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

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.

2018 ◽  
Vol 115 (30) ◽  
pp. E7202-E7211 ◽  
Author(s):  
Scott L. Brincat ◽  
Markus Siegel ◽  
Constantin von Nicolai ◽  
Earl K. Miller

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.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco

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.


2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco

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.


2011 ◽  
Vol 467-469 ◽  
pp. 1291-1296
Author(s):  
Wen Wen Bai ◽  
Xin Tian

Working memory is one of important cognitive functions and recent studies demonstrate that prefrontal cortex plays an important role in working memory. But the issue that how neural activity encodes during working memory task is still a question that lies at the heart of cognitive neuroscience. The aim of this study is to investigate neural ensemble coding mechanism via average firing rate during working memory task. Neural population activity was measured simultaneously from multiple electrodes placed in prefrontal cortex while rats were performing a working memory task in Y-maze. Then the original data was filtered by a high-pass filtering, spike detection and spike sorting, spatio-temporal trains of neural population were ultimately obtained. Then, the average firing rates were computed in a selected window (500ms) with a moving step (125ms). The results showed that the average firing rate were higher during workinig memory task, along with obvious ensemble activity. Conclusion: The results indicate that the working memory information is encoded with neural ensemble activity.


eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Matthew D Golub ◽  
Byron M Yu ◽  
Steven M Chase

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.


2018 ◽  
Author(s):  
Adrianna R. Loback ◽  
Michael J. Berry

When correlations within a neural population are strong enough, neural activity in early visual areas is organized into a discrete set of clusters. Here, we show that a simple, biologically plausible circuit can learn and then readout in real-time the identity of experimentally measured clusters of retinal ganglion cell population activity. After learning, individual readout neurons develop cluster tuning, meaning that they respond strongly to any neural activity pattern in one cluster and weakly to all other inputs. Different readout neurons specialize for different clusters, and all input clusters can be learned, as long as the number of readout units is mildly larger than the number of input clusters. We argue that this operation can be repeated as signals flow up the cortical hierarchy.


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