Efficiency characterization of a large neuronal network: a causal information approach

When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of inter-connectivity across neurons. More specifically we take into account the fine temporal ``structures'' of the complex neuronal signals not just by using the probability distributions associated to the inter spike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of inter-connectivity across neurons grows, inferring the emergent dynamical properties of the system.Comment: 26 pages, 3 Figures; Physica A, in pres

Common inputs in subthreshold membrane potential: the role of quiescent states in neuronal activity

Experiments in certain regions of the cerebral cortex suggest that the spiking activity of neuronalpopulations is regulated by common non-Gaussian inputs across neurons. We model these deviations from random walk processes with q-Gaussian distributions into simple threshold neurons, and investigate the scaling properties in large neural populations. We show that deviations from the Gaussian statistics provide a natural framework to regulate population statistics such as sparsity, entropy and specific heat. This type of description allows us to provide an adequate strategy to explain the information encoding in the case of low neuronal activity and its possible implications on information transmission.Instituto de Física de Líquidos y Sistemas Biológico

Higher-order cumulants drive neuronal activity patterns, inducing UP-DOWN states in neural populations

A major challenge in neuroscience is to understand the role of the higher-order correlations structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike trains by means of thresholding a multivariate Gaussian random variable. The DG inputs are Gaussian distributed, and thus have no interactions beyond the second order in their inputs; however, they can induce higher-order correlations in the outputs. We propose a combination of analytical and numerical techniques to estimate higher-order, above the second, cumulants of the firing probability distributions. Our findings show that a large amount of pairwise interactions in the inputs can induce the system into two possible regimes, one with low activity (“DOWN state”) and another one with high activity (“UP state”), and the appearance of these states is due to a combination between the third- and fourth-order cumulant. This could be part of a mechanism that would help the neural code to upgrade specific information about the stimuli, motivating us to examine the behavior of the critical fluctuations through the Binder cumulant close to the critical point. We show, using the Binder cumulant, that higher-order correlations in the outputs generate a critical neural system that portrays a second-order phase transition.Fil: Baravalle, Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentin

Common inputs in subthreshold membrane potential: The role of quiescent states in neuronal activity

Experiments in certain regions of the cerebral cortex suggest that the spiking activity of neuronalpopulations is regulated by common non-Gaussian inputs across neurons. We model these deviations from random walk processes with q-Gaussian distributions into simple threshold neurons, and investigate the scaling properties in large neural populations. We show that deviations from theGaussian statistics provide a natural framework to regulate population statistics such as sparsity, entropy and specific heat. This type of description allows us to provide an adequate strategy to explain the information encoding in the case of low neuronal activity and its possible implications on information transmission.Fil: Montangie, Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentin

Information theoretic measures and their applications

The concept of entropy, an ever-growing physical magnitude that measured the degree of decay of order in a physical system, was introduced by Rudolf Clausius in 1865 through an elegant formulation of the second law of thermodynamics. Seven years later, in 1872, Ludwig Boltzmann proved the famous H-theorem, showing that the quantity H always decreases in time, and in the case of perfect gas in equilibrium, the quantity H was related to Clausius’ entropyS. The dynamical approach of Boltzmann, together with the elegant theory of statistical ensembles at equilibrium proposed by Josiah Willard Gibbs, led to the Boltzmann–Gibbs theory of statistical mechanics, which represents one of the most successful theoretical frameworks of physics. In fact, with the introduction of entropy, thermodynamics became a model of theoretical science.Fil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentin

Rhythmic activities of the brain: quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

Electroencephalography (EEG) signals depict the electrical activity that take place at the surface of the brain, and provide an important tool for understanding a variety of cognitive processes. The EEG are the product of synchronized activity of the brain and variations in EEG oscillations patterns reflect the underlying changes in neuronal synchrony. Our aim is to characterize the complexity of the EEG rhythmic oscillations bands when the subjects performs a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment. We estimate the intrinsic correlational structure of the signals within the causality entropy-complexity plane H x C, where the enhanced complexity in the gamma 1, gamma 2 and beta 1 bands allow us to distinguish motor-visual memory tasks from control conditions. We identify the dynamics of the gamma 1, gamma 2 and beta 1 rhythmic oscillations within the zone of a chaotic dissipative behavior, while in contrast the beta 2 band shows a much higher level of entropy and a significant low level of complexity that corresponds to a non-invertible cubic map. Our findings enhance the importance of the gamma band during attention in perceptual feature binding during the visuomotor/imagery tasks.Instituto de Física de Líquidos y Sistemas Biológico

Causal Shannon-Fisher Characterization of Motor/Imagery Movements in EEG

The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon-Fisher plane H × F. This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks.Facultad de Ciencias ExactasInstituto de Física de Líquidos y Sistemas Biológico

Rhythmic activities of the brain: quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

Electroencephalography (EEG) signals depict the electrical activity that take place at the surface of the brain, and provide an important tool for understanding a variety of cognitive processes. The EEG are the product of synchronized activity of the brain and variations in EEG oscillations patterns reflect the underlying changes in neuronal synchrony. Our aim is to characterize the complexity of the EEG rhythmic oscillations bands when the subjects performs a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment. We estimate the intrinsic correlational structure of the signals within the causality entropy-complexity plane H x C, where the enhanced complexity in the gamma 1, gamma 2 and beta 1 bands allow us to distinguish motor-visual memory tasks from control conditions. We identify the dynamics of the gamma 1, gamma 2 and beta 1 rhythmic oscillations within the zone of a chaotic dissipative behavior, while in contrast the beta 2 band shows a much higher level of entropy and a significant low level of complexity that corresponds to a non-invertible cubic map. Our findings enhance the importance of the gamma band during attention in perceptual feature binding during the visuomotor/imagery tasks.Fil: Baravalle, Román. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentin

The dialectics of capital: learning from Gran Chaco

The critical impact of humans on the biosphere has led scientists to coin the term Anthropocene. The global environmental changes associated with it are happening under the aegis of capitalism. A transition towards sustainability requires a critical scrutiny of capitalism. The social–ecological system (SES) approach conceptualises the relationship between the socio-economic subsystem and the biosphere. However, in its various operationalisations it either treats the former as a black box or it fails to capture dynamic aspects. We address these limits and develop a Dialectical Socio-Ecological System (D-SES) framework, which combines process ecology with historical materialism, to describe the emergence and persistence of capitalist dynamics. We draw on data collected through fieldwork and desk research and deploy our framework to study capital-intensive agriculture in the Chaco Salteño, an important agricultural frontier in South America, obtaining some general insights. We open up the socio-economic subsystem and break it down into a lower-level material/economic sphere and an upper-level cultural/institutional sphere. Capitalist dynamics emerge out of the peculiar relationships occurring both within and between these spheres. This configuration shows the typical signs of autocatalysis. It attracts resources and capital to expand itself (centripetality). It becomes more complex and organised over time, fine-tuning production modes, cultures, and institutions (directionality). It is subject to the laws of competition and profit maximisation, which emerge independently from the individual actors and processes making up the system (autonomy). Finally, it engenders frictions, reflecting class antagonism between the direct producers and the appropriators of wealth. These frictions can become leverage points for a system’s transformation.EEA SaltaFil: Ceddia, M. Graziano. University of Bern. Centre for Development and Environment; SuizaFil: Montani, Rodrigo. University of Bern. Centre for Development and Environment; SuizaFil: Montani, Rodrigo. Universidad Nacional de Córdoba. Instituto de Antropología de Córdoba; ArgentinaFil: Montani, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Antropología de Córdoba; ArgentinaFil: Mioni, Walter Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina

An information-theoretic study of neuronal spike correlations in the mammalian cerebral cortex

In chapter I of this thesis we present a review of the historical background of the previousspike correlation studies and current state of the problem. In the chapters II, III and IV ofthis thesis we have applied an information theoretic approach to study the role of correlationsin the neuronal code, using the responses of pairs of neurons to drifting sinusoidal gratingsof different orientations and contrasts recorded in the primary visual cortex of anesthetizedmacaque monkeys. In chapter V we investigate the effects of a focal stroke in a populationof neurons on information transmission using a computational and analytical approach tothe problem. Finally, in chapter VI we use a novel analytical approach to study effects ofhigher order correlations in a population of neurons.It has been proposed in neuroscientific literature that pooling can lead to a significant improvementin signal reliability, provided that the neurons being pooled are at most weaklycross-correlated. We have computed mutual information, and compared the informationavailable from pairs of cells with the sum of the single cell information values. This allowedus to assess the degree of synergy (or conversely, redundancy) in the coding. In chapter IIof this thesis, we show that due to a loss of information encoded in the neuronal identity ofthe cells, pooling spikes across neurons leads to a loss of a large fraction of the informationpresent in their spike trains.We have used information theory to examine whether stimulus-dependent correlation couldcontribute to the neural coding of orientation and contrast by pairs of V1 cells. To this end,in chapter III, we have used a modified version of the method of information components.This analysis revealed that although synchrony is prevalent and informative, the additionalinformation it provides is frequently offset by the redundancy arising from the similar tuningproperties of the two cells. Thus, coding is roughly independent with weak synergy orredundancy arising depending on the similarity in tuning and the temporal precision of theanalysis. Our findings suggest that this would allow cortical circuits to enjoy the stabilityprovided by having similarly tuned neurons without suffering the penalty of redundancyas the associated information transmission deficit is compensated by stimulus dependentsynchrony.In chapter IV, we present a discussion about different measures of correlations and in particularwe propose the Jensen-Shannon Divergence as a measure of the distance between thecorresponding probability distribution functions associated with each spikes fired observedpatterns. We applied this Divergence for fixed stimuli as a measure of discrimination betweencorrelated and independent firing of pairs of cells in the primary visual cortex. Thisprovides a new, information-theoretic measure of the strength of correlation. We found thatthe relative Jensen-Shannon Divergence (measured in relation to the case in which all cellsfired completely independently) decreases with respect to the difference in orientation preferencebetween the receptive field from each pair of cells. Our finding indicates that theJensen-Shannon Divergence can be used for characterizing the effective circuitry network ina population of neurons.The underlying origins of synchronized firing between cortical neurons are still under discussion.Inter-cellular communication through chemically mediated synaptic transmissionis considered a major contributor to the formation of neuronal synchrony. GABAergic inhibitoryneurons may be involved in the generation of oscillatory activity in the cortex andits synchronization. Specifically, reduction of GABAergic inhibition may favour corticalplasticity producing functional recovery following focal brain lesions. Research into neurotransmittersystems is therefore of paramount importance to understand the origins ofsynchronized spiking. However, it is necessary to understand first how simple focal abnormalitiesin GABAergic modulators can affect the information transmission in an impairedbrain tissue. In chapter V, we present a computational and analytical model of a topographicallymapped population code which includes a focal lesion as well as a process for receptivefield enlargement (plasticity). The model simulates the recovery processes in the brain, andallows us to investigate mechanisms which increase the ability of the cortex to restore lostbrain functions. We have estimated the Fisher Information carried by the topographic mapbefore and after the stroke. Our finding shows that by tuning the receptive field plasticity toa certain value, the information transfer through the cortex after stroke can be optimized.A widespread distribution of neuronal activity can generate higher-order stochastic interactions.In this case, pair-wise correlations do not uniquely determine synchronizing spiking ina population of neurons, and higher order interactions across neurons cannot be disregarded.We present a new statistical approach, using the information geometry framework, for analyzingthe probability distribution function (PDF) of spike firing patterns by consideringhigher order correlations in a neuronal pool. In chapter VI, we have studied the limit ofa large population of neurons and associated a deformation parameter to the higher ordercorrelations in the PDF. We have also performed an analytical estimation of the Fisher informationin order to evaluate the implications of higher order correlations between spikeson information transmission. This leads to a new procedure to study higher order stochasticinteractions.The overall findings of this thesis warn about making any extensive statement about therole of neuronal spike correlations without considering the general case inclusive of higherorder correlations, and suggest a need to reshape the current debate about the role of spikecorrelations across neurons.Imperial College Londo