scholarly journals Representational drift in the mouse visual cortex

2020 ◽  
Author(s):  
Daniel Deitch ◽  
Alon Rubin ◽  
Yaniv Ziv
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Population Level ◽  
Population Activity ◽  
Cortical Areas ◽  
Electrophysiological Recordings ◽  
The Face ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Over Time

AbstractNeuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such ‘representational drift’ occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells’ activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

scholarly journals Dynamic causal communication channels between neocortical areas

2021 ◽  
Author(s):  
Mitra Javadzadeh ◽  
Sonja B Hofer
Keyword(s):  
Sensory Information ◽  
Communication Channels ◽  
Brain Regions ◽  
Population Activity ◽  
V1 Neurons ◽  
Cortical Areas ◽  
Electrophysiological Recordings ◽  
Causal Approach ◽  
Visual Cortical ◽  
Over Time

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behaviour. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence visual cortical areas had on each other was surprisingly variable over time. Both feedforward and feedback pathways reliably affected different subpopulations of target neurons at different moments during processing of a visual stimulus, resulting in dynamically rotating communication dimensions between the two cortical areas. The influence of feedback on primary visual cortex (V1) became even more dynamic when visual stimuli were associated with a reward, impacting different subsets of V1 neurons within tens of milliseconds. This, in turn, controlled the geometry of V1 population activity in a behaviourally relevant manner. Thus, distributed neural populations interact through dynamically reorganizing and context- dependent communication channels to evaluate sensory information.

scholarly journals Feedforward and feedback interactions between visual cortical areas use different population activity patterns

2021 ◽  
Author(s):  
João D. Semedo ◽  
Anna I. Jasper ◽  
Amin Zandvakili ◽  
Amir Aschner ◽  
Christian K. Machens ◽  
...  
Keyword(s):  
Brain Function ◽  
Activity Patterns ◽  
Sensory Information ◽  
Neuronal Population ◽  
Stimulus Onset ◽  
Population Activity ◽  
Cortical Areas ◽  
Visual Areas ◽  
Population Interactions ◽  
Visual Cortical

AbstractBrain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate “channels”, such that feedback signaling does not directly affect activity that is fed forward.

scholarly journals Orientation and color tuning of the human visual gamma rhythm

2021 ◽  
Author(s):  
Ye Li ◽  
William Bosking ◽  
Michael S Beauchamp ◽  
Sameer A Sheth ◽  
Daniel Yoshor ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Brain Activity ◽  
Population Level ◽  
Gamma Oscillations ◽  
Cell Orientation ◽  
Population Activity ◽  
Color Tuning ◽  
Human Visual Cortex ◽  
Gamma Rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.

scholarly journals Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

2021 ◽  
Author(s):  
Svenja Melbaum ◽  
David Eriksson ◽  
Thomas Brox ◽  
Ilka Diester
Keyword(s):  
Sensorimotor Cortex ◽  
Activity Patterns ◽  
Population Activity ◽  
Electrophysiological Recordings ◽  
Primary Sensory Cortex ◽  
Behavioral Paradigm ◽  
Structure Of Population ◽  
Freely Moving ◽  
Low Dimensional ◽  
Neuronal Code

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements which are strictly controlled via the experimental settings. It remains unclear how results can be carried over to less constrained behavior, i.e. freely moving subjects. Towards this goal, we developed a self-paced behavioral paradigm which encouraged rats to conduct different types of movements. Via bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking, we identified behavioral coupling of neurons with lateralization and an anterior-posterior gradient from premotor to primary sensory cortex. The structure of population activity patterns was conserved across animals, in spite of severe undersampling of the total number of neurons and variations of electrode positions across individuals. Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.

scholarly journals Discovery and Temporal Analysis of MOOC Study Patterns

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mina Shirvani Boroujeni ◽  
Pierre Dillenbourg
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Activity Patterns ◽  
Synthetic Data ◽  
Temporal Analysis ◽  
Time Step ◽  
Data Driven Approach ◽  
Learning Behaviours ◽  
Over Time

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals’ learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners’ interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners’ behaviour in this context.

Alzheimer’s Disease Progressively Alters the Face-Evoked Visual-Processing Network

2020 ◽  
Vol 77 (3) ◽  
pp. 1025-1042
Author(s):  
Jie Huang ◽  
Paul Beach ◽  
Andrea Bozoki ◽  
David C. Zhu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Lower Order ◽  
Visual Processing ◽  
Higher Order ◽  
Cortical Areas ◽  
The Face ◽  
Visual Cortical ◽  
Processing Network

Background: Postmortem studies of Alzheimer’s disease (AD) brains not only find amyloid-β (Aβ) and neurofibrillary tangles (NFT) in the primary and associative visual cortical areas, but also reveal a temporally successive sequence of AD pathology beginning in higher-order visual association areas, followed by involvement of lower-order visual processing regions with disease progression, and extending to primary visual cortex in late-stage disease. These findings suggest that neuronal loss associated with Aβ and NFT aggregation in these areas may alter not only the local neuronal activation but also visual neural network activity. Objective: Applying a novel method to identify the visual functional network and investigate the association of the network changes with disease progression. Methods: To investigate the effect of AD on the face-evoked visual-processing network, 8 severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a task-fMRI study of viewing face photos. Results: For the HS, the identified group-mean visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, this network was disrupted and reduced in the AD patients in a disease-severity dependent manner: for the MAD patients, the network was disrupted and reduced mainly in the higher-order visual association areas; for the SAD patients, the network was nearly absent in the higher-order association areas, and disrupted and reduced in the lower-order areas. Conclusion: This finding is consistent with the current canonical view of the temporally successive sequence of AD pathology through visual cortical areas.

scholarly journals A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task

2020 ◽  
Vol 32 (8) ◽  
pp. 1455-1465
Author(s):  
Yue Liu ◽  
Scott L. Brincat ◽  
Earl K. Miller ◽  
Michael E. Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Level ◽  
Population Coding ◽  
Task Structure ◽  
Population Activity ◽  
Associate Learning ◽  
Task Variables ◽  
Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some “silent” mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.

scholarly journals Stable behavioral state-specific large scale activity patterns in the developing cortex of neonates

2021 ◽  
Author(s):  
Nima Mojtahedi ◽  
Yury Kovalchuk ◽  
Alexander Böttcher ◽  
Olga Garaschuk
Keyword(s):  
Large Scale ◽  
Specific Activity ◽  
Activity Patterns ◽  
Building Blocks ◽  
Retrosplenial Cortex ◽  
Behavioral State ◽  
Dorsal Cortex ◽  
Complex Activity ◽  
Cortical Areas ◽  
Exact Function

AbstractEndogenous neuronal activity is a hallmark of the developing brain. In rodents, a handful of such activities were described in different cortical areas but the unifying macroscopic perspective is still lacking. Here we combined large-scale in vivo Ca2+ imaging of the dorsal cortex in non-anesthetized neonatal mice with advanced mathematical analyses to reveal unique behavioral state-specific maps of endogenous activity. These maps were remarkably stable over time within and across experiments and used patches of correlated activity with little hemispheric symmetry as well as stationary and propagating waves as building blocks. Importantly, the maps recorded during motion and rest were almost inverse, with sensory-motor areas active during motion and posterior-lateral areas active at rest. The retrosplenial cortex engaged in both resting- and motion-related activities, building functional long-range connections with respective cortical areas. The data obtained bind different region-specific activity patterns described so far into a single consistent picture and set the stage for future inactivation studies, probing the exact function of this complex activity pattern for cortical wiring in neonates.

scholarly journals Inhibition-Dominated Rich-Club Shapes Dynamics in Cortical Microcircuits of Awake Behaving Mice

2021 ◽  
Author(s):  
Hadi Hafizi ◽  
Sunny Nigam ◽  
Josh Barnathan ◽  
Ian Stevenson ◽  
Sotiris C Masmanidis ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Large Scale ◽  
Population Level ◽  
Inhibitory Neurons ◽  
Population Activity ◽  
Rich Club ◽  
Large Scale Network ◽  
Type Composition ◽  
Scale Network ◽  
The Rich

Functional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, Granger causality showed that neurons in the rich-club are the dominant drivers of overall population activity and do so in a frequency-specific manner. Moreover, early activity ofinhibitory neurons, along with excitatory neurons within the rich-club, synergistically predicts the duration of neuronal cascades. Together, these results reveal an unexpected role of a highly connected core of inhibitory neurons in driving and sustaining activity in local cortical networks.

scholarly journals Learning differentially shapes prefrontal and hippocampal activity during classical conditioning

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jan L Klee ◽  
Bryan C Souza ◽  
Francesco P Battaglia
Keyword(s):  
Classical Conditioning ◽  
Single Cell ◽  
Activity Patterns ◽  
Population Level ◽  
Electrophysiological Recordings ◽  
Hippocampal Activity ◽  
Hippocampal Ca1 ◽  
Persistent Firing ◽  
Ca1 Area ◽  
Assembly Activity

The ability to use sensory cues to inform goal directed actions is a critical component of behavior. To study how sounds guide anticipatory licking during classical conditioning, we employed high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC) in mice. CA1 and PFC neurons undergo distinct learning dependent changes at the single cell level and maintain representations of cue identity at the population level. In addition, reactivation of task-related neuronal assemblies during hippocampal awake Sharp-Wave Ripples (aSWR) changed within individual sessions in CA1 and over the course of multiple sessions in PFC. Despite both areas being highly engaged and synchronized during the task, we found no evidence for coordinated single cell or assembly activity during conditioning trials or aSWR. Taken together, our findings support the notion that persistent firing and reactivation of task-related neural activity patterns in CA1 and PFC support learning during classical conditioning.

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