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

Física Estadística de procesos marcovianos : estudio de redes de neuronas y sistemas afines

En esta memoria se presenta un modelo cinético de red de neuronas en el que la intensidad de los acoplamientos sinápticos varia con el tiempo en una escala del orden p(1-p)-1 comparada con la escala en la que varían las neuronas. Describimos algunos resultados exactos y de campo medio para p--- 0. Entre estos incluimos, por ejemplo, el modelo de hopfield con fluctuaciones aleatorias de las sinapsis, de forma que las neuronas se acoplan entre si, en promedio, de acuerdo a una regla de aprendizaje tipo hebb. Las consecuencias de tales fluctuaciones se analizan con detalle para diferentes elecciones de la probabilidad de transición elemental y de la distribución de fluctuaciones, incluyendo el caso de sinapsis asimétricas. Se presenta también un modelo reticular de sistema magnético desordenado, que incluye difusión rápida y aleatoria de impurezas. Esta se modela mediante una competición de dinámicas que lleva al sistema a una situación fuera del equilibrio.Tesis Univ. Granada. Departamento de Física Modern