Robust short-term memory without synaptic learning

Short-term memory in the brain cannot in general be explained the way long-term memory can -- as a gradual modification of synaptic weights -- since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.Comment: 20 pages, 9 figures. Amended to include section on spiking neurons, with general rewrit

Nonlinear preferential rewiring in fixed-size networks as a diffusion process

We present an evolving network model in which the total numbers of nodes and edges are conserved, but in which edges are continuously rewired according to nonlinear preferential detachment and reattachment. Assuming power-law kernels with exponents alpha and beta, the stationary states the degree distributions evolve towards exhibit a second order phase transition - from relatively homogeneous to highly heterogeneous (with the emergence of starlike structures) at alpha = beta. Temporal evolution of the distribution in this critical regime is shown to follow a nonlinear diffusion equation, arriving at either pure or mixed power-laws, of exponents -alpha and 1-alpha

Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances—that naturally balances the network with excitatory and inhibitory synapses—and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.The authors acknowledge support from the Spanish Ministry of economy and competitiveness under the project FIS2013-43201-P

Un método para explorar lo complejo

págs.: 125-134Capítulo incluido en el libro: Complejidad y Ciencias Sociales. Esteban Ruiz Ballesteros y José Luis Solana Ruiz (Editores). Sevilla: Universidad Internacional de Andalucía, 2013. ISBN 978-84-7993-231-2. Enlace: http://hdl.handle.net/10334/362

Instability of attractors in auto–associative networks with bio–inspired fast synaptic noise

We studied auto–associative networks in which synapses are noisy on a time scale much shorter that the one for the neuron dynamics. In our model a presynaptic noise causes postsynaptic depression as recently ob- served in neurobiological systems. This results in a nonequilibrium condi- tion in which the network sensitivity to an external stimulus is enhanced. In particular, the fixed points are qualitatively modified, and the system may easily scape from the attractors. As a result, in addition to pattern recognition, the model is useful for class identification and categorization.MCyT and FEDER (project No. BFM2001- 2841 and Ram´on y Cajal contract

Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms

Electroencephalography (EEG) monitors -by either intrusive or noninvasive electrodes-time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior similar to alpha, beta, gamma and other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans. (C) 2019 Elsevier B.V. All rights reserved.We acknowledge the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion"(AEI) for financial support under grant FIS2017-84256-P (FEDER funds)

The entropic origin of disassortativity in complex networks

Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated, i.e. disassortative? With a view to answering this long-standing question, we define a general class of degree-degree correlated networks and obtain the associated Shannon entropy as a function of parameters. It turns out that the maximum entropy does not typically correspond to uncorrelated networks, but to either assortative (correlated) or disassortative (anticorrelated) ones. More specifically, for highly heterogeneous (scale-free) networks, the maximum entropy principle usually leads to disassortativity, providing a parsimonious explanation to the question above. Furthermore, by comparing the correlations measured in some real-world networks with those yielding maximum entropy for the same degree sequence, we find a remarkable agreement in various cases. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. In cases in which empirical observations deviate from the neutral predictions -- as happens in social networks -- one can then infer that there are specific correlating mechanisms at work.Comment: 4 pages, 4 figures. Accepted in Phys. Rev. Lett. (2010

Dynamic Asset Allocation under Recursive Ambiguous Utility

资产配置理论是随着金融理论从传统金融学到行为金融学的推进而不断发展。在主观期望效用理论下，投资者的最优资产配置主要由三个因素共同决定：资产收益模型、投资者信念和投资者效用的表现形式。 国外大量研究表明，股票收益表现出两个规律:一是短期收益存在“趋势性”；二是长期收益存在“均值回复”性。据此，本文假定股票收益有两种可能的模型——一阶自相关模型和均值回复模型。由于接收的市场信息不充分，投资者不知道真实收益模型，他只能通过观察历史收益数据产生对这两个模型的信念，并按照贝叶斯法则对该信念进行更新。考虑到投资者行为具有模糊厌恶性，本文采用模糊递归效用模型，并将收益模型的不确定性和信念更新过程纳入其中来...Asset allocation theory was developed along with the progress from traditional finance to behavioral finance. Under the theory of Subjective Expected Utility, the optimal asset allocation of investors are mainly determined by three common factors, including the model of asset return, the belief of investors and the representation of investors’ utility. Significant amount of foreign research h...学位：经济学硕士院系专业：经济学院金融系_金融工程学号：1562007115147

Entropic origin of disassortativity in complex networks

Why are most empirical networks, with the prominent exception of social ones, generically degreedegree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated) configurations, but in the case of highly heterogeneous, scale-free networks a certain disassortativity is predicted-offering a parsimonious explanation for the question above. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. When empirical observations deviate from the neutral predictions-as happens for social networks-one can then infer that there are specific correlating mechanisms at work

Evolving Networks and the Development of Neural Systems

It is now generally assumed that the heterogeneity of most networks in nature probably arises via preferential attachment of some sort. However, the origin of various other topological features, such as degree-degree correlations and related characteristics, is often not clear and attributed to specific functional requirements. We show how it is possible to analyse a very general scenario in which nodes gain or lose edges according to any (e.g., nonlinear) functions of local and/or global degree information. Applying our method to two rather different examples of brain development -- synaptic pruning in humans and the neural network of the worm C. Elegans -- we find that simple biologically motivated assumptions lead to very good agreement with experimental data. In particular, many nontrivial topological features of the worm's brain arise naturally at a critical point.Comment: 16 pages, 4 figures. Accepted for publication in J. Stat. Mec