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 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.

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 TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

scholarly journals A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields

2019 ◽  
Author(s):  
Marc Schleiss
Keyword(s):  
Classical Method ◽  
Mathematical Problem ◽  
Grid Cell ◽  
Cascade Model ◽  
Small Scale ◽  
Grid Cells ◽  
Cascade Generator ◽  
Cascade Models ◽  
First Results ◽  
Original Amount

Abstract. Spatial downscaling of rainfall fields is a challenging mathematical problem for which many different types of methods have been proposed. One popular solution consists in redistributing rainfall amounts over smaller and smaller scales by means of a discrete multiplicative random cascade (DMRC). This works well for slowly varying, homogeneous rainfall fields but often fails in the presence of intermittency (i.e., large amounts of zero rainfall values). The most common workaround in this case is to use two separate cascade models, one for the occurrence and another for the intensity. In this paper, a new and simpler approach based on the notion of equal-volume areas (EVAs) is proposed. Unlike classical cascades where rainfall amounts are redistributed over grid cells of equal size, the EVA cascade splits grid cells into areas of different sizes, each of them containing exactly half of the original amount of water. The relative areas of the sub-grid cells are determined by drawing random values from a logit-normal cascade generator model with scale and intensity dependent standard deviation. The process ends when the amount of water in each sub-grid cell is smaller than a fixed bucket capacity, at which point the output of the cascade can be re-sampled over a regular Cartesian mesh. The present paper describes the implementation of the EVA cascade model and gives some first results for 100 selected events in the Netherlands. Performance is assessed by comparing the outputs of the EVA model to bilinear interpolation and to a classical DMRC model based on fixed grid cell sizes. Results show that on average, the EVA cascade outperforms the classical method, producing fields with more realistic distributions, small-scale extremes and spatial structures. Improvements are mostly credited to the higher robustness of the EVA model to the presence of intermittency and to the lower variance of its generator. However, improvements are not systematic and both approaches have their advantages and weaknesses. For example, while the classical cascade tends to overestimate small-scale extremes and variability, the EVA model tends to produce fields that are slightly too smooth and blocky compared with observations.

Optimal signal in sensory neurons under an extended rate coding concept

Biosystems ◽  
2007 ◽  
Vol 89 (1-3) ◽  
pp. 10-15 ◽  
Author(s):  
Petr Lansky ◽  
Priscilla E. Greenwood
Keyword(s):  
Sensory Neurons ◽  
Rate Coding ◽  
Optimal Signal

scholarly journals NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yang Cao ◽  
Wenjian Xu ◽  
Chao Niu ◽  
Xiaochen Bo ◽  
Fei Li
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
R Package ◽  
System Level ◽  
Application Development ◽  
Mathematical Concepts ◽  
Computational Framework ◽  
Knowledge Based ◽  
Different Types ◽  
Gene Regulatory

Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied “basic networks,” measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette.

Parallel Neural Multiprocessing with Gamma Frequency Latencies

2020 ◽  
Vol 32 (9) ◽  
pp. 1635-1663
Author(s):  
Ruohan Zhang ◽  
Dana H. Ballard
Keyword(s):  
Cortical Cell ◽  
Spike Timing ◽  
System Level ◽  
Neural Responses ◽  
Gamma Frequency ◽  
Neural Processes ◽  
Rate Coding ◽  
Millisecond Range ◽  
Cortex System ◽  
Cell Data

The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

On the Role of Biophysical Properties of Cortical Neurons in Binding and Segmentation of Visual Scenes

1999 ◽  
Vol 11 (5) ◽  
pp. 1113-1138 ◽  
Author(s):  
Paul F. M. J. Verschure ◽  
Peter König
Keyword(s):  
Leakage Current ◽  
Cortical Neurons ◽  
Single Cells ◽  
Spatial Dimension ◽  
Differential Regulation ◽  
System Level ◽  
Biophysical Properties ◽  
Moving Stimulus ◽  
Context Dependent ◽  
Different Levels

Neuroscience is progressing vigorously, and knowledge at different levels of description is rapidly accumulating. To establish relationships between results found at these different levels is one of the central challenges. In this simulation study, we demonstrate how microscopic cellular properties, taking the example of the action of modulatory substances onto the membrane leakage current, can provide the basis for the perceptual functions reflected in the macroscopic behavior of a cortical network. In the first part, the action of the modulatory system on cortical dynamics is investigated. First, it is demonstrated that the inclusion of these biophysical properties in a model of the primary visual cortex leads to the dynamic formation of synchronously active neuronal assemblies reflecting a context-dependent binding and segmentation of image components. Second, it is shown that the differential regulation of the leakage current can be used to bias the interactions of multiple cortical modules. This allows the flexible use of different feature domains for scene segmentation. Third, we demonstrate how, within the proposed architecture, the mapping of a moving stimulus onto the spatial dimension of the network results in an increased speed of synchronization. In the second part, we demonstrate how the differential regulation of neuromodulatory activity can be achieved in a self-consistent system. Three different mechanisms are described and investigated. This study thus demonstrates how a modulatory system, affecting the biophysical properties of single cells, can be used to achieve context-dependent processing at the system level.

scholarly journals Multi-step rhodopsin inactivation schemes can account for the size variability of single photon responses in Limulus ventral photoreceptors.

1994 ◽  
Vol 103 (4) ◽  
pp. 691-726 ◽  
Author(s):  
M A Goldring ◽  
J E Lisman
Keyword(s):  
Visual Pigment ◽  
Single Photon ◽  
Exact Distribution ◽  
Cascade Model ◽  
Active State ◽  
Single Photons ◽  
Stochastic Variation ◽  
Cascade Models ◽  
Size Variability ◽  
Single Reaction

Limulus ventral photoreceptors generate highly variable responses to the absorption of single photons. We have obtained data on the size distribution of these responses, derived the distribution predicted from simple transduction cascade models and compared the theory and data. In the simplest of models, the active state of the visual pigment (defined by its ability to activate G protein) is turned off in a single reaction. The output of such a cascade is predicted to be highly variable, largely because of stochastic variation in the number of G proteins activated. The exact distribution predicted is exponential, but we find that an exponential does not adequately account for the data. The data agree much better with the predictions of a cascade model in which the active state of the visual pigment is turned off by a multi-step process.

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