scholarly journals Revealing neural correlates of behavior without behavioral measurements

2019 ◽  
Author(s):  
Alon Rubin ◽  
Liron Sheintuch ◽  
Noa Brande-Eilat ◽  
Or Pinchasof ◽  
Yoav Rechavi ◽  
...  
Keyword(s):  
Internal Structure ◽  
Large Scale ◽  
Tuning Curve ◽  
Activity Patterns ◽  
A Priori ◽  
Network Activity ◽  
Head Direction ◽  
External Variable ◽  
Unknown Variable ◽  
Ensemble Activity

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a-priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition, and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any pre-defined external variable – revealing place-tuning in the hippocampus and head-direction tuning in the thalamus and postsubiculum, without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The structure of ensemble activity patterns was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

scholarly journals Revealing neural correlates of behavior without behavioral measurements

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alon Rubin ◽  
Liron Sheintuch ◽  
Noa Brande-Eilat ◽  
Or Pinchasof ◽  
Yoav Rechavi ◽  
...  
Keyword(s):  
Internal Structure ◽  
Large Scale ◽  
Tuning Curve ◽  
Activity Patterns ◽  
A Priori ◽  
Network Activity ◽  
Head Direction ◽  
External Variable ◽  
Unknown Variable ◽  
Tuning Curves

Abstract Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The internal structure was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

scholarly journals Selective entrainment of gamma subbands by different slow network oscillations

2017 ◽  
Vol 114 (17) ◽  
pp. 4519-4524 ◽  
Author(s):  
Weiwei Zhong ◽  
Mareva Ciatipis ◽  
Thérèse Wolfenstetter ◽  
Jakob Jessberger ◽  
Carola Müller ◽  
...  
Keyword(s):  
Large Scale ◽  
Dorsal Hippocampus ◽  
Activity Patterns ◽  
Specific Interaction ◽  
Brain Regions ◽  
Network Activity ◽  
Gamma Frequency ◽  
Cornu Ammonis ◽  
Oscillatory Coupling ◽  
Fast Oscillations

Theta oscillations (4–12 Hz) are thought to provide a common temporal reference for the exchange of information among distant brain networks. On the other hand, faster gamma-frequency oscillations (30–160 Hz) nested within theta cycles are believed to underlie local information processing. Whether oscillatory coupling between global and local oscillations, as showcased by theta-gamma coupling, is a general coding mechanism remains unknown. Here, we investigated two different patterns of oscillatory network activity, theta and respiration-induced network rhythms, in four brain regions of freely moving mice: olfactory bulb (OB), prelimbic cortex (PLC), parietal cortex (PAC), and dorsal hippocampus [cornu ammonis 1 (CA1)]. We report differential state- and region-specific coupling between the slow large-scale rhythms and superimposed fast oscillations. During awake immobility, all four regions displayed a respiration-entrained rhythm (RR) with decreasing power from OB to CA1, which coupled exclusively to the 80- to 120-Hz gamma subband (γ2). During exploration, when theta activity was prevailing, OB and PLC still showed exclusive coupling of RR with γ2 and no theta-gamma coupling, whereas PAC and CA1 switched to selective coupling of theta with 40- to 80-Hz (γ1) and 120- to 160-Hz (γ3) gamma subbands. Our data illustrate a strong, specific interaction between neuronal activity patterns and respiration. Moreover, our results suggest that the coupling between slow and fast oscillations is a general brain mechanism not limited to the theta rhythm.

scholarly journals Memory Replay in Balanced Recurrent Networks

2016 ◽  
Author(s):  
Nikolay Chenkov ◽  
Henning Sprekeler ◽  
Richard Kempter
Keyword(s):  
Large Scale ◽  
Neuronal Excitability ◽  
Activity Patterns ◽  
Recurrent Network ◽  
Network Activity ◽  
Sharp Waves ◽  
Sequence Of Events ◽  
Up States ◽  
Plausible Model ◽  
Network States

AbstractComplex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.Author SummarySynaptic plasticity is the basis for learning and memory, and many experiments indicate that memories are imprinted in synaptic connections. However, basic mechanisms of how such memories are retrieved and consolidated remain unclear. In particular, how can one-shot learning of a sequence of events achieve a sufficiently strong synaptic footprint to retrieve or replay this sequence? Using both numerical simulations of spiking neural networks and an analytic approach, we provide a biologically plausible model for understanding how minute synaptic changes in a recurrent network can nevertheless be retrieved by small cues or even manifest themselves as activity patterns that emerge spontaneously. We show how the retrieval of exceedingly small changes in the connections across assemblies is robustly facilitated by recurrent connectivity within assemblies. This interaction between recurrent amplification within an assembly and the feed-forward propagation of activity across the network establishes a basis for the retrieval of memories.

scholarly journals Refinement of corticospinal neuron activity during skilled motor learning

2021 ◽  
Author(s):  
Najet Serradj ◽  
Francesca Marino ◽  
Yunuen Moreno-López ◽  
Sydney Agger ◽  
Andrew Sloan ◽  
...  
Keyword(s):  
Large Scale ◽  
Primary Motor Cortex ◽  
Corticospinal Tract ◽  
Activity Patterns ◽  
Critical Role ◽  
Network Activity ◽  
Neuron Activity ◽  
Cortical Motor ◽  
Dependent Behavior

AbstractThe learning of motor skills relies on plasticity of the primary motor cortex as task acquisition drives the remodeling of cortical motor networks1,2. Large scale cortical remodeling of evoked motor outputs occurs in response to the learning of skilled, corticospinal-dependent behavior, but not simple, unskilled tasks1. Here we determine the response of corticospinal neurons to both skilled and unskilled motor training and assess the role of corticospinal neuron activity in the execution of the trained behaviors. Using in vivo calcium imaging, we found that refinement of corticospinal activity correlated with the development of skilled, but not unskilled, motor expertise. Animals that failed to learn our skilled task exhibited a limited repertoire of dynamic movements and a corresponding absence of network modulation. Transection of the corticospinal tract and aberrant activation of corticospinal neurons show the necessity for corticospinal network activity patterns in the execution of skilled, but not unskilled, movement. We reveal a critical role for corticospinal network modulation in the learning and execution of skilled motor movements. The integrity of the corticospinal tract is essential to the recovery of voluntary movement after central nervous system injuries and these findings should help to shape translational approaches to motor recovery.

Synchronizing Brain Rhythms to Improve Cognition

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Shrey Grover ◽  
John A. Nguyen ◽  
Robert M.G. Reinhart
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Neuropsychiatric Disorders ◽  
Network Activity ◽  
Human Cognition ◽  
Annual Review ◽  
Publication Date ◽  
Early Results ◽  
Transcranial Alternating Current Stimulation ◽  
Communication Dynamics

Impaired cognition is common in many neuropsychiatric disorders and severely compromises quality of life. Synchronous electrophysiological rhythms represent a core mechanism for sculpting communication dynamics among large-scale brain networks that underpin cognition and its breakdown in neuropsychiatric disorders. Here, we review an emerging neuromodulation technology called transcranial alternating current stimulation that has shown remarkable early results in rapidly improving various domains of human cognition by modulating properties of rhythmic network synchronization. Future noninvasive neuromodulation research holds promise for potentially rescuing network activity patterns and improving cognition, setting groundwork for the development of drug-free, circuit-based therapeutics for people with cognitive brain disorders. Expected final online publication date for the Annual Review of Medicine, Volume 72 is January 27, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Transient cortical circuits match spontaneous and sensory-driven activity during development

Science ◽  
2020 ◽  
Vol 370 (6514) ◽  
pp. eabb2153 ◽  
Author(s):  
Zoltán Molnár ◽  
Heiko J. Luhmann ◽  
Patrick O. Kanold
Keyword(s):  
Spontaneous Activity ◽  
Large Scale ◽  
Developmental Stages ◽  
Activity Patterns ◽  
Network Activity ◽  
Cortical Circuits ◽  
Pathological Conditions ◽  
Psychiatric Conditions ◽  
Cortical Dysfunction ◽  
Cortical Architecture

At the earliest developmental stages, spontaneous activity synchronizes local and large-scale cortical networks. These networks form the functional template for the establishment of global thalamocortical networks and cortical architecture. The earliest connections are established autonomously. However, activity from the sensory periphery reshapes these circuits as soon as afferents reach the cortex. The early-generated, largely transient neurons of the subplate play a key role in integrating spontaneous and sensory-driven activity. Early pathological conditions—such as hypoxia, inflammation, or exposure to pharmacological compounds—alter spontaneous activity patterns, which subsequently induce disturbances in cortical network activity. This cortical dysfunction may lead to local and global miswiring and, at later stages, can be associated with neurological and psychiatric conditions.

The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

2018 ◽  
Vol 29 (8) ◽  
pp. 3201-3210 ◽  
Author(s):  
Benjamin Meyer ◽  
Kenneth S L Yuen ◽  
Victor Saase ◽  
Raffael Kalisch
Keyword(s):  
Functional Role ◽  
Large Scale ◽  
Activity Patterns ◽  
Clinical Importance ◽  
Mental States ◽  
Brain Network ◽  
Anxiety Reduction ◽  
Anterior Cingulate ◽  
Network Activity

Abstract Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

scholarly journals Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis

2015 ◽  
Vol 27 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Carlos E. Vargas-Irwin ◽  
David M. Brandman ◽  
Jonas B. Zimmermann ◽  
John P. Donoghue ◽  
Michael J. Black
Keyword(s):  
Dimensionality Reduction ◽  
Spike Train ◽  
Large Scale ◽  
Activity Patterns ◽  
Series Data ◽  
Distance Metrics ◽  
Information Encoding ◽  
Similarity Space ◽  
Neural Spiking ◽  
Ensemble Activity

Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large-scale ensemble activity beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how spike train SIMilarity space (SSIMS) analysis captures the relationship between goal directions for an eight-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

2021 ◽  
Author(s):  
Ting-Hsuan Wang ◽  
Cheng-Ching Huang ◽  
Jui-Hung Hung
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
A Priori ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Comparable Accuracy ◽  
Meta Analyses ◽  
Next Generation Sequencing Ngs ◽  
Adapter Trimming ◽  
Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epileptogenesis due to glia-mediated synaptic scaling

2008 ◽  
Vol 6 (37) ◽  
pp. 655-668 ◽  
Author(s):  
Cristina Savin ◽  
Jochen Triesch ◽  
Michael Meyer-Hermann
Keyword(s):  
Immune System ◽  
Neuronal Activity ◽  
Activity Patterns ◽  
Brain Inflammation ◽  
Network Activity ◽  
Homeostatic Regulation ◽  
Synaptic Scaling ◽  
Tnf Α ◽  
Factor Α ◽  
Potential Interactions

Homeostatic regulation of neuronal activity is fundamental for the stable functioning of the cerebral cortex. One form of homeostatic synaptic scaling has been recently shown to be mediated by glial cells that interact with neurons through the diffusible messenger tumour necrosis factor-α (TNF-α). Interestingly, TNF-α is also used by the immune system as a pro-inflammatory messenger, suggesting potential interactions between immune system signalling and the homeostatic regulation of neuronal activity. We present the first computational model of neuron–glia interaction in TNF-α-mediated synaptic scaling. The model shows how under normal conditions the homeostatic mechanism is effective in balancing network activity. After chronic immune activation or TNF-α overexpression by glia, however, the network develops seizure-like activity patterns. This may explain why under certain conditions brain inflammation increases the risk of seizures. Additionally, the model shows that TNF-α diffusion may be responsible for epileptogenesis after localized brain lesions.

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