scholarly journals Low Dimensional Activity in Spiking Neuronal Networks

2017 ◽  
Author(s):  
Emil Wärnberg ◽  
Arvind Kumar
Keyword(s):  
Neural Activity ◽  
Degrees Of Freedom ◽  
Neuronal Networks ◽  
Brain Activity ◽  
A Priori ◽  
Network Activity ◽  
Connectivity Pattern ◽  
Dimensional Manifold ◽  
Low Dimensional

AbstractSeveral recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. Surprisingly, the structure of the intrinsic manifold of the network activity puts constraints on learning. For instance, animals find it difficult to perform tasks that may require a change in the intrinsic manifold. Here, we demonstrate that the Neural Engineering Framework (NEF) can be adapted to design a biologically plausible spiking neuronal network that exhibit low dimensional activity. Consistent with experimental observations, the resulting synaptic weight distribution is heavy-tailed (log-normal). In our model, a change in the intrinsic manifold of the network activity requires rewiring of the whole network, which may be either not possible or a very slow process. This observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.Significance statementA network in the brain consists of thousands of neurons. A priori, we expect that the network will have as many degrees of freedom as its number of neurons. Surprisingly, experimental evidence suggests that local brain activity is confined to a space spanned by 10 variables. Here, we describe an approach to construct spiking neuronal networks that exhibit low-dimensional activity and address the question: how the intrinsic dimensionality of the network activity restricts the learning as suggested by recent experiments? Specifically, we show that tasks that requires animals to change the network activity outside the intrinsic space would entail large changes in the neuronal connectivity, and therefore, animals are either slow or not able to acquire such tasks.

scholarly journals Estimating the dimensionality of the manifold underlying multi-electrode neural recordings

2020 ◽  
Author(s):  
Ege Altan ◽  
Sara A. Solla ◽  
Lee E. Miller ◽  
Eric J. Perreault
Keyword(s):  
Neural Activity ◽  
Degrees Of Freedom ◽  
Information Loss ◽  
Dimensional Manifold ◽  
Population Activity ◽  
Significant Information ◽  
Neural Data ◽  
Neural Recordings ◽  
Low Dimensional ◽  
Low Dimensional Manifold

AbstractIt is generally accepted that the number of neurons in a given brain area far exceeds the information that area encodes. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms’ accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding from the low-dimensional manifold to the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated dimensionality. We thus developed a denoising algorithm based on deep learning, the “Joint Autoencoder”, which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.Author SummaryThe number of neurons that we can record from has increased exponentially for decades; today we can simultaneously record from thousands of neurons. However, the individual firing rates are highly redundant. One approach to identifying important features from redundant data is to estimate the dimensionality of the neural recordings, which represents the number of degrees of freedom required to describe the data without significant information loss. Better understanding of dimensionality may also uncover the mechanisms of computation within a neural circuit. Circuits carrying out complex computations might be higher-dimensional than those carrying out simpler computations. Typically, studies have quantified neural dimensionality using one of several available methods despite a lack of consensus on which method would be most appropriate for neural data. In this work, we used several methods to investigate the accuracy of simulated neural data with properties mimicking those of actual neural recordings. Based on these results, we devised an analysis pipeline to estimate the dimensionality of neural recordings. Our work will allow scientists to extract informative features from a large number of highly redundant neurons, as well as quantify the complexity of information encoded by these neurons.

scholarly journals Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

2021 ◽  
Author(s):  
Corson N Areshenkoff ◽  
Daniel J Gale ◽  
Joe Y Nashed ◽  
Dominic Standage ◽  
John Randall Flanagan ◽  
...  
Keyword(s):  
Motor Learning ◽  
Large Scale ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Learning Performance ◽  
Dimensional Manifold ◽  
Learning Abilities ◽  
Manifold Structure ◽  
Electrophysiological Studies ◽  
Low Dimensional

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

scholarly journals An unconventional GABAergic circuit differently controls pyramidal neuron activity in two visual cortical areas via endocannabinoids

2021 ◽  
Author(s):  
Martin Montmerle ◽  
Fani Koukouli ◽  
Andrea Aguirre ◽  
Jeremy Peixoto ◽  
Vikash Choudhary ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Cannabinoid Receptor ◽  
Gaba Release ◽  
Network Activity ◽  
Connectivity Pattern ◽  
Neuron Activity ◽  
Cortical Areas ◽  
Visual Cortical ◽  
And Function

Perisomatic inhibition of neocortical pyramidal neurons (PNs) coordinates cortical network activity during sensory processing, and it has been mainly attributed to parvalbumin-expressing basket cells (BCs). However, cannabinoid receptor type 1 (CB1)-expressing interneurons also inhibit the perisomatic region of PNs but the connectivity and function of these elusive, yet prominent, neocortical GABAergic cells is unknown. We found that the connectivity pattern of CB1-positive BCs strongly differs between primary and high-order cortical visual areas. Moreover, persistently active CB1 signaling suppresses GABA release from CB1 BCs in the medial secondary visual cortex (V2M), but not in the primary (V1) visual area. Accordingly, in vivo, tonic CB1 signaling is responsible for higher but less coordinated PN activity in V2M than in V1. Our results indicate a differential CB1-mediated mechanism controlling PN activity, and suggest an alternative connectivity schemes of a specific GABAergic circuit in different cortical areas

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 The impact of D-cycloserine and sarcosine on in vivo frontal neural activity in a schizophrenia-like model

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lulu Yao ◽  
Zongliang Wang ◽  
Di Deng ◽  
Rongzhen Yan ◽  
Jun Ju ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Brain Slices ◽  
Unit Recording ◽  
Glycine Transporter 1 ◽  
Glycine Transporter ◽  
Neural Spiking ◽  
In Vivo Calcium Imaging ◽  
The Impact

Abstract Background N-methyl-D-aspartate receptor (NMDAR) hypofunction has been proposed to underlie the pathogenesis of schizophrenia. Specifically, reduced function of NMDARs leads to altered balance between excitation and inhibition which further drives neural network malfunctions. Clinical studies suggested that NMDAR modulators (glycine, D-serine, D-cycloserine and glycine transporter inhibitors) may be beneficial in treating schizophrenia patients. Preclinical evidence also suggested that these NMDAR modulators may enhance synaptic NMDAR function and synaptic plasticity in brain slices. However, an important issue that has not been addressed is whether these NMDAR modulators modulate neural activity/spiking in vivo. Methods By using in vivo calcium imaging and single unit recording, we tested the effect of D-cycloserine, sarcosine (glycine transporter 1 inhibitor) and glycine, on schizophrenia-like model mice. Results In vivo neural activity is significantly higher in the schizophrenia-like model mice, compared to control mice. D-cycloserine and sarcosine showed no significant effect on neural activity in the schizophrenia-like model mice. Glycine induced a large reduction in movement in home cage and reduced in vivo brain activity in control mice which prevented further analysis of its effect in schizophrenia-like model mice. Conclusions We conclude that there is no significant impact of the tested NMDAR modulators on neural spiking in the schizophrenia-like model mice.

scholarly journals Image-guided neural activity manipulation with a paramagnetic drug

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah Bricault ◽  
Ali Barandov ◽  
Peter Harvey ◽  
Elizabeth DeTienne ◽  
Aviad Hai ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Neural Activity ◽  
Brain Activity ◽  
Pharmacological Properties ◽  
Resonance Imaging ◽  
Significant Challenge ◽  
Image Guided

AbstractTargeted manipulations of neural activity are essential approaches in neuroscience and neurology, but monitoring such procedures in the living brain remains a significant challenge. Here we introduce a paramagnetic analog of the drug muscimol that enables targeted neural inactivation to be performed with feedback from magnetic resonance imaging. We validate pharmacological properties of the compound in vitro, and show that its distribution in vivo reliably predicts perturbations to brain activity.

scholarly journals Evaluation of in vitro neuronal networks for the study of spontaneous activity

STEMedicine ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. e35 ◽  
Author(s):  
Diletta Pozzi
Keyword(s):  
Nervous System ◽  
Spontaneous Activity ◽  
Time Course ◽  
Neuronal Networks ◽  
Brain Slices ◽  
In Vitro Models ◽  
Network Activity ◽  
External Stimuli

In the absence of external stimuli, the nervous system exhibits a spontaneous electrical activity whose functions are not fully understood, and that represents the background noise of brain operations. Spontaneous activity has been proven to arise not only in vivo, but in in vitro neuronal networks as well, following some stereotypical patterns that reproduce the time course of development of the mammalian nervous system. This review provides an overview of in vitro models for the study of spontaneous network activity, discussing their ability to reproduce in vivo - like dynamics and the main findings obtained with each particular model. While explanted brain slices are able to reproduce the neuronal oscillations typically observed in anaesthetized animals, dissociated cultures allow the use of patient-derived neurons and limit the number of animals used for sample preparation.

scholarly journals Differences in social brain function in autism spectrum disorder are linked to the serotonin transporter

2021 ◽  
Author(s):  
Nichol M.L. Wong ◽  
Ottavia Dipasquale ◽  
Federico E Turkheimer ◽  
James L Findon ◽  
Robert H Wichers ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Oral Dose ◽  
Serotonin Transporter ◽  
A Priori ◽  
Emotion Processing ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Network Activity ◽  
Control Group

Alterations in the serotonergic control of brain pathways responsible for facial-emotion processing in people with autism spectrum disorder (ASD) may be a target for intervention. However, the molecular underpinnings of autistic-neurotypical serotonergic differences are challenging to access in vivo. Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) has helped define molecular-enriched fMRI brain networks based on a priori information about the spatial distribution of neurochemical systems from available PET templates. Here, we used REACT to estimate the dominant fMRI signal related to the serotonin transporter (5-HTT) distribution during processing of aversive facial expressions of emotion processing in adults with and without ASD. We first predicted a group difference in baseline (placebo) functioning of this system. We next used a single 20 mg oral dose of citalopram, i.e. a serotonin reuptake inhibitor, to test the hypothesis that network activity in people with and without ASD would respond differently to inhibition of 5-HTT. To confirm the specificity of our findings, we also repeated the analysis with 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 receptor maps. We found a baseline group difference in the 5-HTT-enriched response to faces in the ventromedial prefrontal cortex. A single oral dose of citalopram 'shifted' the response in the ASD group towards the neurotypical baseline but did not alter response in the control group. Our findings suggest that the 5HTT-enriched functional network is dynamically different in ASD during processing of socially relevant stimuli. Whether this acute neurobiological response to citalopram in ASD translates to a clinical target will be an important next step.

scholarly journals From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior

BME Frontiers ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Richard H. Roth ◽  
Jun B. Ding
Keyword(s):  
Neuronal Activity ◽  
Neural Activity ◽  
Large Scale ◽  
Technological Development ◽  
Brain Activity ◽  
Complex Problem ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Electrophysiological Methods

Understanding how brain activity encodes information and controls behavior is a long-standing question in neuroscience. This complex problem requires converging efforts from neuroscience and engineering, including technological solutions to perform high-precision and large-scale recordings of neuronal activity in vivo as well as unbiased methods to reliably measure and quantify behavior. Thanks to advances in genetics, molecular biology, engineering, and neuroscience, in recent decades, a variety of optical imaging and electrophysiological approaches for recording neuronal activity in awake animals have been developed and widely applied in the field. Moreover, sophisticated computer vision and machine learning algorithms have been developed to analyze animal behavior. In this review, we provide an overview of the current state of technology for neuronal recordings with a focus on optical and electrophysiological methods in rodents. In addition, we discuss areas that future technological development will need to cover in order to further our understanding of the neural activity underlying behavior.

scholarly journals Extracellular matrix supports excitation-inhibition balance in neuronal networks by stabilizing inhibitory synapses

2020 ◽  
Author(s):  
Egor Dzyubenko ◽  
Michael Fleischer ◽  
Daniel Manrique-Castano ◽  
Mina Borbor ◽  
Christoph Kleinschnitz ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Neuronal Network ◽  
Neuronal Plasticity ◽  
Neuronal Networks ◽  
Network Activity ◽  
Inhibitory Synapses ◽  
Neuronal Network Activity ◽  
The Brain

AbstractMaintaining the balance between excitation and inhibition is essential for the appropriate control of neuronal network activity. Sustained excitation-inhibition (E-I) balance relies on the orchestrated adjustment of synaptic strength, neuronal activity and network circuitry. While growing evidence indicates that extracellular matrix (ECM) of the brain is a crucial regulator of neuronal excitability and synaptic plasticity, it remains unclear whether and how ECM contributes to neuronal circuit stability. Here we demonstrate that the integrity of ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Depletion of ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. After ECM depletion, inhibitory synapse strength homeostatically increases via the reduction of presynaptic GABAB receptors. However, the inhibitory connectivity reduces to an extent that inhibitory synapse scaling is no longer efficient in controlling neuronal network activity. Our results indicate that the brain ECM preserves the balanced network state by stabilizing inhibitory synapses.Significance statementThe question how the brain’s extracellular matrix (ECM) controls neuronal plasticity and network activity is key for an appropriate understanding of brain functioning. In this study, we demonstrate that ECM depletion much more strongly affects the integrity of inhibitory than excitatory synapses in vitro and in vivo. We revealed that by retaining inhibitory connectivity, ECM ensures the efficiency of inhibitory control over neuronal network activity. Our work significantly expands our current state of knowledge about the mechanisms of neuronal network activity regulation. Our findings are similarly relevant for researchers working on the physiological regulation of neuronal plasticity in vitro and in vivo and for researchers studying the remodeling of neuronal networks upon brain injury, where prominent ECM alterations occur.

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