scholarly journals From serial to parallel: predicting synchronous firing of large neural populations from sequential recordings

2019 ◽  
Author(s):  
Oleksandr Sorochynskyi ◽  
Stéphane Deny ◽  
Olivier Marre ◽  
Ulisse Ferrari
Keyword(s):  
Ganglion Cells ◽  
Single Cells ◽  
Relevant Information ◽  
Neuronal Population ◽  
Synchronous Activity ◽  
Population Activity ◽  
Synchronous Firing ◽  
Large Populations ◽  
Single Cell Responses ◽  
Fast Pace

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress has made possible to determine the type of each recorded neuron in a given area thanks to genetic and physiological tools. However, it is unclear how to infer the activity of a full population of neurons of the same type from sequential recordings across different experiments. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just pool together the spike trains from sequential recordings. Here we present a method to build population activity from single cell responses taken from sequential recordings, which only requires pairwise recordings to train the model. Our method combines copula distributions and maximum entropy modeling. After training, the model allows us to predict the activity of large populations using only sequential recordings of single cells. We applied this method to a population of ganglion cells, the retinal output, all belonging to the same type. From just the spiking response of each cell to a repeated stimulus, we could predict the full activity of the population. We could then generalize to predict the population responses to different stimuli and even to different experiments. As a result, we were able to use our approach to construct a synthetic model of a very large neuronal population, which uses data combined from multiple experiments. We then predicted the extent of synchronous activity and showed it grew with the number of neurons. This approach is a promising way to infer population activity from sequential recordings in sensory areas.

scholarly journals Predicting synchronous firing of large neural populations from sequential recordings

2021 ◽  
Vol 17 (1) ◽  
pp. e1008501
Author(s):  
Oleksandr Sorochynskyi ◽  
Stéphane Deny ◽  
Olivier Marre ◽  
Ulisse Ferrari
Keyword(s):  
Ganglion Cell ◽  
Maximum Entropy ◽  
Ganglion Cells ◽  
Large Population ◽  
Relevant Information ◽  
Synchronous Activity ◽  
Maximum Entropy Modeling ◽  
Synchronous Firing ◽  
Fast Pace ◽  
Full Population

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells’ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible.

scholarly journals Task rule and choice are reflected by layer-specific processing in rodent auditory cortical microcircuits

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Marina M. Zempeltzi ◽  
Martin Kisse ◽  
Michael G. K. Brunk ◽  
Claudia Glemser ◽  
Sümeyra Aksit ◽  
...  
Keyword(s):  
Decision Making ◽  
Current Source ◽  
Primary Auditory Cortex ◽  
Relevant Information ◽  
Neuronal Population ◽  
Meriones Unguiculatus ◽  
Mongolian Gerbils ◽  
Population Activity ◽  
Mixed Effect ◽  
Task Rule

AbstractThe primary auditory cortex (A1) is an essential, integrative node that encodes the behavioral relevance of acoustic stimuli, predictions, and auditory-guided decision-making. However, the realization of this integration with respect to the cortical microcircuitry is not well understood. Here, we characterize layer-specific, spatiotemporal synaptic population activity with chronic, laminar current source density analysis in Mongolian gerbils (Meriones unguiculatus) trained in an auditory decision-making Go/NoGo shuttle-box task. We demonstrate that not only sensory but also task- and choice-related information is represented in the mesoscopic neuronal population code of A1. Based on generalized linear-mixed effect models we found a layer-specific and multiplexed representation of the task rule, action selection, and the animal’s behavioral options as accumulating evidence in preparation of correct choices. The findings expand our understanding of how individual layers contribute to the integrative circuit in the sensory cortex in order to code task-relevant information and guide sensory-based decision-making.

scholarly journals Task Engagement Enhances Population Encoding of Stimulus Meaning in Primary Auditory Cortex

2017 ◽  
Author(s):  
Sophie Bagur ◽  
Martin Averseng ◽  
Diego Elgueda ◽  
Stephen David ◽  
Jonathan Fritz ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Activity Patterns ◽  
Primary Auditory Cortex ◽  
Stimulus Presentation ◽  
Population Level ◽  
Motor Command ◽  
Relevant Information ◽  
Neuronal Population ◽  
Task Engagement ◽  
Population Activity

AbstractThe main functions of primary sensory cortical areas are classically considered to be the extraction and representation of stimulus features. In contrast, higher cortical sensory association areas are thought to be responsible for combining these sensory representations with internal motivations and learnt associations. These regions generate appropriate neural responses that are maintained until a motor command is executed. Within this framework, responses of the primary sensory areas during task performance are expected to carry less information about the behavioral meaning of the stimulus than higher sensory, association, motor and frontal cortices. Here we demonstrate instead that the neuronal population responses in the early primary auditory cortex (A1) display many aspects of responses generally associated with higher-level areas. A1 activity was recorded in awake ferrets while they were either passively listening or actively discriminating two periodic click trains of different rates in a Go/No-Go paradigm. By applying population-level dimensionality reduction techniques, we found that task-engagement induced a shift in the nature of the encoding from a sensory-driven representation of the two stimuli to a behaviorally relevant representation of the two categories that specifically enhances the target stimulus. We demonstrate that this shift in encoding relies partly on a novel mechanism of change in spontaneous activity patterns upon engagement in the task. We show that this population-level representation of stimuli in A1 population activity bears strong similarities to responses in the frontal cortex, but appears earlier following stimulus presentation. Analysis of neural activity recorded in various Go/No-Go tasks, with different sounds and reinforcement paradigms, reveals that this striking population-level enhancement of target representation is a general property of task engagement. These findings indicate that primary sensory cortices play a highly flexible role in the processing of incoming stimuli and implement a crucial change in the structure of population activity in order to extract task-relevant information during behavior.

Correlated Firing Improves Stimulus Discrimination in a Retinal Model

2004 ◽  
Vol 16 (11) ◽  
pp. 2261-2291 ◽  
Author(s):  
Garrett T. Kenyon ◽  
James Theiler ◽  
John S. George ◽  
Bryan J. Travis ◽  
David W. Marshak
Keyword(s):  
Stimulus Intensity ◽  
Ganglion Cells ◽  
Fano Factor ◽  
Spike Trains ◽  
Dependent Manner ◽  
Common Input ◽  
Threshold Detector ◽  
Synchronous Firing ◽  
Inhibitory Feedback ◽  
Synchronous Oscillations

Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.

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 Optimization of sensory stimulation for neuronal population activity

2010 ◽  
Vol 11 (S1) ◽  
Author(s):  
Noelia Montejo ◽  
Jean-Luc Blanc ◽  
Yann Mahnoun ◽  
Jean-Michel Brezun ◽  
Nicolas Catz ◽  
...  
Keyword(s):  
Sensory Stimulation ◽  
Neuronal Population ◽  
Population Activity

scholarly journals The Fine Structure of Synapses in the Ciliary Ganglion of the Chick

1960 ◽  
Vol 7 (1) ◽  
pp. 31-36 ◽  
Author(s):  
A. J. de Lorenzo
Keyword(s):  
Fine Structure ◽  
Synaptic Vesicles ◽  
Ganglion Cells ◽  
Single Cells ◽  
Synaptic Cleft ◽  
Structural Features ◽  
Ciliary Ganglion ◽  
Single Axon ◽  
Electron Microscopes ◽  
Electron Microscope Image

Ciliary ganglia of chick embryos and newly hatched chicks were examined in the light and electron microscopes. Particular attention was given to the fine structure of calyciform synapses, which are characteristically found in ciliary ganglia of birds. The calyciform endings are characterized by large expansions of the presynaptic axons upon ganglion cells, and the terminal processes extend over a considerable area of the cell surface. Often, indeed they appear to envelop the cell. In the electron microscope image, the appositional membranes are separated by a space about 300 to 400 A wide; i.e., the synaptic cleft. At irregularly spaced regions, the appositional membranes show areas of increased density. The presynaptic processes contain clusters of synaptic vesicles, localized at these dense regions. Thus the fine structure complex typical of other synapses is evident. The unique structural features of this synapse are as follows: (a) The calyx or presynaptic terminal derives from a single axon, does not arborize, and terminates upon a single ganglion cell. Thus, unlike the classical bouton terminal, this represents an anatomical device for firing single cells by single axons. (b) The surface area in contiguity, i.e., the area of appositional membranes, is far more extensive than the bouton terminal. The fine structure of this synapse is compared with others, for example, the classical boutons terminaux and purely electrical synapses, in an attempt to correlate fine structure with function.

scholarly journals Blindfold learning of an accurate neural metric

2018 ◽  
Vol 115 (13) ◽  
pp. 3267-3272 ◽  
Author(s):  
Christophe Gardella ◽  
Olivier Marre ◽  
Thierry Mora
Keyword(s):  
Ganglion Cells ◽  
Direct Access ◽  
Retinal Ganglion ◽  
Boltzmann Machine ◽  
Correlated Responses ◽  
Population Activity ◽  
Spatiotemporal Structure ◽  
Distance Map ◽  
Physical Stimuli ◽  
The Brain

The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli.

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