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Benefits of noise in the transmission and processing of time-dependent stimuli by populations of sensory neurons

噪声在感觉神经元群传输和处理时间依赖性刺激方面的好处

基本信息

批准号：
250455001
负责人：
Professor Dr. Benjamin Lindner
金额：
--
依托单位：
Institut für Physik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/250455001?language=en
关键词：
Benefits noise transmission processing time

项目摘要

Neurons in the brain encode information about sensory signals in sequences of stereotypic pulses, called spike trains. In the first period of this project, we studied this encoding process in populations of uncoupled cells that are driven by a common stimulus and are also subject to some intrinsic noise and heterogeneity (a variability of their cellular properties). We derived mathematical formulas for the statistics of the population, in particular, its activity and its partial synchronous output and showed that noise can be beneficial in different respects for the transmission and processing of time-dependent stimuli. In particular, we demonstrated in a successful collaboration of experiment and theory, that both intrinsic noise and leak currents are necessary for a synchrony code to work. In the continuation of the project, analysis of information encoding in the synchronous activity will be extended to the much broader class of spiking neurons with recurrent feedback and address two basic situations. In problem A, a recurrent network in the asynchronous irregular state is driven by a common broadband input signal; in problem B, a three-layered network of populations is considered, in which the first population of uncoupled neurons receives a common broadband input, the second population is subject to feedforward input from the first and to delayed inhibitory feedback from the third population. Problem A is a generic setup that applies to a single propagation step in many cortical pathways, while problem B is more typical for the sensory periphery and reflects in particular the architecture of the electrosensory system in weakly electric fish (a model organism, for which we have access to spike data). For the two models, I want to study (i) whether the recurrent feedback can potentially enhance the synchrony coding, i.e. sharpen the information high-pass filter seen in the synchronous output of a population of uncoupled cells; (ii) what is the effect of network noise and neural heterogeneity on information transmission in the case of a strong recurrent feedback. Besides these general theoretical aspects, the project is devoted to a specific phenomenon: the transmission and processing of faint signals in the weakly electric fish.

大脑中的神经元将感觉信号的信息编码成一系列刻板脉冲，称为脉冲序列。在该项目的第一阶段，我们研究了由共同刺激驱动的非偶联细胞群体中的这种编码过程，这些细胞也受到一些固有噪声和异质性（其细胞特性的可变性）的影响。我们推导了种群统计的数学公式，特别是它的活动和部分同步输出，并表明噪声在不同方面对时间依赖性刺激的传输和处理是有益的。特别是，我们在实验和理论的成功合作中证明了同步代码工作所必需的固有噪声和泄漏电流。在项目的继续，同步活动中的信息编码分析将扩展到更广泛的一类具有周期性反馈的尖峰神经元，并解决两种基本情况。在问题A中，一个处于异步不规则状态的循环网络由一个公共宽带输入信号驱动；在问题B中，考虑了一个三层的种群网络，其中第一群不耦合的神经元接收公共宽带输入，第二群受第一群的前馈输入和第三群的延迟抑制反馈的影响。问题A是一种通用设置，适用于许多皮质通路中的单个传播步骤，而问题B更典型地适用于感觉外围，并特别反映了弱电鱼（一种模式生物，我们可以获得脉冲数据）的电感觉系统结构。对于这两个模型，我想研究(I)循环反馈是否可以潜在地增强同步编码，即锐化在一群解耦细胞的同步输出中看到的信息高通滤波器；（ii）在强循环反馈的情况下，网络噪声和神经异质性对信息传递的影响是什么？除了这些一般的理论方面，该项目致力于一个具体的现象：弱电鱼微弱信号的传输和处理。