scholarly journals Weaker feedforward inhibition accounts for less pronounced thalamocortical response transformation in mouse vs. rat barrels

2013 ◽  
Vol 110 (10) ◽  
pp. 2378-2392 ◽  
Author(s):  
E. E. Kwegyir-Afful ◽  
H. T. Kyriazi ◽  
D. J. Simons
Keyword(s):  
Barrel Cortex ◽  
Simulated Data ◽  
Inhibitory Neurons ◽  
Temporal Window ◽  
Thalamic Neurons ◽  
Preferred Direction ◽  
Common Motif ◽  
Fast Spike ◽  
Response Transformation ◽  
Feedforward Inhibition

Feedforward inhibition is a common motif of thalamocortical circuits. Strong engagement of inhibitory neurons by thalamic inputs enhances response differentials between preferred and nonpreferred stimuli. In rat whisker-barrel cortex, robustly driven inhibitory barrel neurons establish a brief epoch during which synchronous or near-synchronous thalamic firing produces larger responses to preferred stimuli, such as high-velocity deflections of the principal whisker in a preferred direction. Present experiments in mice show that barrel neuron responses to preferred vs. nonpreferred stimuli differ less than in rats. In addition, fast-spike units, thought to be inhibitory barrel neurons, fire less robustly to whisker stimuli in mice than in rats. Analyses of real and simulated data indicate that mouse barrel circuitry integrates thalamic inputs over a broad temporal window, and that, as a consequence, responses of barrel neurons are largely similar to those of thalamic neurons. Results are consistent with weaker feedforward inhibition in mouse barrels. Differences in thalamocortical circuitry between mice and rats may reflect mechanical properties of the whiskers themselves.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

2021 ◽  
Vol 101 (1) ◽  
pp. 353-415
Author(s):  
Jochen F. Staiger ◽  
Carl C. H. Petersen
Keyword(s):  
Barrel Cortex ◽  
Specific Activity ◽  
Sensory Information ◽  
Inhibitory Neurons ◽  
Future Research ◽  
Cell Type ◽  
Molecular Features ◽  
Somatotopic Map ◽  
Cell Type Specific ◽  
The Impact

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a ‘barrel’ (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

scholarly journals Target Interneuron Preference in Thalamocortical Pathways Determines the Temporal Structure of Cortical Responses

2018 ◽  
Vol 29 (7) ◽  
pp. 2815-2831 ◽  
Author(s):  
Y Audrey Hay ◽  
Jérémie Naudé ◽  
Philippe Faure ◽  
Bertrand Lambolez
Keyword(s):  
Temporal Structure ◽  
Sensory Information ◽  
Response Duration ◽  
Cortical Response ◽  
Inhibitory Neurons ◽  
Thalamic Nuclei ◽  
Response Dynamics ◽  
Mean Field Model ◽  
Local Circuits ◽  
Feedforward Inhibition

Abstract Sensory processing relies on fast detection of changes in environment, as well as integration of contextual cues over time. The mechanisms by which local circuits of the cerebral cortex simultaneously perform these opposite processes remain obscure. Thalamic “specific” nuclei relay sensory information, whereas “nonspecific” nuclei convey information on the environmental and behavioral contexts. We expressed channelrhodopsin in the ventrobasal specific (sensory) or the rhomboid nonspecific (contextual) thalamic nuclei. By selectively activating each thalamic pathway, we found that nonspecific inputs powerfully activate adapting (slow-responding) interneurons but weakly connect fast-spiking interneurons, whereas specific inputs exhibit opposite interneuron preference. Specific inputs thereby induce rapid feedforward inhibition that limits response duration, whereas, in the same cortical area, nonspecific inputs elicit delayed feedforward inhibition that enables lasting recurrent excitation. Using a mean field model, we confirm that cortical response dynamics depends on the type of interneuron targeted by thalamocortical inputs and show that efficient recruitment of adapting interneurons prolongs the cortical response and allows the summation of sensory and contextual inputs. Hence, target choice between slow- and fast-responding inhibitory neurons endows cortical networks with a simple computational solution to perform both sensory detection and integration.

scholarly journals Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
B Semihcan Sermet ◽  
Pavel Truschow ◽  
Michael Feyerabend ◽  
Johannes M Mayrhofer ◽  
Tess B Oram ◽  
...  
Keyword(s):  
Thalamic Nucleus ◽  
Barrel Cortex ◽  
Sensory Information ◽  
Cell Types ◽  
Excitatory Input ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Thalamic Nuclei ◽  
Thalamic Input ◽  
Layer 4

Mouse primary somatosensory barrel cortex (wS1) processes whisker sensory information, receiving input from two distinct thalamic nuclei. The first-order ventral posterior medial (VPM) somatosensory thalamic nucleus most densely innervates layer 4 (L4) barrels, whereas the higher-order posterior thalamic nucleus (medial part, POm) most densely innervates L1 and L5A. We optogenetically stimulated VPM or POm axons, and recorded evoked excitatory postsynaptic potentials (EPSPs) in different cell-types across cortical layers in wS1. We found that excitatory neurons and parvalbumin-expressing inhibitory neurons received the largest EPSPs, dominated by VPM input to L4 and POm input to L5A. In contrast, somatostatin-expressing inhibitory neurons received very little input from either pathway in any layer. Vasoactive intestinal peptide-expressing inhibitory neurons received an intermediate level of excitatory input with less apparent layer-specificity. Our data help understand how wS1 neocortical microcircuits might process and integrate sensory and higher-order inputs.

scholarly journals Vigilance and behavioral state-dependent modulation of cortical neuronal activity throughout the sleep/wake cycle

2021 ◽  
Author(s):  
Aurelie Brecier ◽  
Melodie Borel ◽  
Nadia Urbain ◽  
Luc J Gentet
Keyword(s):  
Neuronal Activity ◽  
Rem Sleep ◽  
Barrel Cortex ◽  
Field Potential ◽  
Nrem Sleep ◽  
Inhibitory Neurons ◽  
Sleep Stages ◽  
Theta Waves ◽  
Physiological Diversity ◽  
Activity Dynamics

GABAergic inhibitory neurons, through their molecular, anatomic and physiological diversity, provide a substrate for the modulation of ongoing cortical circuit activity throughout the sleep-wake cycle. Here, we investigated neuronal activity dynamics of parvalbumin (PV), vasoactive intestinal polypeptide (VIP) and somatostatin (SST) neurons in naturally-sleeping head-restrained mice at the level of layer 2/3 of the primary somatosensory barrel cortex of mice. Through calcium-imaging and targeted single-unit loose-patch or whole-cell recordings, we found that PV action potential (AP) firing activity was largest during both NREM (non-rapid eye movement) and REM sleep stages, that VIP neurons were activated during REM sleep and that the overall activity of SST neurons remained stable throughout the sleep/wake cycle. Analysis of neuronal activity dynamics uncovered rapid decreases in PV cell firing at wake onset followed by a progressive recovery during wake. Simultaneous local field potential (LFP) recordings further revealed that, except for SST neurons, a large proportion of neurons were modulated by ongoing delta and theta waves. During NREM sleep spindles, PV and SST activity increased and decreased, respectively. Finally, we uncovered the presence of whisking behavior in mice during REM sleep and show that the activity of VIP and SST is differentially modulated during awake and sleeping whisking bouts, which may provide a neuronal substrate for internal brain representations occurring during sleep.

scholarly journals The reliability of conditional Granger causality analysis in the time domain

2018 ◽  
Author(s):  
Raffaella Franciotti ◽  
Nicola Walter Falasca
Keyword(s):  
Time Domain ◽  
Simulated Data ◽  
Information Flows ◽  
Connection Strength ◽  
True Positive ◽  
Causality Analysis ◽  
Temporal Window ◽  
Time Points ◽  
Electrophysiological Signals ◽  
The Time Domain

Background. Brain function requires a coordinated flow of information among functionally specialized areas. Quantitative methods provide a multitude of metrics to quantify the oscillatory interactions measured by invasive or non-invasive recording techniques. Granger causality (G-causality) has emerged as a useful tool to investigate the directions of information flows, but challenges remain on the ability of G-causality when applying on biological data. In addition it is not clear if G-causality can distinguish between direct and indirect influences and if G-causality reliability was related to the strength of the neural interactions. Methods. In this study time domain G-causality connectivity analysis was performed on simulated electrophysiological signals. A network of 19 nodes was constructed with a designed structure of direct and indirect information flows among nodes, which we referred to as a ground truth structure. G-causality reliability was evaluated on two sets of simulated data while varying one of the following variables: the number of time points in the temporal window, the lags between causally interacting nodes, the connection strength between the links, and the noise. Results. Results showed that the number of time points in the temporal window affects G-causality reliability substantially. A large number of time points could decrease the reliability of the G-causality results, increasing the number of false positive (type I errors). In the presence of stationary signals, G-causality results are reliable showing all true positive links (absence of type II errors), when the underlying structure has the delays between the interacting nodes lower than 100 ms, the connection strength higher to 0.1 time the amplitude of the driver signal and good signal to noise ratio. Finally, indirect links were revealed by G-causality analysis for connection strength higher than the direct link and lags lower than the direct link. Discussion. Conditional multivariate vector autoregressive model was applied to 19 virtual time series to estimate the reliability of the G-causality analysis on the identification of the true positive link, on the presence of spurious links and on the effects of indirect links. Simulated data revealed that weak direct but not weak indirect causal effects could be identified by G-causality analysis. These results demonstrate a good sensitivity and specificity of the conditional G-causality analysis in the time domain when applied on covariance stationary, non-correlated electrophysiological signals.

Activation of a wide-spread network of inhibitory neurons in barrel cortex

1997 ◽  
Vol 14 (2) ◽  
pp. 138-147 ◽  
Author(s):  
James Mccasland ◽  
Lyndon Hibbard ◽  
Robert Rhoades ◽  
Thomas Woolsey
Keyword(s):  
Barrel Cortex ◽  
Inhibitory Neurons ◽  
Wide Spread

Thalamocortical Specificity and the Synthesis of Sensory Cortical Receptive Fields

2005 ◽  
Vol 94 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Jose-Manuel Alonso ◽  
Harvey A. Swadlow
Keyword(s):  
Visual Cortex ◽  
Barrel Cortex ◽  
Receptive Fields ◽  
High Specificity ◽  
Inhibitory Neurons ◽  
Simple Cells ◽  
Layer 4 ◽  
Different Response ◽  
Cortical Receptive Fields ◽  
Visual Cortical

A persistent and fundamental question in sensory cortical physiology concerns the manner in which receptive fields of layer-4 neurons are synthesized from their thalamic inputs. According to a hierarchical model proposed more than 40 years ago, simple receptive fields in layer 4 of primary visual cortex originate from the convergence of highly specific thalamocortical inputs (e.g., geniculate inputs with on-center receptive fields overlap the on subregions of layer 4 simple cells). Here, we summarize studies in the visual cortex that provide support for this high specificity of thalamic input to visual cortical simple cells. In addition, we review studies of GABAergic interneurons in the somatosensory “barrel” cortex with receptive fields that are generated by a very different mechanism: the nonspecific convergence of thalamic inputs with different response properties. We hypothesize that these 2 modes of thalamocortical connectivity onto subpopulations of excitatory and inhibitory neurons constitute a general feature of sensory neocortex and account for much of the diversity seen in layer-4 receptive fields.

Estimation of Time-Dependent Input from Neuronal Membrane Potential

2011 ◽  
Vol 23 (12) ◽  
pp. 3070-3093 ◽  
Author(s):  
Ryota Kobayashi ◽  
Shigeru Shinomoto ◽  
Petr Lansky
Keyword(s):  
Membrane Potential ◽  
Empirical Bayes ◽  
Simulated Data ◽  
Time Dependent ◽  
Inhibitory Neurons ◽  
Likelihood Method ◽  
Neuronal Membrane ◽  
Neuron Models ◽  
Firing Rates ◽  
Mean And Variance

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.

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