scholarly journals Deconstructing scale-free neuronal avalanches: behavioral transitions and neuronal response

2021 ◽  
Author(s):  
Davor Curic ◽  
Victorita E. Ivan ◽  
David T. Cuesta ◽  
Ingrid M. Esteves ◽  
Majid H. Mohajerani ◽  
...  
Keyword(s):  
Neuronal Response ◽  
Population Level ◽  
Retrosplenial Cortex ◽  
Neural Population ◽  
Information Cascades ◽  
Strong Connectivity ◽  
Scale Free ◽  
Cognitive Information ◽  
Neuronal Avalanches ◽  
The Brain

Abstract Observations of neurons in a resting brain and neurons in cultures often display spontaneous scale-free collective dynamics in the form of information cascades, also called “neuronal avalanches”. This has motivated the so called critical brain hypothesis which posits that the brain is self-tuned to a critical point or regime, separating exponentially-growing dynamics from quiescent states, to achieve optimality. Yet, how such optimality of information transmission is related to behaviour and whether it persists under behavioural transitions has remained a fundamental knowledge gap. Here, we aim to tackle this challenge by studying behavioural transitions in mice using two-photon calcium imaging of the retrosplenial cortex -- an area of the brain well positioned to integrate sensory, mnemonic, and cognitive information by virtue of its strong connectivity with the hippocampus, medial prefrontal cortex, and primary sensory cortices. Our work shows that the response of the underlying neural population to behavioural transitions can vary significantly between different sub-populations such that one needs to take the structural and functional network properties of these sub-populations into account to understand the properties at the total population level. Specifically, we show that the retrosplenial cortex contains at least one sub-population capable of switching between two different scale-free regimes, indicating an intricate relationship between behaviour and the optimality of neuronal response at the subgroup level. This asks for a potential reinterpretation of the emergence of self-organized criticality in neuronal systems.

scholarly journals A neural population mechanism for rapid learning

2017 ◽  
Author(s):  
Matthew G. Perich ◽  
Juan A. Gallego ◽  
Lee E. Miller
Keyword(s):  
Brain Area ◽  
Population Level ◽  
Neural Population ◽  
Reaching Movement ◽  
Neural Populations ◽  
Show Evidence ◽  
Preparatory Activity ◽  
Plastic Changes ◽  
The Brain

AbstractLong-term learning of language, mathematics, and motor skills likely requires plastic changes in the cortex, but behavior often requires faster changes, sometimes based even on single errors. Here, we show evidence of one mechanism by which the brain can rapidly develop new motor output, seemingly without altering the functional connectivity between or within cortical areas. We recorded simultaneously from hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices, and computed models relating these neural populations throughout adaptation to reaching movement perturbations. We found a signature of learning in the “null subspace” of PMd with respect to M1. Earlier experiments have shown that null subspace activity allows the motor cortex to alter preparatory activity without directly influencing M1. In our experiments, the null subspace planning activity evolved with the adaptation, yet the “potent” mapping that captures information sent to M1 was preserved. Our results illustrate a population-level mechanism within the motor cortices to adjust the output from one brain area to its downstream structures that could be exploited throughout the brain for rapid, online behavioral adaptation.

scholarly journals The interaction between neural populations: Additive versus diffusive coupling

2021 ◽  
Author(s):  
Marinho Antunes Lopes ◽  
Khalid Hamandi ◽  
Jiaxiang Zhang ◽  
Jen Creaser
Keyword(s):  
Brain Network ◽  
Coupling Function ◽  
Neural Population ◽  
Escape Time ◽  
Future Studies ◽  
Scale Free ◽  
Diffusive Coupling ◽  
Neural Populations ◽  
Global And Local ◽  
The Brain

Models of networks of populations of neurons commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population's activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. Here, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider networks with two and three nodes, and large random and scale-free networks with 64 nodes. We further assess functional networks inferred from magnetoencephalography (MEG) from people with epilepsy and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network's global and local ability to generate seizures. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. We find that people with epilepsy have higher additive BNI than controls, as hypothesized, while the diffusive BNI provides the opposite result. Moreover, individual nodes that are more likely to drive seizures with one type of coupling are more likely to prevent seizures with the other coupling function. Our results on the MEG networks and evidence from the literature suggest that the additive coupling may be a better modelling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies.

scholarly journals Behavioral Triggers of Skin Conductance Responses and Their Neural Correlates in the Primate Amygdala

2009 ◽  
Vol 101 (4) ◽  
pp. 1749-1754 ◽  
Author(s):  
Christopher M. Laine ◽  
Kevin M. Spitler ◽  
Clayton P. Mosher ◽  
Katalin M. Gothard
Keyword(s):  
Skin Conductance ◽  
Single Unit ◽  
Population Level ◽  
Emotional Reaction ◽  
Neural Population ◽  
Experimental Conditions ◽  
Indirect Measure ◽  
Skin Conductance Responses ◽  
The Right ◽  
Sympathetic Arousal

The amygdala plays a crucial role in evaluating the emotional significance of stimuli and in transforming the results of this evaluation into appropriate autonomic responses. Lesion and stimulation studies suggest involvement of the amygdala in the generation of the skin conductance response (SCR), which is an indirect measure of autonomic activity that has been associated with both emotion and attention. It is unclear if this involvement marks an emotional reaction to an external stimulus or sympathetic arousal regardless of its origin. We recorded skin conductance in parallel with single-unit activity from the right amygdala of two rhesus monkeys during a rewarded image viewing task and while the monkeys sat alone in a dimly lit room, drifting in and out of sleep. In both experimental conditions, we found similar SCR-related modulation of activity at the single-unit and neural population level. This suggests that the amygdala contributes to the production or modulation of SCRs regardless of the source of sympathetic arousal.

scholarly journals Nervous Activity of the Brain in Five Dimensions

Biophysica ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 38-47
Author(s):  
Arturo Tozzi ◽  
James F. Peters ◽  
Norbert Jausovec ◽  
Arjuna P. H. Don ◽  
Sheela Ramanna ◽  
...  
Keyword(s):  
Fourier Analysis ◽  
Nervous Activity ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Data Sets ◽  
Spatial And Temporal Patterns ◽  
Five Dimensions ◽  
Four Dimensions ◽  
Scale Free ◽  
The Brain

The nervous activity of the brain takes place in higher-dimensional functional spaces. It has been proposed that the brain might be equipped with phase spaces characterized by four spatial dimensions plus time, instead of the classical three plus time. This suggests that global visualization methods for exploiting four-dimensional maps of three-dimensional experimental data sets might be used in neuroscience. We asked whether it is feasible to describe the four-dimensional trajectories (plus time) of two-dimensional (plus time) electroencephalographic traces (EEG). We made use of quaternion orthographic projections to map to the surface of four-dimensional hyperspheres EEG signal patches treated with Fourier analysis. Once achieved the proper quaternion maps, we show that this multi-dimensional procedure brings undoubted benefits. The treatment of EEG traces with Fourier analysis allows the investigation the scale-free activity of the brain in terms of trajectories on hyperspheres and quaternionic networks. Repetitive spatial and temporal patterns undetectable in three dimensions (plus time) are easily enlightened in four dimensions (plus time). Further, a quaternionic approach makes it feasible to identify spatially far apart and temporally distant periodic trajectories with the same features, such as, e.g., the same oscillatory frequency or amplitude. This leads to an incisive operational assessment of global or broken symmetries, domains of attraction inside three-dimensional projections and matching descriptions between the apparently random paths hidden in the very structure of nervous fractal signals.

scholarly journals Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Orestis Stylianou ◽  
Frigyes Samuel Racz ◽  
Andras Eke ◽  
Peter Mukli
Keyword(s):  
Resting State ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Coupled Dynamics ◽  
Scale Free ◽  
Neural Processes ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Neuronal apoptosis: signal and cell diversity

1969 ◽  
Vol 40 (1) ◽  
pp. 124-133
Author(s):  
Lina Vanessa Becerra ◽  
Hernán José Pimienta
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Neuronal Response ◽  
Neuronal Cell ◽  
Cell Types ◽  
Point Of View ◽  
Trophic Factors ◽  
Cell Diversity ◽  
Apoptotic Pathways ◽  
The Brain

Programmed cell death occurs as a physiological process during development. In the brain and spinal cord this event determines the number and location of the different cell types. In adulthood, programmed cell death or apoptosis is more restricted but it may play a major role in different acute and chronic pathological entities. However, in contrast to other tissues where apoptosis has been widely documented from a morphological point of view, in the central nervous system complete anatomical evidence of apoptosis is scanty. In spite of this there is consensus about the activation of different signal systems associated to programmed cell death. In the present article we attempt to summarize the main apoptotic pathways so far identified in nervous tissue. Considering that apoptotic pathways are multiple, the neuronal cell types are highly diverse and specialized and that neuronal response to injury and survival depends upon tissue context, (i.e., preservation of connectivity, glial integrity and cell matrix, blood supply and trophic factors availability) what is relevant for the apoptotic process in a sector of the brain may not be important in another.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

SummaryPrevious research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as “stiff” dimensions, while it is insensitive to many others (“sloppy” dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Pre-stimulus phase and amplitude regulation of phase-locked responses are maximized in the critical state

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Arthur-Ervin Avramiea ◽  
Richard Hardstone ◽  
Jan-Matthis Lueckmann ◽  
Jan Bím ◽  
Huibert D Mansvelder ◽  
...  
Keyword(s):  
Computational Model ◽  
Critical State ◽  
Global Dynamics ◽  
Critical Dynamics ◽  
Neuronal Responses ◽  
Scale Free ◽  
Stimulus Response ◽  
Functional States ◽  
The Brain

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

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