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 Robust odor coding across states in piriform cortex requires recurrent circuitry: evidence for pattern completion in an associative network

2019 ◽  
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 ◽  
Network Models ◽  
Theoretical Studies ◽  
Odor Coding ◽  
Pattern Completion ◽  
Attractor Dynamics ◽  
Cortical Circuit

AbstractPattern 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 network models can perform this process. Although phenomenological evidence for pattern completion and attractor dynamics have been described in various recurrent neural circuits, the crucial role that recurrent circuitry plays in implementing these processes has not been shown. Here we show that although odor representations in mouse olfactory bulb degrade under anesthesia, responses in downstream 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. Thus, an auto-associative cortical circuit stabilizes output in response to degraded input, and the recurrent circuitry that defines these networks is required for this stabilization.

scholarly journals Modelling the impact of structural directionality on connectome-based models of neural activity

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Amelia Padmore ◽  
Martin R Nelson ◽  
Nadia Chuzhanova ◽  
Jonathan J Crofts
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Directed Network ◽  
Fixed Point Attractor ◽  
Important Challenge ◽  
Brain States ◽  
Chemical Synapses ◽  
The Impact ◽  
The Brain

Abstract Understanding structure--function relationships in the brain remains an important challenge in neuroscience. However, whilst structural brain networks are intrinsically directed, due to the prevalence of chemical synapses in the cortex, most studies in network neuroscience represent the brain as an undirected network. Here, we explore the role that directionality plays in shaping transition dynamics of functional brain states. Using a system of Hopfield neural elements with heterogeneous structural connectivity given by different species and parcellations (cat, Caenorhabditis elegans and two macaque networks), we investigate the effect of removing directionality of connections on brain capacity, which we quantify via its ability to store attractor states. In addition to determining large numbers of fixed-point attractor sets, we deploy the recently developed basin stability technique in order to assess the global stability of such brain states, which can be considered a proxy for network state robustness. Our study indicates that not only can directed network topology have a significant effect on the information capacity of connectome-based networks, but it can also impact significantly the domains of attraction of the aforementioned brain states. In particular, we find network modularity to be a key mechanism underlying the formation of neural activity patterns, and moreover, our results suggest that neglecting network directionality has the scope to eliminate states that correlate highly with the directed modular structure of the brain. A numerical analysis of the distribution of attractor states identified a small set of prototypical direction-dependent activity patterns that potentially constitute a `skeleton' of the non-stationary dynamics typically observed in the brain. This study thereby emphasizes the substantial role network directionality can have in shaping the brain's ability to both store and process information.

scholarly journals Rat Prefrontal Cortex Inactivations during Decision Making Are Explained by Bistable Attractor Dynamics

2017 ◽  
Vol 29 (11) ◽  
pp. 2861-2886 ◽  
Author(s):  
Alex T. Piet ◽  
Jeffrey C. Erlich ◽  
Charles D. Kopec ◽  
Carlos D. Brody
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Memory Model ◽  
Network Configuration ◽  
Perceptual Decision Making ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Flexible Models ◽  
Evidence Integration

Two-node attractor networks are flexible models for neural activity during decision making. Depending on the network configuration, these networks can model distinct aspects of decisions including evidence integration, evidence categorization, and decision memory. Here, we use attractor networks to model recent causal perturbations of the frontal orienting fields (FOF) in rat cortex during a perceptual decision-making task (Erlich, Brunton, Duan, Hanks, & Brody, 2015 ). We focus on a striking feature of the perturbation results. Pharmacological silencing of the FOF resulted in a stimulus-independent bias. We fit several models to test whether integration, categorization, or decision memory could account for this bias and found that only the memory configuration successfully accounts for it. This memory model naturally accounts for optogenetic perturbations of FOF in the same task and correctly predicts a memory-duration-dependent deficit caused by silencing FOF in a different task. Our results provide mechanistic support for a “postcategorization” memory role of the FOF in upcoming choices.

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 Relationship between individual neuron and network spontaneous activity in developing mouse cortex

2014 ◽  
Vol 112 (12) ◽  
pp. 3033-3045 ◽  
Author(s):  
Heather M. Barnett ◽  
Julijana Gjorgjieva ◽  
Keiko Weir ◽  
Cara Comfort ◽  
Adrienne L. Fairhall ◽  
...  
Keyword(s):  
Normal Development ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Intrinsic Excitability ◽  
Intrinsic Activity ◽  
Dorsal Cortex ◽  
Synchronous Activity ◽  
Mouse Cortex ◽  
Propagating Waves ◽  
Autonomous Activity

Spontaneous synchronous activity (SSA) that propagates as electrical waves is found in numerous central nervous system structures and is critical for normal development, but the mechanisms of generation of such activity are not clear. In previous work, we showed that the ventrolateral piriform cortex is uniquely able to initiate SSA in contrast to the dorsal neocortex, which participates in, but does not initiate, SSA (Lischalk JW, Easton CR, Moody WJ. Dev Neurobiol 69: 407–414, 2009). In this study, we used Ca2+ imaging of cultured embryonic day 18 to postnatal day 2 coronal slices (embryonic day 17 + 1–4 days in culture) of the mouse cortex to investigate the different activity patterns of individual neurons in these regions. In the piriform cortex where SSA is initiated, a higher proportion of neurons was active asynchronously between waves, and a larger number of groups of coactive cells was present compared with the dorsal cortex. When we applied GABA and glutamate synaptic antagonists, asynchronous activity and cellular clusters remained, while synchronous activity was eliminated, indicating that asynchronous activity is a result of cell-intrinsic properties that differ between these regions. To test the hypothesis that higher levels of cell-autonomous activity in the piriform cortex underlie its ability to initiate waves, we constructed a conductance-based network model in which three layers differed only in the proportion of neurons able to intrinsically generate bursting behavior. Simulations using this model demonstrated that a gradient of intrinsic excitability was sufficient to produce directionally propagating waves that replicated key experimental features, indicating that the higher level of cell-intrinsic activity in the piriform cortex may provide a substrate for SSA generation.

scholarly journals Multiple states in ongoing neural activity in the rat visual cortex

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256791
Author(s):  
Daichi Konno ◽  
Shinji Nishimoto ◽  
Takafumi Suzuki ◽  
Yuji Ikegaya ◽  
Nobuyoshi Matsumoto
Keyword(s):  
Visual Cortex ◽  
Neural Activity ◽  
Activity Patterns ◽  
Electrode Array ◽  
Nonlinear Dimensionality Reduction ◽  
Neural Responses ◽  
Spontaneous Neural Activity ◽  
Custom Made ◽  
Visual Cortices ◽  
Freely Behaving

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

scholarly journals Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

2020 ◽  
Author(s):  
KW Scangos ◽  
AN Khambhati ◽  
PM Daly ◽  
LW Owen ◽  
JR Manning ◽  
...  
Keyword(s):  
Neural Activity ◽  
Functional Organization ◽  
Activity Patterns ◽  
Computational Approach ◽  
Beta Activity ◽  
Targeted Intervention ◽  
Left Temporal Lobe ◽  
Spatiotemporal Resolution ◽  
Neural Recordings ◽  
Biological Substrates

AbstractQuantitative biological substrates of depression remain elusive. We carried out this study to determine whether application of a novel computational approach to high spatiotemporal resolution direct neural recordings may unlock the functional organization and coordinated activity patterns of depression networks. We identified two subnetworks conserved across the majority of individuals studied. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings identify distributed circuit activity associated with depression, link neural activity with functional connectivity profiles, and inform strategies for personalized targeted intervention.

Decoding the sense of smell: understanding the structural organization within the brain

2020 ◽  
Vol 2 (7) ◽  
pp. 4-9
Author(s):  
Shripriya Singh
Keyword(s):  
Neural Activity ◽  
Structural Organization ◽  
Piriform Cortex ◽  
Molecular Structures ◽  
The Other ◽  
Multiphoton Imaging ◽  
Sensory Inputs ◽  
Correlated Patterns ◽  
Olfactory Sense ◽  
The Brain

The olfactory sense is a potent sensory tool which helps us perceive our environment much better. However, smells despite being similar have different impacts on individuals. What makes one odor categorically different from the other and why do people have a unique and personalized experience with smell is an answer that needs to be addressed. In the present article we have discussed the research in which neuroscientists have decoded and described how the relationships between different odors are encoded in the brain. How the brain transforms information about odor chemistry into the perception of smell is a major highlight of this publication. Carefully selected odors with defined molecular structures were delivered in mice and the neural activity was analyzed. It was observed that neuronal representations of smell in the cortex reflected chemical similarities between odors, thus allowing the brain to categorize scents. The study has employed chemo informatics and multiphoton imaging in the mouse to demonstrate both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odor relationships through correlated patterns of activity. The research has given us cues in the direction of how the brain translates odor chemistry into neurochemistry and eventually perception of smell.

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