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

El crowdfunding en España y sus aplicaciones en el ocio

En este trabajo estudiaremos el crowdfunding como fuente de financiación dada la importancia que está adquiriendo en los últimos años, para ello analizaremos el concepto de crowdfunding, incluyendo sus diversas clases, así como su evolución histórica desde su origen, centrándonos principalmente en la modalidad de recompensa. Adicionalmente analizaremos algunas de las plataformas de crowdfunding de recompensa más importantes tanto a nivel internacional como a nivel español y se realizara una comparativa entre las mismas. Posteriormente nos adentraremos en la situación del crowdfunding en España en los últimos años con el objetivo de determinar la tendencia y hacer predicciones acerca de la situación de esta modalidad de financiación en el futuro. También se estudiara la legislación aplicable a las plataformas de financiación participativa y de la misma forma se analizara el impacto fiscal que tiene esta modalidad de crowdfunding entre los diferentes sujetos participantes en el proyecto de recaudación. Para concluir determinaremos las posibles aplicaciones del crowdfunding en el mundo del ocio, ilustrando a través de ejemplos reales posibles usos.Universidad de Sevilla. Doble Grado de Derecho y Finanzas y Contabilida

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

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

Origin of coherent structures in a discrete chaotic medium

Using as an example a large lattice of locally interacting Hindmarsh-Rose chaotic neurons, we disclose the origin of ordered structures in a discrete nonequilibrium medium with fast and slow chaotic oscillations. The origin of the ordering mechanism is related to the appearance of a periodic average dynamics in the group of chaotic neurons whose individual slow activity is significantly synchronized by the group mean field. Introducing the concept of a "coarse grain" as a cluster of neuron elements with periodic averaged behavior allows consideration of the dynamics of a medium composed of these clusters. A study of this medium reveals spatially ordered patterns in the periodic and slow dynamics of the coarse grains that are controlled by the average intensity of the fast chaotic pulsation

Channel-specific input/output transformations arising from the interaction between dynamic synapses and subthreshold oscillations

Authors acknowledge support by MINECO TIN2012-30883 and FIS2013-43201-P

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

Interplay between subthreshold oscillations and depressing synapses in single neurons

Latorre R, Torres JJ, Varona P (2016) Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons. PLoS ONE 11(1): e0145830. doi:10.1371/journal.pone.0145830In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron's resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron's response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.This work was supported by MINECO TIN2012-30883 (RL and PV) and FIS2013-43201-P (JJT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A new HLA-based distributed control architecture for agricultural teams of robots in hybrid applications with real and simulated devices or environments

The control architecture is one of the most important part of agricultural robotics and other robotic systems. Furthermore its importance increases when the system involves a group of heterogeneous robots that should cooperate to achieve a global goal. A new control architecture is introduced in this paper for groups of robots in charge of doing maintenance tasks in agricultural environments. Some important features such as scalability, code reuse, hardware abstraction and data distribution have been considered in the design of the new architecture. Furthermore, coordination and cooperation among the different elements in the system is allowed in the proposed control system. By integrating a network oriented device server Player, Java Agent Development Framework (JADE) and High Level Architecture (HLA), the previous concepts have been considered in the new architecture presented in this paper. HLA can be considered the most important part because it not only allows the data distribution and implicit communication among the parts of the system but also allows to simultaneously operate with simulated and real entities, thus allowing the use of hybrid systems in the development of applications