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 Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept

2015 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Ensemble Empirical Mode Decomposition ◽  
Proof Of Concept ◽  
Imaging Data ◽  
Mode Decomposition ◽  
Multiple Resolutions ◽  
The Brain

One of the goals of systems and computational neuroscience is to understand how information is processed by a single neuron and integrated by a network of neurons. A plausible approach to identifying spatial neighborhoods of the brain that host potential neural networks of interest is by observing spatially-bounded aggregates of neural activity. To this end, the potential of the multidimensional ensemble empirical mode decomposition algorithm in extracting multiple resolutions of neural activity from calcium imaging data is evaluated.

scholarly journals Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept

2015 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Ensemble Empirical Mode Decomposition ◽  
Proof Of Concept ◽  
Imaging Data ◽  
Mode Decomposition ◽  
Multiple Resolutions ◽  
The Brain

One of the goals of systems and computational neuroscience is to understand how information is processed by a single neuron and integrated by a network of neurons. A plausible approach to identifying spatial neighborhoods of the brain that host potential neural networks of interest is by observing spatially-bounded aggregates of neural activity. To this end, the potential of the multidimensional ensemble empirical mode decomposition algorithm in extracting multiple resolutions of neural activity from calcium imaging data is evaluated.

Olfactory modulation of barrel cortex activity during active whisking and passive whisker stimulation

2021 ◽  
Author(s):  
Anthony Renard ◽  
Evan Harrell ◽  
Brice Bathallier
Keyword(s):  
Facial Nerve ◽  
Calcium Imaging ◽  
Tactile Stimulation ◽  
Barrel Cortex ◽  
Neuronal Responses ◽  
Population Activity ◽  
Tactile Information ◽  
Large Populations ◽  
Whisker Stimulation ◽  
The Impact

Abstract Rodents depend on olfaction and touch to meet many of their fundamental needs. The joint significance of these sensory systems is underscored by an intricate coupling between sniffing and whisking. However, the impact of simultaneous olfactory and tactile inputs on sensory representations in the cortex remains elusive. To study these interactions, we recorded large populations of barrel cortex neurons using 2-photon calcium imaging in head-fixed mice during olfactory and tactile stimulation. We find that odors alter barrel cortex activity in at least two ways, first by enhancing whisking, and second by central cross-talk that persists after whisking is abolished by facial nerve sectioning. Odors can either enhance or suppress barrel cortex neuronal responses, and while odor identity can be decoded from population activity, it does not interfere with the tactile representation. Thus, barrel cortex represents olfactory information which, in the absence of learned associations, is coded independently of tactile information.

scholarly journals Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells

1998 ◽  
Vol 79 (2) ◽  
pp. 1017-1044 ◽  
Author(s):  
Kechen Zhang ◽  
Iris Ginzburg ◽  
Bruce L. McNaughton ◽  
Terrence J. Sejnowski
Keyword(s):  
Bayesian Methods ◽  
Neuronal Population ◽  
Place Cells ◽  
The Body ◽  
Unified Framework ◽  
Population Activity ◽  
Reconstruction Methods ◽  
Special Cases ◽  
Physical Variables ◽  
The Brain

Zhang, Kechen, Iris Ginzburg, Bruce L. McNaughton, and Terrence J. Sejnowski. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79: 1017–1044, 1998. Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multi-electrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.

scholarly journals Using Connectivity to Explain Autism

2019 ◽  
Author(s):  
Jack Adamek ◽  
Yu Luo ◽  
Joshua Ewen
Keyword(s):  
Scientific Explanation ◽  
Causal Explanation ◽  
Autism Spectrum ◽  
Imaging Method ◽  
Imaging Data ◽  
Reverse Engineer ◽  
Physiological Processes ◽  
The Mind ◽  
The Brain

The chapters in this Handbook reveal the breadth of brilliant imaging and analysis techniques designed to fulfill the mandate of cognitive neuroscience: to understand how anatomical structures and physiological processes in the brain cause typical and atypical behavior. Yet merely producing data from the latest imaging method is insufficient to truly achieve this goal. We also need a mental toolbox that contains methods of inference that allow us to derive true scientific explanation from these data. Causal inference is not easy in the human brain, where we are limited primarily to observational data and our methods of experimental perturbation in the service of causal explanation are limited. As a case study, we reverse engineer one of the most influential accounts of a neuropsychiatric disorder that is derived from observational imaging data: the connectivity theories of autism spectrum disorder (ASD). We take readers through an approach of first considering all possible causal paths that are allowed by preliminary imaging-behavioral correlations. By progressively sharpening the specificity of the measures and brain/behavioral constructs, we iteratively chip away at this space of allowable causal paths, like the sculptor chipping away the excess marble to reveal the statue. To assist in this process, we consider how current imaging methods that are lumped together under the rubric of “connectivity” may actually offer a differentiated set of connectivity constructs that can more specifically relate notions of information transmission in the mind to the physiology of the brain.

Dissociation of Spatial-, Object-, and Sound-Coding Neurons in the Mediodorsal Nucleus of the Primate Thalamus

2003 ◽  
Vol 89 (2) ◽  
pp. 1067-1077 ◽  
Author(s):  
Ikuo Tanibuchi ◽  
Patricia S. Goldman-Rakic
Keyword(s):  
Prefrontal Cortex ◽  
Dorsolateral Prefrontal Cortex ◽  
Single Neuron ◽  
Neuronal Responses ◽  
Mediodorsal Nucleus ◽  
Sound Coding ◽  
Spatial Tasks ◽  
Dorsolateral Prefrontal ◽  
Information Segregation ◽  
The Brain

The mediodorsal nucleus (MD) is the thalamic gateway to the prefrontal cortex, an area of the brain associated with spatial and object working memory functions. We have recorded single-neuron activities from the MD nucleus in monkeys trained to perform spatial tasks with peripheral visual stimuli and a nonspatial task with foveally presented pictures of objects and faces—tasks identical to those we have previously used to map regional specializations in the dorso- and ventro-lateral prefrontal cortex, respectively. We found that MD neurons exhibited categorical specificity—either responding selectively to locations in the spatial tasks or preferentially to specific representations of faces and objects in the nonspatial task. Spatially tuned neurons were located in parts of the MD connected with the dorsolateral prefrontal cortex while neurons responding to the identity of stimuli mainly occupied more ventral positions in the nucleus that has its connections with the inferior prefrontal convexity. Neuronal responses to auditory stimuli were also examined, and vocalization sensitive neurons were found in more posterior portions of the MD. We conclude that MD neurons are dissociable by their spatial and nonspatial coding properties in line with their cortical connections and that the principle of information segregation in cortico-cortical pathways extends to the “association” nuclei of the thalamus.

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 Decoding locomotion from population neural activity in moving C. elegans

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Kelsey M Hallinen ◽  
Ross Dempsey ◽  
Monika Scholz ◽  
Xinwei Yu ◽  
Ashley Linder ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Neural Activity ◽  
Single Neuron ◽  
Neural Representation ◽  
Correlation Structure ◽  
Experimental Measurements ◽  
Population Activity ◽  
Calcium Activity ◽  
C Elegans

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.

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.

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