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 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 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 New neural activity patterns emerge with long-term learning

2019 ◽  
Vol 116 (30) ◽  
pp. 15210-15215 ◽  
Author(s):  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Jay A. Hennig ◽  
Alan D. Degenhart ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Causal Link ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Neural Population Activity

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

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 The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data

2017 ◽  
Vol 29 (1) ◽  
pp. 50-93 ◽  
Author(s):  
Cian O’Donnell ◽  
J. Tiago Gonçalves ◽  
Nick Whiteley ◽  
Carlos Portera-Cailliau ◽  
Terrence J. Sejnowski
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Population Data ◽  
Population Coding ◽  
Network Activity ◽  
Neural Population ◽  
Population Activity ◽  
Tracking Model ◽  
Population Rate ◽  
Neural Population Activity

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca[Formula: see text] and voltage imaging tools.

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.

Sign in / Sign up

Export Citation Format

Share Document