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 A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

2019 ◽  
Author(s):  
Yue Liu ◽  
Scott L Brincat ◽  
Earl K Miller ◽  
Michael E Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Data ◽  
Population Level ◽  
Population Coding ◽  
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 paper 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 task-relevant variables whereas HPC only transiently encodes the identity of the stimuli. We also found 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 the learning-dependent changes in the population activity.

scholarly journals Population coding and oscillatory subspace synchronization integrate context into actions

2021 ◽  
Author(s):  
Jan Weber ◽  
Anne-Kristin Solbakk ◽  
Alejandro Blenkmann ◽  
Anais Llorens ◽  
Ingrid Funderud ◽  
...  
Keyword(s):  
Large Scale ◽  
Population Level ◽  
Population Coding ◽  
Action Plans ◽  
Contextual Cues ◽  
Population Activity ◽  
Scale Population ◽  
Hand Off ◽  
Neurophysiological Mechanisms ◽  
Context Dependent

Contextual cues and prior evidence guide human goal-directed behavior. To date, the neurophysiological mechanisms that implement contextual priors to guide subsequent actions remain unclear. Here we demonstrate that increasing behavioral uncertainty introduces a shift from an oscillatory to a continuous processing mode in human prefrontal cortex. At the population level, we found that oscillatory and continuous dynamics reflect dissociable signatures that support distinct aspects of encoding, transmission and execution of context-dependent action plans. We show that prefrontal population activity encodes predictive context and action plans in serially unfolding orthogonal subspaces, while prefrontal-motor theta oscillations synchronize action-encoding population subspaces to mediate the hand-off of action plans. Collectively, our results reveal how two key features of large-scale population activity, namely continuous population trajectories and oscillatory synchrony, operate in concert to guide context-dependent human behavior.

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 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 Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

2016 ◽  
Vol 114 (2) ◽  
pp. 394-399 ◽  
Author(s):  
John D. Murray ◽  
Alberto Bernacchia ◽  
Nicholas A. Roy ◽  
Christos Constantinidis ◽  
Ranulfo Romo ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Population Level ◽  
Population Coding ◽  
Neural Dynamics ◽  
Stable Population ◽  
Neuron Activity ◽  
Delayed Response

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

scholarly journals Population coding of strategic variables during foraging in freely-moving macaques

2019 ◽  
Author(s):  
Neda Shahidi ◽  
Paul Schrater ◽  
Tony Wright ◽  
Xaq Pitkow ◽  
Valentin Dragoi
Keyword(s):  
Prefrontal Cortex ◽  
Time Course ◽  
Internal Representation ◽  
Population Coding ◽  
Neural Responses ◽  
Population Activity ◽  
Relevant Task ◽  
Dorsolateral Prefrontal ◽  
Future Outcomes ◽  
Foraging Task

Animals forage within their environment to extract valuable resources at lowest cost. Previous studies have suggested that animals simply maximize the current flow of reward without predicting the future outcomes of their actions and that this recent reward rate is represented in various brain areas. To test this, we devised a foraging task in which the relevant reward dynamics were hidden from the animal, and wirelessly record population activity in dorsolateral prefrontal cortex (dlPFC) while monkeys forage freely in their environment. We discover that their brains indeed contain predictions of future rewards and plans of their next actions. By decoding the dynamic reward probability and the memory of recent outcomes from the dlPFC population response, we show that monkeys create an internal representation of reward dynamics. The decoded variables predicted animal’s subsequent actions better than either the true experimental variables or the raw neural responses. Our results suggest that the relevant task variables and behavioral decisions are dynamically encoded in prefrontal cortex during the time course of foraging.

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 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 The population tracking model: A simple, scalable statistical model for neural population data

2016 ◽  
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

AbstractOur 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 (∼ 2Neurons). 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, so 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, ∼ 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, 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 Ca2+ and voltage imaging tools.

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