scholarly journals Sparse Bursts Optimize Information Transmission in a Multiplexed Neural Code

2017 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Single Firing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

AbstractMany cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing rate output, which collapses all input streams into one. We propose that neurons can simultaneously represent multiple input streams by using a novel code that distinguishes single spikes and bursts at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. It also suggests specific connectivity patterns that allows to demultiplex this information. These connectivity patterns can be used by the nervous system to maintain optimal multiplexing. Contrary to firing rate coding, our findings indicate that a single neural ensemble can communicate multiple independent signals to different targets.

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

Possible Effects of Depolarizing GABAA Conductance on the Neuronal Input–Output Relationship: A Modeling Study

2005 ◽  
Vol 93 (6) ◽  
pp. 3504-3523 ◽  
Author(s):  
Kenji Morita ◽  
Kunichika Tsumoto ◽  
Kazuyuki Aihara
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Reversal Potential ◽  
Pyramidal Cells ◽  
Resting Potential ◽  
Input Output ◽  
Wide Range ◽  
The Relationship

Recent in vitro experiments revealed that the GABAA reversal potential is about 10 mV higher than the resting potential in mature mammalian neocortical pyramidal cells; thus GABAergic inputs could have facilitatory, rather than inhibitory, effects on action potential generation under certain conditions. However, how the relationship between excitatory input conductances and the output firing rate is modulated by such depolarizing GABAergic inputs under in vivo circumstances has not yet been understood. We examine herewith the input–output relationship in a simple conductance-based model of cortical neurons with the depolarized GABAA reversal potential, and show that a tonic depolarizing GABAergic conductance up to a certain amount does not change the relationship between a tonic glutamatergic driving conductance and the output firing rate, whereas a higher GABAergic conductance prevents spike generation. When the tonic glutamatergic and GABAergic conductances are replaced by in vivo–like highly fluctuating inputs, on the other hand, the effect of depolarizing GABAergic inputs on the input–output relationship critically depends on the degree of coincidence between glutamatergic input events and GABAergic ones. Although a wide range of depolarizing GABAergic inputs hardly changes the firing rate of a neuron driven by noncoincident glutamatergic inputs, a certain range of these inputs considerably decreases the firing rate if a large number of driving glutamatergic inputs are coincident with them. These results raise the possibility that the depolarized GABAA reversal potential is not a paradoxical mystery, but is instead a sophisticated device for discriminative firing rate modulation.

scholarly journals Separating Spike Count Correlation from Firing Rate Correlation

2016 ◽  
Vol 28 (5) ◽  
pp. 849-881 ◽  
Author(s):  
Giuseppe Vinci ◽  
Valérie Ventura ◽  
Matthew A. Smith ◽  
Robert E. Kass
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Experimental Manipulation ◽  
Response Variability ◽  
Spike Count ◽  
Firing Rates ◽  
Area V4 ◽  
Rate Correlation ◽  
Over Time

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.

scholarly journals Synchrony-Division Neural Multiplexing: An Encoding Model

2021 ◽  
Author(s):  
Mohammad R. Rezaei ◽  
Milos R. Popovic ◽  
Steven A Prescott ◽  
Milad Lankarany
Keyword(s):  
Sensory Neurons ◽  
Cortical Neurons ◽  
Cascade Model ◽  
System Level ◽  
Computational Framework ◽  
Multiple Features ◽  
Neural Ensemble ◽  
Cascade Models ◽  
Rate Coding ◽  
Feature Estimation

Cortical neurons receive mixed information from collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated that the time underlying the onset-offset of a tactile stimulus and its varying intensity can be respectively represented by synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding of how an ensemble of homogeneous neurons enables SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble can perform two different functions, namely, temporal- and rate- coding, simultaneously.

scholarly journals Up and Down States of Cortical Neurons in Focal Limbic Seizures

2019 ◽  
Vol 30 (5) ◽  
pp. 3074-3086 ◽  
Author(s):  
Zongwei Yue ◽  
Isaac G Freedman ◽  
Peter Vincent ◽  
John P Andrews ◽  
Christopher Micek ◽  
...  
Keyword(s):  
Membrane Potential ◽  
Firing Rate ◽  
Cortical Neurons ◽  
Input Resistance ◽  
Loss Of Consciousness ◽  
Cortical Function ◽  
Limbic Seizures ◽  
Electrophysiological Properties ◽  
Up And Down States

Abstract Recent work suggests an important role for cortical–subcortical networks in seizure-related loss of consciousness. Temporal lobe seizures disrupt subcortical arousal systems, which may lead to depressed cortical function and loss of consciousness. Extracellular recordings show ictal neocortical slow waves at about 1 Hz, but it is not known whether these simply represent seizure propagation or alternatively deep sleep-like activity, which should include cortical neuronal Up and Down states. In this study, using in vivo whole-cell recordings in a rat model of focal limbic seizures, we directly examine the electrophysiological properties of cortical neurons during seizures and deep anesthesia. We found that during seizures, the membrane potential of frontal cortical secondary motor cortex layer 5 neurons fluctuates between Up and Down states, with decreased input resistance and increased firing rate in Up states when compared to Down states. Importantly, Up and Down states in seizures are not significantly different from those in deep anesthesia, in terms of membrane potential, oscillation frequency, firing rate, and input resistance. By demonstrating these fundamental similarities in cortical electrophysiology between deep anesthesia and seizures, our results support the idea that a state of decreased cortical arousal may contribute to mechanisms of loss of consciousness during seizures.

scholarly journals Estimating Instantaneous Irregularity of Neuronal Firing

2009 ◽  
Vol 21 (7) ◽  
pp. 1931-1951 ◽  
Author(s):  
Takeaki Shimokawa ◽  
Shigeru Shinomoto
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Bayesian Method ◽  
Local Variation ◽  
Neuronal Firing ◽  
Empirical Bayesian ◽  
Low Degree ◽  
Firing Irregularity ◽  
Spike Sequence

Cortical neurons in vivo had been regarded as Poisson spike generators that convey no information other than the rate of random firing. Recently, using a metric for analyzing local variation of interspike intervals, researchers have found that individual neurons express specific patterns in generating spikes, which may symbolically be termed regular, random, or bursty, rather invariantly in time. In order to study the dynamics of firing patterns in greater detail, we propose here a Bayesian method for estimating firing irregularity and the firing rate simultaneously for a given spike sequence, and we implement an algorithm that may render the empirical Bayesian estimation practicable for data comprising a large number of spikes. Application of this method to electrophysiological data revealed a subtle correlation between the degree of firing irregularity and the firing rate for individual neurons. Irregularity of firing did not deviate greatly around the low degree of dependence on the firing rate and remained practically unchanged for individual neurons in the cortical areas V1 and MT, whereas it fluctuated greatly in the lateral geniculate nucleus of the thalamus. This indicates the presence and absence of autocontrolling mechanisms for maintaining patterns of firing in the cortex and thalamus, respectively.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals TRAF3 mediates neuronal apoptosis in early brain injury following subarachnoid hemorrhage via targeting TAK1-dependent MAPKs and NF-κB pathways

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yan Zhou ◽  
Tao Tao ◽  
Guangjie Liu ◽  
Xuan Gao ◽  
Yongyue Gao ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Brain Injury ◽  
Therapeutic Target ◽  
Cortical Neurons ◽  
Neuronal Apoptosis ◽  
Early Brain Injury ◽  
Neurological Deficits ◽  
Human Brain Tissue

AbstractNeuronal apoptosis has an important role in early brain injury (EBI) following subarachnoid hemorrhage (SAH). TRAF3 was reported as a promising therapeutic target for stroke management, which covered several neuronal apoptosis signaling cascades. Hence, the present study is aimed to determine whether downregulation of TRAF3 could be neuroprotective in SAH-induced EBI. An in vivo SAH model in mice was established by endovascular perforation. Meanwhile, primary cultured cortical neurons of mice treated with oxygen hemoglobin were applied to mimic SAH in vitro. Our results demonstrated that TRAF3 protein expression increased and expressed in neurons both in vivo and in vitro SAH models. TRAF3 siRNA reversed neuronal loss and improved neurological deficits in SAH mice, and reduced cell death in SAH primary neurons. Mechanistically, we found that TRAF3 directly binds to TAK1 and potentiates phosphorylation and activation of TAK1, which further enhances the activation of NF-κB and MAPKs pathways to induce neuronal apoptosis. Importantly, TRAF3 expression was elevated following SAH in human brain tissue and was mainly expressed in neurons. Taken together, our study demonstrates that TRAF3 is an upstream regulator of MAPKs and NF-κB pathways in SAH-induced EBI via its interaction with and activation of TAK1. Furthermore, the TRAF3 may serve as a novel therapeutic target in SAH-induced EBI.

scholarly journals Cortical neurons exhibit diverse myelination patterns that scale between mouse brain regions and regenerate after demyelination

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cody L. Call ◽  
Dwight E. Bergles
Keyword(s):  
Cortical Neurons ◽  
Regional Differences ◽  
Brain Regions ◽  
Axon Diameter ◽  
Neuronal Identity ◽  
Cortical Areas ◽  
Myelin Sheaths ◽  
Parvalbumin Interneurons ◽  
Selection Of

ABSTRACTAxons in the cerebral cortex show a broad range of myelin coverage. Oligodendrocytes establish this pattern by selecting a cohort of axons for myelination; however, the distribution of myelin on distinct neurons and extent of internode replacement after demyelination remain to be defined. Here we show that myelination patterns of seven distinct neuron subtypes in somatosensory cortex are influenced by both axon diameter and neuronal identity. Preference for myelination of parvalbumin interneurons was preserved between cortical areas with varying myelin density, suggesting that regional differences in myelin abundance arises through local control of oligodendrogenesis. By imaging loss and regeneration of myelin sheaths in vivo we show that myelin distribution on individual axons was altered but overall myelin content on distinct neuron subtypes was restored. Our findings suggest that local changes in myelination are tolerated, allowing regenerated oligodendrocytes to restore myelin content on distinct neurons through opportunistic selection of axons.

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