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.

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 A silicon early visual system as a model animal

2004 ◽  
Vol 44 (17) ◽  
pp. 2083-2089 ◽  
Author(s):  
Tobi Delbrück ◽  
Shih-Chii Liu
Keyword(s):  
Visual System ◽  
Model Animal ◽  
Early Visual System

Feature-Linking Model for Image Enhancement

2016 ◽  
Vol 28 (6) ◽  
pp. 1072-1100 ◽  
Author(s):  
Kun Zhan ◽  
Jicai Teng ◽  
Jinhui Shi ◽  
Qiaoqiao Li ◽  
Mingying Wang
Keyword(s):  
Neural Networks ◽  
Image Enhancement ◽  
Human Visual System ◽  
Input Image ◽  
Temporal Coding ◽  
Essential Dimension ◽  
Neural Representations ◽  
Gamma Band Oscillations ◽  
Processing Mechanisms ◽  
Enhancement Algorithm

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.

scholarly journals Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Movement Planning ◽  
Neural Population ◽  
Great Effort ◽  
Single Neurons ◽  
Latent Factors ◽  
Brain Machine Interfaces ◽  
Population Activity ◽  
Systems Perspective ◽  
Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

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 Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Sign in / Sign up

Export Citation Format

Share Document