scholarly journals Learning and attention reveal a general relationship between neuronal variability and perception

2017 ◽  
Author(s):  
Amy M. Ni ◽  
Douglas A. Ruff ◽  
Joshua J. Alberts ◽  
Jen Symmonds ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Neuronal Population ◽  
Population Coding ◽  
Task Demands ◽  
Spike Count ◽  
Current Debate ◽  
Visually Guided ◽  
Population Activity ◽  
Neuronal Populations ◽  
And Performance

The trial-to-trial response variability that is shared between pairs of neurons (termed spike count correlations1 or rSC) has been the subject of many recent studies largely because it might limit the amount of information that can be encoded by neuronal populations. Spike count correlations are flexible and change depending on task demands2-7. However, the relationship between correlated variability and information coding is a matter of current debate2-14. This debate has been difficult to resolve because testing the theoretical predictions would require simultaneous recordings from an experimentally unfeasible number of neurons. We hypothesized that if correlated variability limits population coding, then spike count correlations in visual cortex should a) covary with subjects’ performance on visually guided tasks and b) lie along the dimensions in neuronal population space that contain information that is used to guide behavior. We focused on two processes that are known to improve visual performance: visual attention, which allows observers to focus on important parts of a visual scene15-17, and perceptual learning, which slowly improves observers’ ability to discriminate specific, well-practiced stimuli18-20. Both attention and learning improve performance on visually guided tasks, but the two processes operate on very different timescales and are typically studied using different perceptual tasks. Here, by manipulating attention and learning in the same task, subjects, trials, and neuronal populations, we show that there is a single, robust relationship between correlated variability in populations of visual neurons and performance on a change-detection task. We also propose an explanation for the mystery of how correlated variability might affect performance: it is oriented along the dimensions of population space used by the animal to make perceptual decisions. Our results suggest that attention and learning affect the same aspects of the neuronal population activity in visual cortex, which may be responsible for learning- and attention-related improvements in behavioral performance. More generally, our study provides a framework for leveraging the activity of simultaneously recorded populations of neurons, cognitive factors, and perceptual decisions to understand the neuronal underpinnings of behavior.

scholarly journals Perceptual detection depends on spike count integration

2019 ◽  
Author(s):  
Jackson J. Cone ◽  
Morgan L. Bade ◽  
Nicolas Y. Masse ◽  
Elizabeth A. Page ◽  
David J. Freedman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Visual Cortex ◽  
Recurrent Neural Networks ◽  
Neural Circuit ◽  
Visual Detection ◽  
Spike Count ◽  
Neuronal Responses ◽  
Neuronal Populations ◽  
And Behavior

AbstractWhenever the retinal image changes some neurons in visual cortex increase their rate of firing, while others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements were delivered to the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity was costly. This work clarifies neuronal decoding strategies employed by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.

scholarly journals Optimal Tuning Widths in Population Coding of Periodic Variables

2006 ◽  
Vol 18 (7) ◽  
pp. 1555-1576 ◽  
Author(s):  
Marcelo A. Montemurro ◽  
Stefano Panzeri
Keyword(s):  
Cortical Neurons ◽  
Neuronal Population ◽  
Population Coding ◽  
Model Parameters ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Periodic Stimulus ◽  
Tuning Width ◽  
Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

scholarly journals Primary motor cortex activity traces distinct trajectories of population dynamics during spontaneous facial motor sequences in mice

2021 ◽  
Author(s):  
Juan Carlos Boffi ◽  
Tristan Wiessalla ◽  
Robert Prevedel
Keyword(s):  
Population Dynamics ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Population ◽  
Future Research ◽  
Sequencing Analysis ◽  
Population Activity ◽  
Neuronal Populations ◽  
Custom Made ◽  
Facial Area

AbstractWe explore the link between on-going neuronal activity at primary motor cortex (M1) and face movement in awake mice. By combining custom-made behavioral sequencing analysis and fast volumetric Ca2+-imaging, we simultaneously tracked M1 population activity during many different facial motor sequences. We show that a facial area of M1 displays distinct trajectories of neuronal population dynamics across different spontaneous facial motor sequences, suggesting an underlying population dynamics code.Significance statementHow our brain controls a seemingly limitless diversity of body movements remains largely unknown. Recent research brings new light into this subject by showing that neuronal populations at the primary motor cortex display different dynamics during forelimb reaching movements versus grasping, which suggests that different motor sequences could be associated with distinct motor cortex population dynamics. To explore this possibility, we designed an experimental paradigm for simultaneously tracking the activity of neuronal populations in motor cortex across many different motor sequences. Our results support the concept that distinct population dynamics encode different motor sequences, bringing new insight into the role of motor cortex in sculpting behavior while opening new avenues for future research.

scholarly journals Human electrocorticography reveals a common neurocognitive pathway for mentalizing about the self and others

2021 ◽  
Author(s):  
Kevin Tan ◽  
Amy Daitch ◽  
Pedro Pinheiro-Chagas ◽  
Kieran Fox ◽  
Josef Parvizi ◽  
...  
Keyword(s):  
Social Cognitive ◽  
Response Times ◽  
Human Subjects ◽  
Neuronal Population ◽  
Spatiotemporal Resolution ◽  
Population Activity ◽  
Psychological Traits ◽  
Self And Other ◽  
Neuronal Populations ◽  
The Social

Abstract Hundreds of neuroimaging studies show that mentalizing (i.e., theory of mind) recruits default mode network (DMN) regions with remarkable consistency. Nevertheless, the social-cognitive functions of individual DMN regions remain unclear, perhaps due to the limited spatiotemporal resolution of neuroimaging. We used electrocorticography (ECoG) to record neuronal population activity while 16 human subjects judged the psychological traits of themselves and others. Self- and other-mentalizing recruited near-identical neuronal populations in a common spatiotemporal sequence: activations were earliest in visual cortex, followed by temporoparietal DMN regions, and finally medial prefrontal cortex. Critically, regions with later activations showed greater functional specificity for mentalizing, greater self/other differentiation, and stronger associations with behavioral response times. Moreover, other-mentalizing evoked slower and lengthier activations than self-mentalizing across successive DMN regions, suggesting temporally extended demands on higher-level processes. Our results reveal a common neurocognitive pathway for self- and other-mentalizing that follows a hierarchy of functional specialization across DMN regions.

scholarly journals Divergence in Population Coding for Space between Dorsal and Ventral CA1

2021 ◽  
Author(s):  
Udaysankar Chockanathan ◽  
Krishnan Padmanabhan
Keyword(s):  
Maximum Entropy ◽  
Virtual Environment ◽  
Spatial Information ◽  
Dorsal Hippocampus ◽  
Population Coding ◽  
Field Size ◽  
List Type ◽  
Entropy Model ◽  
Population Activity ◽  
Neuronal Populations

SummaryMolecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. We found that the populations of neurons in dorsal CA1 had a higher dimensionality and showed more complex patterns of activity, translating to more information being encoded, as compared ensembles in vCA1. Furthermore, a pairwise maximum entropy model was better at predicting the structure of global patterns of activity in ventral CA1 as compared to dorsal CA1. Taken together, we uncovered different coding strategies that likely emerge from anatomical and physiological differences along the longitudinal axis of hippocampus and that may, in turn, underpin the divergent ethological roles of dorsal and ventral CA1.HighlightsNo differences in dCA1 and vCA1 place field size in recordings of neuronal populations in mice navigating a virtual environmentdCA1 has higher single-neuron and population-level spatial information compared to vCA1 due to differences in the sparsity of firing.Population activity of dCA1 has higher entropy and is higher dimensional than vCA1Pairwise maximum entropy models are better at predicting population activity in vCA1 compared to dCA1

scholarly journals Low rank mechanisms underlying flexible visual representations

2020 ◽  
Vol 117 (47) ◽  
pp. 29321-29329 ◽  
Author(s):  
Douglas A. Ruff ◽  
Cheng Xue ◽  
Lily E. Kramer ◽  
Faisal Baqai ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Cognitive Processes ◽  
Task Switching ◽  
Visual Processing ◽  
Neuronal Population ◽  
Low Rank ◽  
Visual Neurons ◽  
Sensory Stimuli ◽  
Neuronal Populations ◽  
Wide Range

Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex have heterogeneous dependence on stimulus properties (e.g., contrast), processes that affect all stages of visual processing (e.g., adaptation), and cognitive processes (e.g., attention or task switching). Understanding whether these processes affect similar neuronal populations and whether they have similar effects on entire populations can provide insight into whether they utilize analogous mechanisms. In particular, it has recently been demonstrated that attention has low rank effects on the covariability of populations of visual neurons, which impacts perception and strongly constrains mechanistic models. We hypothesized that measuring changes in population covariability associated with other sensory and cognitive processes could clarify whether they utilize similar mechanisms or computations. Our experimental design included measurements in multiple visual areas using four distinct sensory and cognitive processes. We found that contrast, adaptation, attention, and task switching affect the variability of responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit may use similar mechanisms to perform many forms of modulation and likely reflects a general principle that applies to a wide range of brain areas and sensory, cognitive, and motor processes.

scholarly journals Spontaneous behaviors drive multidimensional, brainwide activity

Science ◽  
2019 ◽  
Vol 364 (6437) ◽  
pp. eaav7893 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Charu Bai Reddy ◽  
Matteo Carandini ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spontaneous Activity ◽  
Random Noise ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Neuronal Populations ◽  
Ongoing Behavior ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
External Stimuli

Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

scholarly journals Encoding of Naturalistic Optic Flow by a Population of Blowfly Motion-Sensitive Neurons

2006 ◽  
Vol 96 (3) ◽  
pp. 1602-1614 ◽  
Author(s):  
K. Karmeier ◽  
J. H. van Hateren ◽  
R. Kern ◽  
M. Egelhaaf
Keyword(s):  
Optic Flow ◽  
Three Dimensional ◽  
Real Life ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Free Flight ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Neuronal Populations ◽  
Self Motion

In sensory systems information is encoded by the activity of populations of neurons. To analyze the coding properties of neuronal populations sensory stimuli have usually been used that were much simpler than those encountered in real life. It has been possible only recently to stimulate visual interneurons of the blowfly with naturalistic visual stimuli reconstructed from eye movements measured during free flight. Therefore we now investigate with naturalistic optic flow the coding properties of a small neuronal population of identified visual interneurons in the blowfly, the so-called VS and HS neurons. These neurons are motion sensitive and directionally selective and are assumed to extract information about the animal's self-motion from optic flow. We could show that neuronal responses of VS and HS neurons are mainly shaped by the characteristic dynamical properties of the fly's saccadic flight and gaze strategy. Individual neurons encode information about both the rotational and the translational components of the animal's self-motion. Thus the information carried by individual neurons is ambiguous. The ambiguities can be reduced by considering neuronal population activity. The joint responses of different subpopulations of VS and HS neurons can provide unambiguous information about the three rotational and the three translational components of the animal's self-motion and also, indirectly, about the three-dimensional layout of the environment.

scholarly journals A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

2017 ◽  
Author(s):  
Sebastián A. Romano ◽  
Verónica Pérez-Schuster ◽  
Adrien Jouary ◽  
Alessia Candeo ◽  
Jonathan Boulanger-Weill ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Neuronal Population ◽  
Population Coding ◽  
Imaging Data ◽  
Neuronal Responses ◽  
Population Activity ◽  
The Brain

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

scholarly journals Neuronal population mechanisms of lightness perception

2018 ◽  
Vol 120 (5) ◽  
pp. 2296-2310 ◽  
Author(s):  
Douglas A. Ruff ◽  
David H. Brainard ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Lightness Perception ◽  
Neuronal Mechanism ◽  
Area V4 ◽  
Neuronal Populations ◽  
Types Of Information ◽  
The Way

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses. NEW & NOTEWORTHY A core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

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