Dual coding hypotheses for neural information representation

Information is represented and processed in neural systems in various ways. The rate coding, population coding, and temporal coding are typical examples of representation. It is a hot issue in neuroscience what kinds of coding is used in real neural systems. Different regions of the brain may resort to different coding strategies. Moreover, recent studies suggest the possibility of dual or multiple codes, in which different modes of information are embedded in one neural system. The present paper reviews various possibilities of neural codes focusing on dual codes.     

条件化恐惧记忆消退的神经振荡机制

恐惧作为个体应对内外界危险因素形成的自我保护机制的一部分,在生物体的生存中发挥着重要作用.但过度的恐惧不仅对个体生存无益,反而易引发创伤后应激障碍、焦虑等精神疾病,严重影响个体生活质量.临床上通常采用基于行为学研究结果的暴露疗法对恐惧相关疾病进行治疗,然而在患者处于治疗环境之外的时候,上述症状经常会复发.因此,解析恐惧记忆相关神经环路内信息处理的神经机制,对于理解这些疾病的发生发展,寻求切实有效的治疗方案至关重要.大量研究表明与恐惧记忆消退相关的脑区主要涉及杏仁核、内侧前额叶和海马.在恐惧消退的过程中,这3个脑区表现出特定的神经振荡模式,而且这些活动也具有同步性,构成了恐惧记忆成功消退的神经基础.未来可利用基于神经神经振荡的无创性脑刺激手段干预恐惧记忆消退的神经环路,以促进恐惧记忆的消退并避免复发,为恐惧相关障碍的临床治疗提供重要的科学依据.     

药物成瘾及成瘾记忆的研究现状

本文在介绍药物成瘾与学习和记忆密切相关的神经回路及共同分子机制的基础上,围绕学习和记忆在药物成瘾中的作用,综述了关联性学习与复吸,关联性学习与敏化,异常关联性学习与强迫性用药行为,关联性学习及成瘾记忆与成瘾,多重记忆系统与成瘾的发生发展等方面的研究进展,并强调了突触可塑性及成瘾记忆在药物成瘾中的重要性。在此基础上提出：作为慢性脑病的药物成瘾的形成过程的重要特征是它包含着信息的特殊学习类型。药物成瘾与依赖于多巴胺的关联性学习紊乱有密切关系。海马可能在成瘾中扮演重要角色。     

The search for a hippocampal engram

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory.     

认知地图的神经环路基础

空间记忆是人类认识世界和改造世界的基本认知能力,与我们的生活息息相关.无论是寻找常用的生活物件,如钥匙和手机,还是外出上班、购物和约会,都依赖我们对周围环境的记忆.截止到目前已有大量研究从不同水平探讨大脑如何表征其周围环境,但仍然有很多未解的问题.本文系统综述了基于脑成像和神经电生理技术开展的空间记忆研究进展.通过梳理以往研究中有关生物体在构建认知地图的神经结构和神经活动规律,提出了海马结构和新皮层对空间记忆的编码环路和表征机制,并在此基础上对未来研究进行了展望.     

A neural circuit model for changing the amount of information maintained in short-term memory depending on stimuli relationships

We propose a neural circuit model of changes in amount of information maintained in short-term memory depending on stimuli relationships. The relationships between stimuli are represented by the synchronous firings of overlapping neuronal groups for semantically related stimuli and the excitatory mutual connections for semantically unrelated but simultaneously presented stimuli. We conduct computer simulations to confirm our proposed neural circuit model. The resultant numbers of stored informational input patterns are almost consistent with the maximum numbers in the psychological experiments for both semantically related and unrelated stimuli. This agreement with the psychological experiments suggests that the structure and informational representation of the proposed model are appropriate.     

Neural network firing-rate models on integral form

Firing-rate models describing neural-network activity can be formulated in terms of differential equations for the synaptic drive from neurons. Such models are typically derived from more general models based on Volterra integral equations assuming exponentially decaying temporal coupling kernels describing the coupling of pre- and postsynaptic activities. Here we study models with other choices of temporal coupling kernels. In particular, we investigate the stability properties of constant solutions of two-population Volterra models by studying the equilibrium solutions of the corresponding autonomous dynamical systems, derived using the linear chain trick, by means of the Routh–Hurwitz criterion. In the four investigated synaptic-drive models with identical equilibrium points we find that the choice of temporal coupling kernels significantly affects the equilibrium-point stability properties. A model with an α-function replacing the standard exponentially decaying function in the inhibitory coupling kernel is in most of our examples found to be most prone to instability, while the opposite situation with an α-function describing the excitatory kernel is found to be least prone to instability. The standard model with exponentially decaying coupling kernels is typically found to be an intermediate case. We further find that stability is promoted by increasing the weight of self-inhibition or shortening the time constant of the inhibition.     

综述: 脑功能原理：神经细胞编程和信息储存

大脑采集感觉信息、整合认知和控制行为过程,这些任务的实现依赖于神经细胞及其环路的信息储存与编程.澄清神经信息编程与储存的原理是研制拟脑计算机的基础.本文将基于神经细胞的模拟-数字信号转换、数字信号兼容式输出以及新信息储存与提取等方面的研究揭示脑认知原理.     

The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback

According to a popular hypothesis, short-term memories are stored as persistent neural activity maintained by synaptic feedback loops. This hypothesis has been formulated mathematically in a number of recurrent network models. Here we study an abstraction of these models, a single neuron with a synapse onto itself, or autapse. This abstraction cannot simulate the way in which persistent activity patterns are distributed over neural populations in the brain. However, with proper tuning of parameters, it does reproduce the continuously graded, or analog, nature of many examples of persistent activity. The conditions for tuning are derived for the dynamics of a conductance-based model neuron with a slow excitatory autapse. The derivation uses the method of averaging to approximate the spiking model with a nonspiking, reduced model. Short-term analog memory storage is possible if the reduced model is approximately linear and if its feedforward bias and autapse strength are precisely tuned.     

The differential requirement of mushroom body α/β subdivisions in long-term memory retrieval in Drosophila

The mushroom body (MB), a bilateral brain structure possessing about 2000-2500 neurons per hemisphere, plays a central role in olfactory learning and memory in Drosophila melanogaster. Extensive studies have demonstrated that three major types of MB neurons (α/β, α’/β’ and γ) exhibit distinct functions in memory processing, including the critical role of approximately 1000 MB α/β neurons in retrieving long-term memory. Inspired by recent findings that MB α/β neurons can be further divided into three subdivisions (surface, posterior and core) and wherein the α/β core neurons play an permissive role in long-term memory consolidation, we examined the functional differences of all the three morphological subdivisions of MB α/β by temporally precise manipulation of their synaptic outputs during long-term memory retrieval. We found the normal neurotransmission from a combination of MB α/β surface and posterior neurons is necessary for retrieving both aversive and appetitive long-term memory, whereas output from MB α/β posterior or core subdivision alone is dispensable. These results imply a specific requirement of about 500 MB α/β neurons in supporting long-term memory retrieval and a further functional partitioning for memory processing within the MB α/β region.     

Modularity and robustness of frontal cortical networks

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Fading memory and kernel properties of generic cortical microcircuit models

It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp invariant speech recognition. This is possible because such circuits have an inherent tendency to integrate incoming information in such a way that simple linear readouts can be trained to transform the current circuit activity into the target output for a very large number of computational tasks. Consequently we propose to analyze circuits of spiking neurons in terms of their roles as analog fading memory and non-linear kernels, rather than as implementations of specific computational operations and algorithms. This article is a sequel to [W. Maass, T. Natschl?ger, H. Markram, Real-time computing without stable states: a new framework for neural computation based on perturbations, Neural Comput. 14 (11) (2002) 2531-2560, Online available as #130 from: ], and contains new results about the performance of generic neural microcircuit models for the recognition of speech that is subject to linear and non-linear time-warps, as well as for computations on time-varying firing rates. These computations rely, apart from general properties of generic neural microcircuit models, just on capabilities of simple linear readouts trained by linear regression. This article also provides detailed data on the fading memory property of generic neural microcircuit models, and a quick review of other new results on the computational power of such circuits of spiking neurons.     

Spike-time reliability of layered neural oscillator networks

We study the reliability of layered networks of coupled “type I” neural oscillators in response to fluctuating input signals. Reliability means that a signal elicits essentially identical responses upon repeated presentations, regardless of the network’s initial condition. We study reliability on two distinct scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within a network, and pooled-response reliability, which concerns the repeatability of total synaptic outputs from a subpopulation of the neurons in a network. We find that neuronal reliability depends strongly both on the overall architecture of a network, such as whether it is arranged into one or two layers, and on the strengths of the synaptic connections. Specifically, for the type of single-neuron dynamics and coupling considered, single-layer networks are found to be very reliable, while two-layer networks lose their reliability with the introduction of even a small amount of feedback. As expected, pooled responses for large enough populations become more reliable, even when individual neurons are not. We also study the effects of noise on reliability, and find that noise that affects all neurons similarly has much greater impact on reliability than noise that affects each neuron differently. Qualitative explanations are proposed for the phenomena observed.