scholarly journals Developmental increase of inhibition drives decorrelation of neural activity

2021 ◽  
Author(s):  
Mattia Chini ◽  
Thomas Pfeffer ◽  
Ileana L. Hanganu-Opatz
Keyword(s):  
Neural Activity ◽  
Neurodevelopmental Disorders ◽  
Activity Patterns ◽  
Relative Increase ◽  
Neural Network Modeling ◽  
Developmental Transition ◽  
Efficient Storage ◽  
Eeg Data ◽  
Functional Consequences ◽  
The Brain

Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are unknown. The developmental transition has important functional consequences, as the latter state allows for more efficient storage, retrieval and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation/inhibition (E-I) ratio towards inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in two mouse models of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.

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 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 Learning multiple variable-speed sequences in striatum via cortical tutoring

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
James M Murray ◽  
G Sean Escola
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Pattern Generation ◽  
Cortical Circuits ◽  
Motor Sequence ◽  
Learned Behaviors ◽  
Multiple Variable ◽  
Lesion Studies ◽  
Inhibitory Circuit ◽  
The Brain

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

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.

Anticipation of temporally structured events in the brain

2020 ◽  
Author(s):  
Caroline S Lee ◽  
Mariam Aly ◽  
Christopher Baldassano
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Subjective Experiences ◽  
Movie Clip ◽  
Multiple Regions ◽  
Neural Event ◽  
The Brain

Learning about temporal structure is adaptive because it enables the generation of expectations. We examined how the brain uses experience in structured environments to anticipate upcoming events. During fMRI, individuals watched a 90-second movie clip six times. Using a Hidden Markov Model applied to searchlights across the whole brain, we identified temporal shifts between activity patterns evoked by the first vs. repeated viewings of the movie clip. In multiple regions throughout the cortex, neural activity patterns for repeated viewings shifted to preceded those of initial viewing by up to 12 seconds. This anticipation varied hierarchically in a posterior (less anticipation) to anterior (more anticipation) fashion. In a subset of these regions, neural event boundaries shifted with repeated viewing to precede subjective event boundaries by 5-7 seconds. Together, these results demonstrate a hierarchy of anticipatory signals in the human brain and link them to subjective experiences of events.

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 The brain uses invariant dynamics to generalize outputs across movements

2021 ◽  
Author(s):  
Vivek R. Athalye ◽  
Preeya Khanna ◽  
Suraj Gowda ◽  
Amy L. Orsborn ◽  
Rui M. Costa ◽  
...  
Keyword(s):  
Optimal Control ◽  
Nervous System ◽  
Neural Activity ◽  
Optimal Control Theory ◽  
Activity Patterns ◽  
Network Connectivity ◽  
Brain Machine Interface ◽  
Recent Proposal ◽  
Machine Interface ◽  
The Brain

AbstractThe nervous system uses a repertoire of outputs to produce diverse movements. Thus, the brain must solve how to issue and transition the same outputs in different movements. A recent proposal states that network connectivity constrains the transitions of neural activity to follow invariant rules across different movements, which we term ‘invariant dynamics’. However, it is unknown whether invariant dynamics are actually used to drive and generalize outputs across movements, and what advantage they provide for controlling movement. Using a brain-machine interface that transformed motor cortex activity into outputs for a neuroprosthetic cursor, we discovered that the same output is issued by different activity patterns in different movements. These distinct patterns then transition according to a model of invariant dynamics, leading to patterns that drive distinct future outputs. Optimal control theory revealed this use of invariant dynamics reduces the feedback input needed to control movement. Our results demonstrate that the brain uses invariant dynamics to generalize outputs across movements.

scholarly journals Roundabout receptor 2 maintains inhibitory control of the adult midbrain

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Bryan B Gore ◽  
Samara M Miller ◽  
Yong Sang Jo ◽  
Madison A Baird ◽  
Mrinalini Hoon ◽  
...  
Keyword(s):  
Axon Guidance ◽  
Inhibitory Control ◽  
Neural Activity ◽  
Dopamine Release ◽  
Activity Patterns ◽  
Adult Brain ◽  
Inhibitory Synapses ◽  
Learning Deficits ◽  
Genetic Inactivation ◽  
The Brain

The maintenance of excitatory and inhibitory balance in the brain is essential for its function. Here we find that the developmental axon guidance receptor Roundabout 2 (Robo2) is critical for the maintenance of inhibitory synapses in the adult ventral tegmental area (VTA), a brain region important for the production of the neurotransmitter dopamine. Following selective genetic inactivation of Robo2 in the adult VTA of mice, reduced inhibitory control results in altered neural activity patterns, enhanced phasic dopamine release, behavioral hyperactivity, associative learning deficits, and a paradoxical inversion of psychostimulant responses. These behavioral phenotypes could be phenocopied by selective inactivation of synaptic transmission from local GABAergic neurons of the VTA, demonstrating an important function for Robo2 in regulating the excitatory and inhibitory balance of the adult brain.

scholarly journals Harmonic brain modes: a unifying framework for linking space and time in brain dynamics

2017 ◽  
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

AbstractA 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; i.e. 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, anaesthesia, 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 Learning multiple variable-speed sequences in striatum via cortical tutoring

2017 ◽  
Author(s):  
James M. Murray ◽  
G. Sean Escola
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Pattern Generation ◽  
Cortical Circuits ◽  
Motor Sequence ◽  
Learned Behaviors ◽  
Multiple Variable ◽  
Lesion Studies ◽  
Inhibitory Circuit ◽  
The Brain

AbstractSparse, sequential patterns of neural activity have been observed in numerous brain areas during time-keeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

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