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

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

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)

EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Moreover, near this phase transition, our model system exhibits waves with a strong component in the so-called delta-theta domain that coexist with fast oscillations. These findings provide a simple explanation for the observed delta-gamma and theta-gamma modulation in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data.Spanish Ministry of Science and TechnologyAgencia Española de Investigación (AEI)FEDER - FIS2017-84256-PConsejería de Conocimiento, Investigación Universidad, Junta de Andalucía and European Regional Development Funds, Spain - SOMM17/6105/UGR Y A-FQM-175-UGR18Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_0017

Physics Clues on the Mind Substrate and Attributes

The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings. In parallel, theoretical and computational studies developed to understand many natural and artificial complex systems, which have truthfully explained their amazing emergent features and precise the role of the interaction dynamics and other conditions behind the different collective phenomena they happen to display. Focusing on promising ideas that arise when comparing these neurobiology and physics studies, the present perspective article shortly reviews such fascinating scenarios looking for clues about how high-level cognitive processes such as consciousness, intelligence, and identity can emerge. We, thus, show that basic concepts of physics, such as dynamical phases and non-equilibrium phase transitions, become quite relevant to the brain activity while determined by factors at the subcellular, cellular, and network levels. We also show how these transitions depend on details of the processing mechanism of stimuli in a noisy background and, most important, that one may detect them in familiar electroencephalogram (EEG) recordings. Thus, we associate the existence of such phases, which reveal a brain operating at (non-equilibrium) criticality, with the emergence of most interesting phenomena during memory tasks.Project of I+D+i PID2020-113681GB-I00 MICIN/AEI/10.13039/501100011033European CommissionFEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Project P20_0017

A theoretical description of inverse stochastic resonance in nature

The inverse stochastic resonance (ISR) phenomenon consists of an unexpected depression in the response of a system under external noise, e.g., as observed in the mean firing rate in some pacemaker neurons subject to moderate values of noise. A possible cause for such unexpected reaction is the occurrence of a bistable regime controlling these neurons dynamics. We here explore theoretically the emergence of ISR in a general bistable model system, and thus determine the specific conditions the potential function driving the dynamics must accomplish. We conclude that such an intriguing, and apparently widely observed, phenomenon ensues in the case of an asymmetric potential function when the high activity minimum state of the system is metastable having a larger basin of attraction than the low activity state which is the global minimum of the system. We then discuss on the relevance of such a picture to understand the ISR features and to predict its appearance in nature. In addition, we report on existence of another intriguing, non-standard stochastic resonance in our model even in the absence of any weak signal input. Depending on the shape of the potential function, this new phenomenon shows up together with ISR precisely within the theoretical framework we present in this paper.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). We also thank fruitful comments from P. L. Garrido