Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs

According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the “common-noise” groups compared to the “independent-noise” groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.

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Firing rate models for gamma oscillations

Journal of Neurophysiology ◽

10.1152/jn.00741.2018 ◽

2019 ◽

Vol 121 (6) ◽

pp. 2181-2190 ◽

Cited By ~ 8

Author(s):

Stephen Keeley ◽

Áine Byrne ◽

André Fenton ◽

John Rinzel

Keyword(s):

Firing Rate ◽

Computational Models ◽

Network Models ◽

Gamma Oscillations ◽

Brain Regions ◽

Dynamic Feature ◽

Gamma Rhythm ◽

Firing Rate Models ◽

General Network ◽

Dynamic Variables

Gamma oscillations are readily observed in a variety of brain regions during both waking and sleeping states. Computational models of gamma oscillations typically involve simulations of large networks of synaptically coupled spiking units. These networks can exhibit strongly synchronized gamma behavior, whereby neurons fire in near synchrony on every cycle, or weakly modulated gamma behavior, corresponding to stochastic, sparse firing of the individual units on each cycle of the population gamma rhythm. These spiking models offer valuable biophysical descriptions of gamma oscillations; however, because they involve many individual neuronal units they are limited in their ability to communicate general network-level dynamics. Here we demonstrate that few-variable firing rate models with established synaptic timescales can account for both strongly synchronized and weakly modulated gamma oscillations. These models go beyond the classical formulations of rate models by including at least two dynamic variables per population: firing rate and synaptic activation. The models’ flexibility to capture the broad range of gamma behavior depends directly on the timescales that represent recruitment of the excitatory and inhibitory firing rates. In particular, we find that weakly modulated gamma oscillations occur robustly when the recruitment timescale of inhibition is faster than that of excitation. We present our findings by using an extended Wilson-Cowan model and a rate model derived from a network of quadratic integrate-and-fire neurons. These biophysical rate models capture the range of weakly modulated and coherent gamma oscillations observed in spiking network models, while additionally allowing for greater tractability and systems analysis. NEW & NOTEWORTHY Here we develop simple and tractable models of gamma oscillations, a dynamic feature observed throughout much of the brain with significant correlates to behavior and cognitive performance in a variety of experimental contexts. Our models depend on only a few dynamic variables per population, but despite this they qualitatively capture features observed in previous biophysical models of gamma oscillations that involve many individual spiking units.

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Student Perceptions of Community: A Different Perspective

NASPA Journal ◽

10.2202/0027-6014.1064 ◽

1998 ◽

Vol 35 (4) ◽

Author(s):

Jackie Clark ◽

Joan Hirt

Keyword(s):

Student Perceptions ◽

Large Scale ◽

Research University ◽

Small Scale ◽

Social Environments ◽

Common Assumption ◽

Small Communities ◽

Public Research University ◽

The Common ◽

Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

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A High-Content Screening Assay for Small Molecules That Stabilize Mutant Triose Phosphate Isomerase (TPI) as Treatments for TPI Deficiency

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/24725552211018198 ◽

2021 ◽

pp. 247255522110181

Author(s):

Andreas Vogt ◽

Samantha L. Eicher ◽

Tracey D. Myers ◽

Stacy L. Hrizo ◽

Laura L. Vollmer ◽

...

Keyword(s):

High Throughput Screening ◽

Large Scale ◽

Protein Quality ◽

Fluorescent Protein ◽

Triose Phosphate Isomerase ◽

Pharmacological Intervention ◽

Screening Assay ◽

Triose Phosphate ◽

The Common ◽

Green Fluorescent

Triose phosphate isomerase deficiency (TPI Df) is an untreatable, childhood-onset glycolytic enzymopathy. Patients typically present with frequent infections, anemia, and muscle weakness that quickly progresses with severe neuromusclar dysfunction requiring aided mobility and often respiratory support. Life expectancy after diagnosis is typically ~5 years. There are several described pathogenic mutations that encode functional proteins; however, these proteins, which include the protein resulting from the “common” TPIE105D mutation, are unstable due to active degradation by protein quality control (PQC) pathways. Previous work has shown that elevating mutant TPI levels by genetic or pharmacological intervention can ameliorate symptoms of TPI Df in fruit flies. To identify compounds that increase levels of mutant TPI, we have developed a human embryonic kidney (HEK) stable knock-in model expressing the common TPI Df protein fused with green fluorescent protein (HEK TPIE105D-GFP). To directly address the need for lead TPI Df therapeutics, these cells were developed into an optical drug discovery platform that was implemented for high-throughput screening (HTS) and validated in 3-day variability tests, meeting HTS standards. We initially used this assay to screen the 446-member National Institutes of Health (NIH) Clinical Collection and validated two of the hits in dose–response, by limited structure–activity relationship studies with a small number of analogs, and in an orthogonal, non-optical assay in patient fibroblasts. The data form the basis for a large-scale phenotypic screening effort to discover compounds that stabilize TPI as treatments for this devastating childhood disease.

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A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720006002272 ◽

2006 ◽

Vol 04 (03) ◽

pp. 639-647 ◽

Cited By ~ 6

Author(s):

ELEAZAR ESKIN ◽

RODED SHARAN ◽

ERAN HALPERIN

Keyword(s):

Large Scale ◽

Computational Cost ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Novel Approach ◽

Maximum Likelihood Criterion ◽

The Common ◽

Genomic Regions ◽

High Computational Cost ◽

Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

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Properties of precise firing synchrony between synaptically coupled cortical interneurons depend on their mode of coupling

Journal of Neurophysiology ◽

10.1152/jn.00304.2015 ◽

2015 ◽

Vol 114 (1) ◽

pp. 624-637 ◽

Cited By ~ 17

Author(s):

Hang Hu ◽

Ariel Agmon

Keyword(s):

Firing Rate ◽

Electrical Coupling ◽

Gamma Oscillations ◽

Inhibitory Synapses ◽

Data Set ◽

Temporal Precision ◽

Gamma Frequency ◽

Rate Dependent ◽

Cortical Interneurons

Precise spike synchrony has been widely reported in the central nervous system, but its functional role in encoding, processing, and transmitting information is yet unresolved. Of particular interest is firing synchrony between inhibitory cortical interneurons, thought to drive various cortical rhythms such as gamma oscillations, the hallmark of cognitive states. Precise synchrony can arise between two interneurons connected electrically, through gap junctions, chemically, through fast inhibitory synapses, or dually, through both types of connections, but the properties of synchrony generated by these different modes of connectivity have never been compared in the same data set. In the present study we recorded in vitro from 152 homotypic pairs of two major subtypes of mouse neocortical interneurons: parvalbumin-containing, fast-spiking (FS) interneurons and somatostatin-containing (SOM) interneurons. We tested firing synchrony when the two neurons were driven to fire by long, depolarizing current steps and used a novel synchrony index to quantify the strength of synchrony, its temporal precision, and its dependence on firing rate. We found that SOM-SOM synchrony, driven solely by electrical coupling, was less precise than FS-FS synchrony, driven by inhibitory or dual coupling. Unlike SOM-SOM synchrony, FS-FS synchrony was strongly firing rate dependent and was not evident at the prototypical 40-Hz gamma frequency. Computer simulations reproduced these differences in synchrony without assuming any differences in intrinsic properties, suggesting that the mode of coupling is more important than the interneuron subtype. Our results provide novel insights into the mechanisms and properties of interneuron synchrony and point out important caveats in current models of cortical oscillations.

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Adaptation in Firing Rate Models

Encyclopedia of Computational Neuroscience ◽

10.1007/978-1-4614-6675-8_100015 ◽

2015 ◽

pp. 142-142

Keyword(s):

Firing Rate ◽

Firing Rate Models

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A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

10.1101/2020.11.23.393702 ◽

2020 ◽

Author(s):

Alina Pauline Liebisch ◽

Thomas Eggert ◽

Alina Shindy ◽

Elia Valentini ◽

Stephanie Irving ◽

...

Keyword(s):

Data Quality ◽

Time Course ◽

Muscular Activity ◽

Gamma Oscillations ◽

Gamma Band ◽

Gamma Activity ◽

Single Muscle ◽

Frequency Range ◽

Continuous Eeg ◽

Artefact Removal

AbstractBackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity.New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes.ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm.ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

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Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity

10.1101/2020.07.23.217026 ◽

2020 ◽

Author(s):

Gabi Socolovsky ◽

Maoz Shamir

Keyword(s):

Mean Field ◽

Rhythmic Activity ◽

Gamma Oscillations ◽

Underlying Mechanism ◽

Spike Timing ◽

Gamma Band ◽

Fine Tuning ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Wide Range

Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.

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Orientation and color tuning of the human visual gamma rhythm

10.1101/2021.04.23.441193 ◽

2021 ◽

Author(s):

Ye Li ◽

William Bosking ◽

Michael S Beauchamp ◽

Sameer A Sheth ◽

Daniel Yoshor ◽

...

Keyword(s):

Visual Cortex ◽

Large Scale ◽

Brain Activity ◽

Population Level ◽

Gamma Oscillations ◽

Cell Orientation ◽

Population Activity ◽

Color Tuning ◽

Human Visual Cortex ◽

Gamma Rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.

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