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 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 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 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 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.

scholarly journals Hippocampal theta oscillations support successful associative memory formation

2020 ◽  
Author(s):  
Srinivas Kota ◽  
Michael D. Rugg ◽  
Bradley C. Lega
Keyword(s):  
Associative Memory ◽  
Memory Formation ◽  
Associative Recognition ◽  
Theta Oscillations ◽  
Memory Encoding ◽  
Phase Reset ◽  
Intracranial Eeg ◽  
Electrophysiological Evidence ◽  
Hippocampal Theta ◽  
Encoding And Retrieval

1.AbstractModels of memory formation posit that recollection as compared to familiarity-based memory depends critically on the hippocampus, which binds features of an event to its context. For this reason, the contrast between study items that are later recollected versus those that are recognized on the basis of familiarity should reveal electrophysiological patterns in the hippocampus selectively involved in associative memory encoding. Extensive data from studies in rodents support a model in which theta oscillations fulfill this role, but results in humans results have not been as clear. Here, we employed an associative recognition memory procedure to identify hippocampal correlates of successful associative memory encoding and retrieval in patients undergoing intracranial EEG monitoring. We identified a dissociation between 2– 5 Hz and 5–9 Hz theta oscillations, by which 2–5 Hz oscillations uniquely were linked with successful associative memory in both the anterior and posterior hippocampus. These oscillations exhibited a significant phase reset that also predicted successful associative encoding, distinguished recollected from familiar items at retrieval, and contributed to reinstatement of encoding-related patterns that distinguished these items. Our results provide direct electrophysiological evidence that 2–5 Hz hippocampal theta oscillations support the encoding and retrieval of memories based on recollection but not familiarity.2.Significance StatementExtensive fMRI evidence suggests that the hippocampus plays a selective role in recollection rather than familiarity, during both encoding and retrieval. However, there is little or no electrophysiological evidence that speaks to whether the hippocampus is selectively involved in recollection. Here, we used intracranial EEG from human participants engaged in an associative recognition paradigm. The findings suggest that oscillatory power and phase reset in the hippocampus are selectively associated with recollection rather than familiarity-based memory judgements. Furthermore, reinstatement of oscillatory patterns in the hippocampus was stronger for successful recollection than familiarity. Collectively, the findings support a role for hippocampal theta oscillations in human episodic memory.

scholarly journals Multi-functional feedforward inhibitory circuits for cortical odor processing

2021 ◽  
Author(s):  
Norimitsu Suzuki ◽  
Malinda L. S. Tantirigama ◽  
Helena H.-Y. Huang ◽  
John M. Bekkers
Keyword(s):  
Calcium Imaging ◽  
Piriform Cortex ◽  
Complex Interplay ◽  
Inhibitory Circuits ◽  
Two Photon ◽  
Odor Processing ◽  
Strong Excitation ◽  
The Brain ◽  
Feedforward Inhibition

Feedforward inhibitory circuits are key contributors to the complex interplay between excitation and inhibition in the brain. Little is known about the function of feedforward inhibition in the primary olfactory (piriform) cortex. Using in vivo two-photon targeted patch clamping and calcium imaging in mice, we find that odors evoke strong excitation in two classes of interneurons – neurogliaform (NG) cells and horizontal (HZ) cells – that provide feedforward inhibition in layer 1 of the piriform cortex. NG cells fire much earlier than HZ cells following odor onset, a difference that can be attributed to the faster odor-driven excitatory synaptic drive that NG cells receive from the olfactory bulb. As a consequence, NG cells strongly but transiently inhibit odor-evoked excitation in layer 2 principal cells, whereas HZ cells provide more diffuse and prolonged feedforward inhibition. Our findings reveal unexpected complexity in the operation of inhibition in the piriform cortex.

scholarly journals Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

2020 ◽  
Author(s):  
Carlos Coronel-Oliveros ◽  
Rodrigo Cofré ◽  
Patricio Orio
Keyword(s):  
Computational Models ◽  
Functional Organization ◽  
Brain Activity ◽  
Functional Integration ◽  
Whole Brain ◽  
Inhibitory Circuits ◽  
Proposed Model ◽  
Inhibitory Feedback ◽  
Noradrenergic Modulation ◽  
The Brain

AbstractSegregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although computational models have reproduced the effect of neuromodulation at the whole-brain level, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, a newly introduced local inhibitory feedback enables the integration of whole-brain activity, and its modulation interacts with the other neuromodulatory influences to facilitate the transit between different functional states. Moreover, the new proposed model is able to reproduce an inverted-U relationship between noradrenergic modulation and network integration. Our work proposes a new possible mechanism behind segregation and integration in the brain.

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