scholarly journals Feedback inhibition and its control in an insect olfactory circuit

2019 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N. Aldworth ◽  
Mark A. Stopfer
Keyword(s):  
Large Scale ◽  
Dynamic Range ◽  
Feedback Inhibition ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Intrinsic Properties ◽  
Olfactory Pathway ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

AbstractInhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Analysis of our in vivo recordings and simulations of our model of the olfactory network suggests that GGN extends the dynamic range of KCs, and leads us to predict the existence of a yet undiscovered olfactory pathway. Our analysis of GGN’s intrinsic properties, inputs, and outputs, in vivo and in silico, reveals basic new features of this critical neuron and the olfactory network that surrounds it.

scholarly journals Feedback inhibition and its control in an insect olfactory circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N Aldworth ◽  
Mark A Stopfer
Keyword(s):  
Mushroom Body ◽  
Large Scale ◽  
Feedback Inhibition ◽  
Temporal Patterns ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Olfactory Pathway ◽  
Olfactory Coding ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

Inhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Our simulations suggest that depolarizing GGN at its input branch can globally inhibit KCs several hundred microns away. Our in vivorecordings show that GGN responds to odors with complex temporal patterns of depolarization and hyperpolarization that can vary with odors and across animals, leading our model to predict the existence of a yet-undiscovered olfactory pathway. Our analysis reveals basic new features of GGN and the olfactory network surrounding it.

scholarly journals Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

2018 ◽  
Vol 115 (45) ◽  
pp. 11619-11624 ◽  
Author(s):  
Wei P. Dai ◽  
Douglas Zhou ◽  
David W. McLaughlin ◽  
David Cai
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Orientation Tuning ◽  
Response Properties ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cortical Excitation ◽  
Contrast Invariance

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

scholarly journals 0074 Inhibitory Neurons in the Dorsomedial Medulla Promote REM Sleep

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A30-A30
Author(s):  
J Stucynski ◽  
A Schott ◽  
J Baik ◽  
J Hong ◽  
F Weber ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Nrem Sleep ◽  
Inhibitory Neurons ◽  
Brain State ◽  
Sleep Regulation ◽  
Optogenetic Stimulation ◽  
Electrophysiological Recordings ◽  
Stimulation Of

Abstract Introduction The neural circuits controlling rapid eye movement (REM) sleep, and in particular the role of the medulla in regulating this brain state, remains an active area of study. Previous electrophysiological recordings in the dorsomedial medulla (DM) and electrical stimulation experiments suggested an important role of this area in the control of REM sleep. However the identity of the involved neurons and their precise role in REM sleep regulation are still unclear. Methods The properties of DM GAD2 neurons in mice were investigated through stereotaxic injection of CRE-dependent viruses in conjunction with implantation of electrodes for electroencephalogram (EEG) and electromyogram (EMG) recordings and optic fibers. Experiments included in vivo calcium imaging (fiber photometry) across sleep and wake states, optogenetic stimulation of cell bodies, chemogenetic excitation and suppression (DREADDs), and connectivity mapping using viral tracing and optogenetics. Results Imaging the calcium activity of DM GAD2 neurons in vivo indicates that these neurons are most active during REM sleep. Optogenetic stimulation of DM GAD2 neurons reliably triggered transitions into REM sleep from NREM sleep. Consistent with this, chemogenetic activation of DM GAD2 neurons increased the amount of REM sleep while inhibition suppressed its occurrence and enhanced NREM sleep. Anatomical tracing revealed that DM GAD2 neurons project to several areas involved in sleep / wake regulation including the wake-promoting locus coeruleus (LC) and the REM sleep-suppressing ventrolateral periaquaductal gray (vlPAG). Optogenetic activation of axonal projections from DM to LC, and DM to vlPAG was sufficient to induce REM sleep. Conclusion These experiments demonstrate that DM inhibitory neurons expressing GAD2 powerfully promote initiation of REM sleep in mice. These findings further characterize the dorsomedial medulla as a critical structure involved in REM sleep regulation and inform future investigations of the REM sleep circuitry. Support R01 HL149133

scholarly journals Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions

2017 ◽  
Vol 45 (9) ◽  
pp. 5523-5538 ◽  
Author(s):  
Jorge Vazquez-Anderson ◽  
Mia K. Mihailovic ◽  
Kevin C. Baldridge ◽  
Kristofer G. Reyes ◽  
Katie Haning ◽  
...  
Keyword(s):  
Large Scale ◽  
Biophysical Model ◽  
Hybridization Data

scholarly journals Impact of functional synapse clusters on neuronal response selectivity

2019 ◽  
Author(s):  
Balázs B Ujfalussy ◽  
Judit K Makara
Keyword(s):  
Large Scale ◽  
Pyramidal Neurons ◽  
Neuronal Response ◽  
Cluster Formation ◽  
Biophysical Model ◽  
Functional Clustering ◽  
Synaptic Interactions ◽  
Nonlinear Amplification ◽  
Output Transformation

SummaryClustering of functionally similar synapses in dendrites is thought to affect input-output transformation by inducing dendritic nonlinearities. However, neither the in vivo impact of synaptic clusters on somatic membrane potential (sVm), nor the rules of cluster formation are elucidated. We developed a computational approach to measure the effect of functional synaptic clusters on sVm response of biophysical model CA1 and L2/3 pyramidal neurons to behaviorally relevant in vivo-like inputs. Large-scale dendritic spatial inhomogeneities in synaptic tuning properties did influence sVm, but small synaptic clusters appearing randomly with unstructured connectivity did not. With structured connectivity, ~10-20 synapses per cluster was optimal for clustering-based tuning, but larger responses were achieved by 2-fold potentiation of the same synapses. We further show that without nonlinear amplification of the effect of random clusters, action potential-based, global plasticity rules can not generate functional clustering. Our results suggest that clusters likely form via local synaptic interactions, and have to be moderately large to impact sVm responses.

scholarly journals What Happens with the Circuit in Alzheimer's Disease in Mice and Humans?

2018 ◽  
Vol 41 (1) ◽  
pp. 277-297 ◽  
Author(s):  
Benedikt Zott ◽  
Marc Aurel Busche ◽  
Reisa A. Sperling ◽  
Arthur Konnerth
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Neuronal Damage ◽  
Strong Support ◽  
Underlying Disease ◽  
Electrophysiological Recordings

A major mystery of many types of neurological and psychiatric disorders, such as Alzheimer's disease (AD), remains the underlying, disease-specific neuronal damage. Because of the strong interconnectivity of neurons in the brain, neuronal dysfunction necessarily disrupts neuronal circuits. In this article, we review evidence for the disruption of large-scale networks from imaging studies of humans and relate it to studies of cellular dysfunction in mouse models of AD. The emerging picture is that some forms of early network dysfunctions can be explained by excessively increased levels of neuronal activity. The notion of such neuronal hyperactivity receives strong support from in vivo and in vitro cellular imaging and electrophysiological recordings in the mouse, which provide mechanistic insights underlying the change in neuronal excitability. Overall, some key aspects of AD-related neuronal dysfunctions in humans and mice are strikingly similar and support the continuation of such a translational strategy.

scholarly journals Biologically realistic mean-field models of conductancebased networks of spiking neurons with adaptation

2018 ◽  
Author(s):  
Matteo di Volo ◽  
Alberto Romagnoni ◽  
Cristiano Capone ◽  
Alain Destexhe
Keyword(s):  
Large Scale ◽  
Field Model ◽  
Mean Field ◽  
Inhibitory Neurons ◽  
Activity Levels ◽  
Mean Field Model ◽  
Nonlinear Properties ◽  
Mean Field Models ◽  
The Mean

AbstractAccurate population models are needed to build very large scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of Adaptive exponential Integrate and fire excitatory and inhibitory neurons. Using a Master Equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable to correctly predict the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high and low activity states alternate (UP-DOWN state dynamics), leading to slow oscillations. We conclude that such mean-field models are “biologically realistic” in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large scale models involving multiple brain areas.

scholarly journals Oxygen and seizure dynamics: II. Computational modeling

2014 ◽  
Vol 112 (2) ◽  
pp. 213-223 ◽  
Author(s):  
Yina Wei ◽  
Ghanim Ullah ◽  
Justin Ingram ◽  
Steven J. Schiff
Keyword(s):  
Energy Consumption ◽  
Epileptic Seizures ◽  
Oxygen Metabolism ◽  
Neuronal Firing ◽  
Metabolic Energy ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Electrophysiological Recordings ◽  
Cell Layers ◽  
The Relationship

Electrophysiological recordings show intense neuronal firing during epileptic seizures leading to enhanced energy consumption. However, the relationship between oxygen metabolism and seizure patterns has not been well studied. Recent studies have developed fast and quantitative techniques to measure oxygen microdomain concentration during seizure events. In this article, we develop a biophysical model that accounts for these experimental observations. The model is an extension of the Hodgkin-Huxley formalism and includes the neuronal microenvironment dynamics of sodium, potassium, and oxygen concentrations. Our model accounts for metabolic energy consumption during and following seizure events. We can further account for the experimental observation that hypoxia can induce seizures, with seizures occurring only within a narrow range of tissue oxygen pressure. We also reproduce the interplay between excitatory and inhibitory neurons seen in experiments, accounting for the different oxygen levels observed during seizures in excitatory vs. inhibitory cell layers. Our findings offer a more comprehensive understanding of the complex interrelationship among seizures, ion dynamics, and energy metabolism.

scholarly journals Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

2016 ◽  
Author(s):  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Shabnam Kadir ◽  
Matteo Carandini ◽  
Harris Kenneth D.
Keyword(s):  
Large Scale ◽  
Superior Performance ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Manual Curation ◽  
Electrophysiological Recordings ◽  
Dimensional Approximation ◽  
Low Dimensional ◽  
Channel Electrode

AbstractAdvances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).

scholarly journals Inhibitory control of correlated intrinsic variability in cortical networks

2016 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Michael Okun ◽  
Peter Bartho ◽  
Kenneth Harris ◽  
...  
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
External Noise ◽  
Inhibitory Neurons ◽  
Computational Techniques ◽  
Model Parameters ◽  
Intrinsic Variability ◽  
Cortical Networks

AbstractCortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the wide variety of activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

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