Nauhaus ◽  
Eero P. Simoncelli ◽  
...  
Keyword(s):  
Visual System ◽  
Image Sequences ◽  
Neural Population ◽  
Population Activity ◽  
Video Clips ◽  
Neural Representations ◽  
Naturally Occurring ◽  
Early Visual System ◽  
Population Trajectory ◽  
Processing Mechanisms

AbstractMany sensory-driven behaviors rely on predictions about future states of the environment. Visual input typically evolves along complex temporal trajectories that are difficult to extrapolate. We test the hypothesis that spatial processing mechanisms in the early visual system facilitate prediction by constructing neural representations that follow straighter temporal trajectories. We recorded V1 population activity in anesthetized macaques while presenting static frames taken from brief video clips, and developed a procedure to measure the curvature of the associated neural population trajectory. We found that V1 populations straighten naturally occurring image sequences, but entangle artificial sequences that contain unnatural temporal transformations. We show that these effects arise in part from computational mechanisms that underlie the stimulus selectivity of V1 cells. Together, our findings reveal that the early visual system uses a set of specialized computations to build representations that can support prediction in the natural environment.

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