scholarly journals Noise-Robust Modes of the Retinal Population Code Have the Geometry of “Ridges” and Correspond to Neuronal Communities

2017 ◽  
Vol 29 (12) ◽  
pp. 3119-3180 ◽  
Author(s):  
Adrianna Loback ◽  
Jason Prentice ◽  
Mark Ioffe ◽  
Michael Berry II
Keyword(s):  
Neural Activity ◽  
Ganglion Cells ◽  
Activity Patterns ◽  
Local Maximum ◽  
Spike Count ◽  
Neural Population ◽  
Local Maxima ◽  
Local Probability ◽  
Multiple Data Sets ◽  
Noise Robust

An appealing new principle for neural population codes is that correlations among neurons organize neural activity patterns into a discrete set of clusters, which can each be viewed as a noise-robust population codeword. Previous studies assumed that these codewords corresponded geometrically with local peaks in the probability landscape of neural population responses. Here, we analyze multiple data sets of the responses of approximately 150 retinal ganglion cells and show that local probability peaks are absent under broad, nonrepeated stimulus ensembles, which are characteristic of natural behavior. However, we find that neural activity still forms noise-robust clusters in this regime, albeit clusters with a different geometry. We start by defining a soft local maximum, which is a local probability maximum when constrained to a fixed spike count. Next, we show that soft local maxima are robustly present and can, moreover, be linked across different spike count levels in the probability landscape to form a ridge. We found that these ridges comprise combinations of spiking and silence in the neural population such that all of the spiking neurons are members of the same neuronal community, a notion from network theory. We argue that a neuronal community shares many of the properties of Donald Hebb's classic cell assembly and show that a simple, biologically plausible decoding algorithm can recognize the presence of a specific neuronal community.

scholarly journals Simultaneous silence organizes structured higher-order interactions in neural populations

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Hideaki Shimazaki ◽  
Kolia Sadeghi ◽  
Tomoe Ishikawa ◽  
Yuji Ikegaya ◽  
Taro Toyoizumi
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Hippocampal Slices ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Neural Population ◽  
Biological Mechanisms ◽  
Individual Activity ◽  
Pairwise Correlations ◽  
Neural Populations

Abstract Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.

scholarly journals New neural activity patterns emerge with long-term learning

2019 ◽  
Vol 116 (30) ◽  
pp. 15210-15215 ◽  
Author(s):  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Jay A. Hennig ◽  
Alan D. Degenhart ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Causal Link ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Neural Population Activity

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

scholarly journals Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

2018 ◽  
Author(s):  
Ming Bo Cai ◽  
Nicolas W. Schuck ◽  
Jonathan W. Pillow ◽  
Yael Niv
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Activity Patterns ◽  
Covariance Structure ◽  
Neural Population ◽  
Similarity Analysis ◽  
Imaging Data ◽  
Signal To Noise ◽  
Representational Similarity Analysis ◽  
Noise Ratio

AbstractThe activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degree of similarity between these neural activity patterns in response to different events is used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).Author summaryWe show the severity of the bias introduced when performing representational similarity analysis (RSA) based on neural activity pattern estimated within imaging runs. Our Bayesian RSA method significantly reduces the bias and can learn a shared representational structure across multiple participants. We also demonstrate its extension as a new multi-class decoding tool.

scholarly journals Constraints on neural redundancy

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Jay A Hennig ◽  
Matthew D Golub ◽  
Peter J Lund ◽  
Patrick T Sadtler ◽  
Emily R Oby ◽  
...  
Keyword(s):  
Neural Activity ◽  
Rhesus Monkeys ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Computer Interface ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Selection Of ◽  
And Task

Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.

scholarly journals Compulsive drinking is associated with neural activity patterns reflecting diminished behavioral control and enhanced seeking representations in dorsal medial prefrontal cortex

2021 ◽  
Author(s):  
Nicholas Timme ◽  
Baofeng Ma ◽  
David N. Linsenbardt ◽  
Ethan Cornwell ◽  
Taylor Galbari ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Neural Activity ◽  
Behavioral Control ◽  
Critical Phenomenon ◽  
Activity Patterns ◽  
Principal Component ◽  
Neural Population ◽  
Negative Consequences

Drinking despite negative consequences (compulsive drinking) is a central contributor to high-risk alcohol intake and is associated with poor treatment outcomes in humans. We used a rodent model of compulsive drinking to examine the role played by dorsal medial prefrontal cortex (dmPFC), a brain region involved in maladaptive decision-making in addiction, in this clinically critical phenomenon. We developed novel advances in principal component and change point analyses to dissect neural population representations of specific decision-making variables. Compulsive subjects showed weakened representations of behavioral control signals that relate to drinking within a trial, but strengthened session-wide seeking state representations that were associated with drinking engagement at the start of each drinking opportunity. Finally, chemogenetic-based excitation of dmPFC prevented escalation of compulsive drinking. Collectively, these data indicate that compulsive drinking is associated with alterations in dmPFC neural activity that underlie diminished behavioral control and enhanced seeking.

Energy Harvesting From the Vibrations of Rotating Systems

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Christopher G. Cooley ◽  
Tan Chai
Keyword(s):  
Rotation Speed ◽  
Excitation Frequency ◽  
Average Power ◽  
Local Maximum ◽  
Operating Conditions ◽  
Centripetal Acceleration ◽  
Local Maxima ◽  
Closed Form Solutions ◽  
Energy Harvesters ◽  
Wide Range

This study investigates the vibration of and power harvested by typical electromagnetic and piezoelectric vibration energy harvesters when applied to vibrating host systems that rotate at constant speed. The governing equations for these electromechanically coupled devices are derived using Newtonian mechanics and Kirchhoff's voltage law. The natural frequency for these devices is speed-dependent due to the centripetal acceleration from their constant rotation. Resonance diagrams are used to identify excitation frequencies and speeds where these energy harvesters have large amplitude vibration and power harvested. Closed-form solutions are derived for the steady-state response and power harvested. These devices have multifrequency dynamic response due to the combined vibration and rotation of the host system. Multiple resonances are possible. The average power harvested over one oscillation cycle is calculated for a wide range of operating conditions. Electromagnetic devices have a local maximum in average harvested power that occurs near a specific excitation frequency and rotation speed. Piezoelectric devices, depending on their mechanical damping, can have two local maxima of average power harvested. Although these maxima are sensitive to small changes in the excitation frequency, they are much less sensitive to small changes in rotation speed.

scholarly journals Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yunfa Fu ◽  
Jian Chen ◽  
Xin Xiong
Keyword(s):  
Grip Force ◽  
Activity Patterns ◽  
Hand Movement ◽  
Local Maximum ◽  
Topographic Map ◽  
Mu Rhythm ◽  
Global Field Power ◽  
Frequency Bands ◽  
Microstate Analysis ◽  
Imagined Movement

Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

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