scholarly journals A neural circuit for flexible control of persistent behavioral states

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ni Ji ◽  
Gurrein K Madan ◽  
Guadalupe I Fabre ◽  
Alyssa Dayan ◽  
Casey M Baker ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Circuit ◽  
Activity Patterns ◽  
State Transitions ◽  
Flexible Control ◽  
C Elegans ◽  
Food Odors ◽  
Long Time ◽  
Behavioral States ◽  
Learning Analysis

To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

scholarly journals A neural circuit for flexible control of persistent behavioral states

2020 ◽  
Author(s):  
Ni Ji ◽  
Gurrein K. Madan ◽  
Guadalupe I. Fabre ◽  
Alyssa Dayan ◽  
Casey M. Baker ◽  
...  
Keyword(s):  
Neural Circuit ◽  
Activity Patterns ◽  
State Transitions ◽  
Flexible Control ◽  
C Elegans ◽  
Food Odors ◽  
Long Time ◽  
Behavioral States ◽  
Network States ◽  
Learning Analysis

ABSTRACTTo adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stable, circuit-wide activity patterns corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the opposing network states. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

scholarly journals Dimensionality in recurrent spiking networks: global trends in activity and local origins in connectivity

2018 ◽  
Author(s):  
Stefano Recanatesi ◽  
Gabriel Koch Ocker ◽  
Michael A. Buice ◽  
Eric Shea-Brown
Keyword(s):  
High Dimension ◽  
Neural Activity ◽  
Degrees Of Freedom ◽  
Neural Circuit ◽  
Activity Patterns ◽  
Global Trends ◽  
Multineuron Activity ◽  
Low Dimensional ◽  
Compact Measure ◽  
Strong Control

AbstractThe dimensionality of a network’s collective activity is of increasing interest in neuroscience. This is because dimensionality provides a compact measure of how coordinated network-wide activity is, in terms of the number of modes (or degrees of freedom) that it can independently explore. A low number of modes suggests a compressed low dimensional neural code and reveals interpretable dynamics [1], while findings of high dimension may suggest flexible computations [2, 3]. Here, we address the fundamental question of how dimensionality is related to connectivity, in both autonomous and stimulus-driven networks. Working with a simple spiking network model, we derive three main findings. First, the dimensionality of global activity patterns can be strongly, and systematically, regulated by local connectivity structures. Second, the dimensionality is a better indicator than average correlations in determining how constrained neural activity is. Third, stimulus evoked neural activity interacts systematically with neural connectivity patterns, leading to network responses of either greater or lesser dimensionality than the stimulus.Author summaryNew recording technologies are producing an amazing explosion of data on neural activity. These data reveal the simultaneous activity of hundreds or even thousands of neurons. In principle, the activity of these neurons could explore a vast space of possible patterns. This is what is meant by high-dimensional activity: the number of degrees of freedom (or “modes”) of multineuron activity is large, perhaps as large as the number of neurons themselves. In practice, estimates of dimensionality differ strongly from case to case, and do so in interesting ways across experiments, species, and brain areas. The outcome is important for much more than just accurately describing neural activity: findings of low dimension have been proposed to allow data compression, denoising, and easily readable neural codes, while findings of high dimension have been proposed as signatures of powerful and general computations. So what is it about a neural circuit that leads to one case or the other? Here, we derive a set of principles that inform how the connectivity of a spiking neural network determines the dimensionality of the activity that it produces. These show that, in some cases, highly localized features of connectivity have strong control over a network’s global dimensionality—an interesting finding in the context of, e.g., learning rules that occur locally. We also show how dimension can be much different than first meets the eye with typical “pairwise” measurements, and how stimuli and intrinsic connectivity interact in shaping the overall dimension of a network’s response.

scholarly journals Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction

2021 ◽  
Author(s):  
Nikolai M. Chapochnikov ◽  
Cengiz Pehlevan ◽  
Dmitri B. Chklovskii
Keyword(s):  
Neural Activity ◽  
Neural Circuit ◽  
Mechanistic Model ◽  
Activity Patterns ◽  
Synaptic Organization ◽  
Major Question ◽  
Activity Data ◽  
Normative Framework ◽  
Combine Structure ◽  
Stimulus Features

AbstractOne major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we propose a biologically plausible mechanistic model of the circuit. Our model predicts the ORN → LN synaptic weights found in the connectome and demonstrate that they reflect correlations in ORN activity patterns. Additionally, our model explains the relation between ORN → LN and LN – LN synaptic weight and the arising of different LN types. This global synaptic organization can autonomously arise through Hebbian plasticity, and thus allows the circuit to adapt to different environments in an unsupervised manner. Functionally, we propose LNs extract redundant input correlations and dampen them in ORNs, thus partially whitening and normalizing the stimulus representations in ORNs. Our work proposes a comprehensive framework to combine structure, activity, function, and learning, and uncovers a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient.SignificanceThe brain represents information with patterns of neural activity. At the periphery, due to the properties of the external world and of encoding neurons, these patterns contain correlations, which are detrimental for stimulus discrimination. We study the peripheral olfactory neural circuit of the Drosophila larva, that preprocesses neural representations before relaying them to higher brain areas. A comprehensive understanding of this preprocessing is, however, lacking. Here, we propose a mechanistic and normative framework describing the function of the circuit and predict the circuit’s synaptic organization based on the circuit’s input neural activity. We show how the circuit can autonomously adapt to different environments, extracts stimulus features, and decorrelate and normalize input representations, which facilitates odor discrimination downstream.

scholarly journals A model of naturalistic decision making in preference tests

2021 ◽  
Author(s):  
John Ksander ◽  
Donald B Katz ◽  
Paul Miller
Keyword(s):  
Foraging Behavior ◽  
Neural Activity ◽  
Neural Circuit ◽  
Aversive Stimulus ◽  
Rapid Change ◽  
Natural World ◽  
State Transitions ◽  
Neural Firing ◽  
Stimulus Input ◽  
Preference Tests

AbstractDecisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal’s natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an “entice to stay” model) or from aversive stimulus input (a “repel to leave” model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in “entice to stay” model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data.Author summaryMany decisions are of the ilk of whether to continue sampling a stimulus or to switch to an alternative, a key feature of foraging behavior. We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise. In an “entice-to-stay” model, a reduction in the necessary positive stimulus input causes switching decisions. In a “repel-to-leave” model, a rise in aversive stimulus input produces a switch decision. We find that in tasks where the sampling of one stimulus follows another, adaptive biological processes arising from a highly hedonic stimulus can reduce the time spent at the following stimulus, by up to ten-fold in the “entice-to-stay” models. Along with potentially observable behavioral differences that could distinguish the classes of networks, we also found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus. In summary, our model findings lead to testable predictions and suggest a neural circuit-based framework for explaining foraging choices.

scholarly journals Satiety, Thermosensation and Mechanosensation Regulate a Spontaneous C. elegans Sleep State

2019 ◽  
Author(s):  
Daniel L. Gonzales ◽  
Jasmine Zhou ◽  
Jacob T. Robinson
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Brain Activity ◽  
External Environment ◽  
State Transitions ◽  
Brain State ◽  
Behavioral State ◽  
Chemical Signaling ◽  
Whole Brain ◽  
C Elegans

AbstractOne remarkable feature of the nervous system is its ability to rapidly and spontaneously switch between activity states. In the extreme example of sleep, animals arrest locomotion, reduce their sensitivity to sensory stimuli, and dramatically alter their neural activity. Small organisms are useful models to better understand these sudden changes in neural states because we can simultaneously observe whole-brain activity, monitor behavior and precisely regulate the external environment. Here, we show a spontaneous sleep-like behavior in C. elegans that is associated with a distinct global-brain state and regulated by both the animal’s internal physiological state and input from multiple sensory circuits. Specifically, we found that when confined in microfluidic chambers, adult worms spontaneously transition between periods of normal activity and short quiescent bouts, with behavioral state transitions occurring every few minutes. This quiescent state, which we call μSleep, meets the behavioral requirements of C. elegans sleep, is dependent on known sleep-promoting neurons ALA and RIS, and is associated with a global down-regulation of neural activity. Consistent with prior studies of C. elegans sleep, we found that μSleep is regulated by satiety and temperature. In addition, we show for the first time that quiescence can be either driven or suppressed by thermosensory input, and that animal restraint induces quiescence through mechanosensory pathways. Together, these results establish a rich model system for studying how neural and behavioral state transitions are influenced by multiple physiological and environmental conditions.Significance StatementUnique brain states govern animal behaviors like sleep and wakefulness; however, how the brain regulates these dramatic state transitions is not well understood. Brain activity can be influenced by a complex interaction between sensory circuits that monitor the external environment, neural circuits that control behavior, and internal chemical signaling. Here, we describe a platform to study behavioral states in a context that allows us to record whole-brain activity while controlling the environment and monitoring animal behavior. Specifically, we identify a pattern of sleep bouts in the roundworm C. elegans that occur when they are confined to microscopic fluidic chambers. This behavior platform provides a powerful system to study how neural circuits interact with chemical signaling to drive brain state transitions.

scholarly journals PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans

2012 ◽  
Vol 15 (12) ◽  
pp. 1675-1682 ◽  
Author(s):  
Arantza Barrios ◽  
Rajarshi Ghosh ◽  
Chunhui Fang ◽  
Scott W Emmons ◽  
Maureen M Barr
Keyword(s):  
Neural Circuit ◽  
Searching Behavior ◽  
Mate Searching ◽  
C Elegans

scholarly journals Correlation between fetal brain activity patterns and behavioral states: An exploratory fetal magnetoencephalography study

2011 ◽  
Vol 228 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Naim Haddad ◽  
Rathinaswamy B. Govindan ◽  
Srinivasan Vairavan ◽  
Eric Siegel ◽  
Jessica Temple ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Fetal Brain ◽  
Behavioral States

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.

Sign in / Sign up

Export Citation Format

Share Document