scholarly journals Multiscale representations of community structures in attractor neural networks

2021 ◽  
Vol 17 (8) ◽  
pp. e1009296
Author(s):  
Tatsuya Haga ◽  
Tomoki Fukai
Keyword(s):  
Multiple Scales ◽  
Activity Patterns ◽  
Graph Laplacian ◽  
Inhibitory Circuits ◽  
Attractor Dynamics ◽  
Plausible Mechanism ◽  
Hippocampal Theta ◽  
Transition Graphs ◽  
Hierarchical Computation ◽  
The Brain

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

scholarly journals Associative memory networks for graph-based abstraction

2020 ◽  
Author(s):  
Tatsuya Haga ◽  
Tomoki Fukai
Keyword(s):  
Associative Memory ◽  
Multiple Scales ◽  
Graph Laplacian ◽  
Inhibitory Circuits ◽  
Attractor Dynamics ◽  
Plausible Mechanism ◽  
Hippocampal Theta ◽  
Transition Graphs ◽  
Hierarchical Computation ◽  
The Brain

AbstractOur cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are only poorly understood. In this paper, we propose an extended attractor network model for the graph-based hierarchical computation, called as Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model with asymmetricity exhibits chunking resembling to hippocampal theta sequences. Our model connects graph theory and the attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

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 Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

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.

scholarly journals Can a comparison of clinical and deep space irradiation scenarios shed light on the radiation response of the brain?

2020 ◽  
Vol 93 (1115) ◽  
pp. 20200245 ◽  
Author(s):  
Charles Limoli
Keyword(s):  
Occupational Exposures ◽  
Functional Change ◽  
Radiation Environment ◽  
Neurocognitive Deficits ◽  
Deep Space ◽  
Environmental Radiation ◽  
Cellular Targets ◽  
Inhibitory Circuits ◽  
The Impact ◽  
The Brain

Not surprisingly, our knowledge of the impact of radiation on the brain has evolved considerably. Decades of work have struggled with identifying the critical cellular targets in the brain, the latency of functional change and understanding how irradiation alters the balance between excitatory and inhibitory circuits. Radiation-induced cell kill following clinical fractionation paradigms pointed to both stromal and parenchymal targets but also defined an exquisite sensitivity of neurogenic populations of newly born cells in the brain. It became more and more apparent too, that acute (days) events transpiring after exposure were poorly prognostic of the late (months-years) waves of radiation injury believed to underlie neurocognitive deficits. Much of these gaps in knowledge persisted as NASA became interested in how exposure to much different radiation types, doses and dose rates that characterize the space radiation environment might impair central nervous system functionality, with possibly negative implications for deep space travel. Now emerging evidence from researchers engaged in clinical, translational and environmental radiation sciences have begun to fill these gaps and have uncovered some surprising similarities in the response of the brain to seemingly disparate exposure scenarios. This article highlights many of the commonalities between the vastly different irradiation paradigms that distinguish clinical treatments from occupational exposures in deep space.

scholarly journals On the methods for reactivation and replay analysis

2020 ◽  
Vol 375 (1799) ◽  
pp. 20190231 ◽  
Author(s):  
David Tingley ◽  
Adrien Peyrache
Keyword(s):  
Neural Activity ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Large Body ◽  
Neural Data ◽  
History Of ◽  
Methodological Approaches ◽  
Neuronal Patterns ◽  
External Stimuli ◽  
The Brain

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation , and those that quantify the temporal structure of changing activity patterns, termed replay . However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

scholarly journals Stimulus-specific information is represented as local activity patterns across the brain

NeuroImage ◽  
2020 ◽  
Vol 223 ◽  
pp. 117326 ◽  
Author(s):  
Amirouche Sadoun ◽  
Tushar Chauhan ◽  
Samir Mameri ◽  
Yi  Fan Zhang ◽  
Pascal Barone ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Specific Information ◽  
Local Activity ◽  
The Brain

scholarly journals Alternating sequences of future and past behavior encoded within hippocampal theta oscillations

Science ◽  
2020 ◽  
Vol 370 (6513) ◽  
pp. 247-250 ◽  
Author(s):  
Mengni Wang ◽  
David J. Foster ◽  
Brad E. Pfeiffer
Keyword(s):  
Activity Patterns ◽  
Place Cells ◽  
Theta Oscillations ◽  
Retrospective Evaluation ◽  
Past Behavior ◽  
Hippocampal Ca1 ◽  
Ongoing Behavior ◽  
Theta Phase ◽  
Hippocampal Theta ◽  
And Storage

Neural networks display the ability to transform forward-ordered activity patterns into reverse-ordered, retrospective sequences. The mechanisms underlying this transformation remain unknown. We discovered that, during active navigation, rat hippocampal CA1 place cell ensembles are inherently organized to produce independent forward- and reverse-ordered sequences within individual theta oscillations. This finding may provide a circuit-level basis for retrospective evaluation and storage during ongoing behavior. Theta phase procession arose in a minority of place cells, many of which displayed two preferred firing phases in theta oscillations and preferentially participated in reverse replay during subsequent rest. These findings reveal an unexpected aspect of theta-based hippocampal encoding and provide a biological mechanism to support the expression of reverse-ordered sequences.

scholarly journals Sex Differences in the Epigenome: A Cause or Consequence of Sexual Differentiation of the Brain?

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 432 ◽  
Author(s):  
Bruno Gegenhuber ◽  
Jessica Tollkuhn
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Sexual Differentiation ◽  
Hormone Receptors ◽  
Neurological Diseases ◽  
Activity Patterns ◽  
Regulatory Elements ◽  
The Body ◽  
Epigenetic Mechanism ◽  
The Brain

Females and males display differences in neural activity patterns, behavioral responses, and incidence of psychiatric and neurological diseases. Sex differences in the brain appear throughout the animal kingdom and are largely a consequence of the physiological requirements necessary for the distinct roles of the two sexes in reproduction. As with the rest of the body, gonadal steroid hormones act to specify and regulate many of these differences. It is thought that transient hormonal signaling during brain development gives rise to persistent sex differences in gene expression via an epigenetic mechanism, leading to divergent neurodevelopmental trajectories that may underlie sex differences in disease susceptibility. However, few genes with a persistent sex difference in expression have been identified, and only a handful of studies have employed genome-wide approaches to assess sex differences in epigenomic modifications. To date, there are no confirmed examples of gene regulatory elements that direct sex differences in gene expression in the brain. Here, we review foundational studies in this field, describe transcriptional mechanisms that could act downstream of hormone receptors in the brain, and suggest future approaches for identification and validation of sex-typical gene programs. We propose that sexual differentiation of the brain involves self-perpetuating transcriptional states that canalize sex-specific development.

scholarly journals Geometric renormalization unravels self-similarity of the multiscale human connectome

2020 ◽  
Vol 117 (33) ◽  
pp. 20244-20253 ◽  
Author(s):  
Muhua Zheng ◽  
Antoine Allard ◽  
Patric Hagmann ◽  
Yasser Alemán-Gómez ◽  
M. Ángeles Serrano
Keyword(s):  
Healthy Subjects ◽  
Multiple Scales ◽  
Structural Connectivity ◽  
Coarse Graining ◽  
Brain Regions ◽  
Self Similarity ◽  
Digital Reconstruction ◽  
Self Similar ◽  
The Brain ◽  
Organizing Principles

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.

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