scholarly journals Neuronal population activity dynamics reveal a low-dimensional signature of operant learning

2021 ◽  
Author(s):  
Renan M. Costa ◽  
Douglas A. Baxter ◽  
John H. Byrne
Keyword(s):  
Single Neuron ◽  
Brain Regions ◽  
Neuronal Population ◽  
High Dimensional ◽  
Operant Learning ◽  
Population Activity ◽  
Temporal Shift ◽  
Voltage Imaging ◽  
Low Dimensional ◽  
Activity Dynamics

AbstractLearning engages a high-dimensional neuronal population space spanning multiple brain regions. We identified a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, we identified two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning.

Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rainer W. Friedrich ◽  
Adrian A. Wanner
Keyword(s):  
Neuronal Population ◽  
Higher Order ◽  
Order Structure ◽  
Annual Review ◽  
Publication Date ◽  
Population Activity ◽  
Stimulus Features ◽  
Processing And Storage ◽  
Low Dimensional ◽  
Neuronal Computation

The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

scholarly journals Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

2021 ◽  
Author(s):  
C. Daniel Greenidge ◽  
Benjamin Scholl ◽  
Jacob Yates ◽  
Jonathan W. Pillow
Keyword(s):  
Gaussian Process ◽  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Neural Population ◽  
High Dimensional ◽  
Neural Decoding ◽  
Population Activity ◽  
Population Codes ◽  
Low Dimensional ◽  
Dimensional Variable

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

scholarly journals No evidence for a special role of language in feature-based categorization

2021 ◽  
Author(s):  
Yael Benn ◽  
Anna A. Ivanova ◽  
Oliver Clark ◽  
Zachary Mineroff ◽  
Chloe Seikus ◽  
...  
Keyword(s):  
Language Impairment ◽  
Relevant Information ◽  
Brain Regions ◽  
Activity Level ◽  
High Dimensional ◽  
Special Role ◽  
Language Resources ◽  
Feature Based ◽  
Low Dimensional

AbstractThe role of language in mediating or augmenting human thought is the subject of long-standing debate. One specific claim links language and the ability to categorize objects based on a certain feature. According to this view, language resources are critical for feature-based categorization because verbal labels can help maintain focus on the relevant categorization criterion and reduce interference from other (irrelevant) features. As a result, language impairment is expected to affect categorization of items grouped according to a single feature (low-dimensional categories, e.g., ‘Things made of wood’), where many irrelevant features need to be inhibited, more than categorization of items that share many features (high-dimensional categories, e.g., ‘Animals’), where few irrelevant features need to be inhibited. We here present findings from individuals with aphasia that go against this hypothesis (Experiments 1 and 2). We also present fMRI data from young healthy adults, showing that the language brain regions exhibit low activity level during categorization, for both low-dimensional and high-dimensional categories (Experiment 3). In tandem, these results demonstrate that language is not critical for object categorization. Our work contributes to the growing evidence that, although language may assist in accessing task-relevant information (e.g., instructions), many cognitive tasks in adult brains proceed without recruiting the language system.

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 Neural population dynamics in motor cortex are different for reach and grasp

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Aneesha K Suresh ◽  
James M Goodman ◽  
Elizaveta V Okorokova ◽  
Matthew Kaufman ◽  
Nicholas G Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Basic Structure ◽  
Neuronal Population ◽  
Neural Population ◽  
Population Activity ◽  
Muscle Activations ◽  
Low Dimensional ◽  
Analytical Approaches

Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

scholarly journals Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

2021 ◽  
Author(s):  
Thomas Pfeffer ◽  
Christian Keitel ◽  
Daniel S. Kluger ◽  
Anne Keitel ◽  
Alena Russmann ◽  
...  
Keyword(s):  
Low Frequency ◽  
Intermediate Frequency ◽  
Brain Regions ◽  
Neuronal Population ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Linear Relationships ◽  
Cortical Regions ◽  
Aperiodic Component

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has only been characterized for a few selected brain regions so far. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by linear and non-linear relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. The non-linearity resembled an inverted U-shape whereby intermediate pupil sizes coincided with maximum 8-32 Hz activity. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

scholarly journals A spiral attractor network drives rhythmic locomotion

2017 ◽  
Author(s):  
Angela M. Bruno ◽  
William N. Frost ◽  
Mark D. Humphries
Keyword(s):  
Movement Control ◽  
High Dimensional ◽  
Fictive Locomotion ◽  
Pedal Ganglion ◽  
Joint Activity ◽  
Population Activity ◽  
Neural Populations ◽  
Low Dimensional ◽  
Circuit Function ◽  
Low Dimensional Dynamics

AbstractThe joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia’s pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit −a spiral −in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons’ participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

scholarly journals High-dimensional geometry of population responses in visual cortex

2018 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Matteo Carandini ◽  
Kenneth D. Harris
Keyword(s):  
Power Law ◽  
Image Coding ◽  
Principal Component ◽  
Neuronal Population ◽  
Neural Population ◽  
High Dimensional ◽  
Natural Image ◽  
Population Activity ◽  
Visual Cortical ◽  
Lower Dimensional

AbstractA neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the nth principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

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