Publications

Hawkes processes with variable length memory and an infinite number of components

Pierre Hodara and Eva Löcherbach

In this paper we propose a model for biological neural nets where the activity of the network is described by Hawkes processes having a variable length memory. The particularity in this paper is that we deal with an infinite number of components. We propose a graphical construction of the process and build, by means of a perfect simulation algorithm, a stationary version of the process. To implement this algorithm, we make use of a Kalikow-type decomposition technique. Two models are described in this paper. In the first model, we associate to each edge of the interaction graph a saturation threshold that controls the influence of a neuron on another. In the second model, we impose a structure on the interaction graph leading to a cascade of spike trains. Such structures, where neurons are divided into layers, can be found in the retina.

Counting results for sparse pseudorandom hypergraphs II

Yoshiharu Kohayakawa, Guilherme Oliveira Mota, Mathias Schacht and Anusch Taraz

We present a variant of a universality result of Rödl (1986) for sparse, 3-uniform hypergraphs contained in strongly jumbled hypergraphs. One of the ingredients of our proof is a counting lemma for fixed hypergraphs in sparse “pseudorandom” hypergraphs, which is proved in the companion paper (Counting results for sparse pseudorandom hypergraphs I).

Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

Nivaldo A. P. de Vasconcelos, Carina Soares-Cunha, Ana João Rodrigues, Sidarta Ribeiro and Nuno Sousa

The cerebral cortex is an anatomically divided and functionally specialized structure. It includes distinct areas, which work on different states over time. The structural features of spiking activity in sensory cortices have been characterized during spontaneous and evoked activity. However, the coordination among cortical and sub-cortical neurons during spontaneous activity across different states remains poorly characterized. We addressed this issue by studying the temporal coupling of spiking variability recorded from primary sensory cortices and hippocampus of anesthetized or freely behaving rats. During spontaneous activity, spiking variability was highly correlated across primary cortical sensory areas at both small and large spatial scales, whereas the cortico-hippocampal correlation was modest. This general pattern of spiking variability was observed under urethane anesthesia, as well as during waking, slow-wave sleep and rapid-eye-movement sleep, and was unchanged by novel stimulation. These results support the notion that primary sensory areas are strongly coupled during spontaneous activity.

Can somatosensory electrical stimulation relieve spasticity in post-stroke patients? A TMS pilot study

Peres A.S.C., Souza V.H., Catunda J.M.Y., Mazzeto-Betti K.C., Santos-Pontelli T.E.G., Vargas C.D., Baffa O., de Araújo D.B., Pontes-Neto O.M., Leite J.P. and Garcia M.A.C.

Evidence suggests that somatosensory electrical stimulation (SES) may decrease the degree of spasticity from neural drives, although there is no agreement between corticospinal modulation and the level of spasticity. Thus, stroke patients and healthy subjects were submitted to SES (3 Hz) for 30' on the impaired and dominant forearms, respectively. Motor evoked potentials induced by single-pulse transcranial magnetic stimulation were collected from two forearm muscles before and after SES. The passive resistance of the wrist joint was measured with an isokinetic system. We found no evidence of an acute carry-over effect of SES on the degree of spasticity.

Anticipated synchronization in neuronal circuits unveiled by a phase-response-curve analysis

Fernanda S. Matias, Pedro V. Carelli, Claudio R. Mirasso and Mauro Copelli

Anticipated synchronization (AS) is a counterintuitive behavior that has been observed in several systems. When AS occurs in a sender-receiver configuration, the latter can predict the future dynamics of the former for certain parameter values. In particular, in neuroscience AS was proposed to explain the apparent discrepancy between information flow and time lag in the cortical activity recorded in monkeys. Despite its success, a clear understanding of the mechanisms yielding AS in neuronal circuits is still missing. Here we use the well-known phase-response-curve (PRC) approach to study the prototypical sender-receiver-interneuron neuronal motif. Our aim is to better understand how the transitions between delayed to anticipated synchronization and anticipated synchronization to phase-drift regimes occur. We construct a map based on the PRC method to predict the phase-locking regimes and their stability. We find that a PRC function of two variables, accounting simultaneously for the inputs from sender and interneuron into the receiver, is essential to reproduce the numerical results obtained using a Hodgkin-Huxley model for the neurons. On the contrary, the typical approximation that considers a sum of two independent single-variable PRCs fails for intermediate to high values of the inhibitory coupling strength of the interneuron. In particular, it loses the delayed-synchronization to anticipated-synchronization transition.

Information transmission and criticality in the contact process

Marzio Cassandro, Antonio Galves and Eva Löcherbach

In the present paper, we study the relation between criticality and information transmission in the one-dimensional contact process with infection parameter λ. To do this we define the {\it sensitivity} of the process to its initial condition. This sensitivity increases for values of λ ‹ λc, the value of the critical parameter. The main point of the present paper is that we show that actually it continues increasing even after λc and only starts decreasing for sufficiently large values of λ. This provides a counterexample to the common belief that associates maximal information transmission to criticality.

Effect of synaptic plasticity on functional connectivity and global activity of a neocortical network model

Renan O. Shimoura, Rodrigo F.O. Pena and Antonio C. Roque

Neocortex plays key role in diverse brain functions. Understanding this role involves the study of collective neural activity patterns under different situations, and how these patterns relate to the structural and functional organization of neocortex. Here we study the effect of synaptic plasticity on neural spiking activity patterns in a neocortical network model. We measure changes in neural spiking patterns due to changes in the strengths of the synapses connecting neurons and relate them to changes in the functional connectivity of the network as disclosed by graph-theoretic measures.

Our neocortical network model was composed of excitatory and inhibitory neurons in the proportion of four excitatory cells for each inhibitory cell. Neurons were described by the Izhikevich model [1]. The parameters of the model were adjusted so that excitatory neurons were of the regular spiking (RS) type and inhibitory neurons were all of either the fast spiking (FS) or the low-threshold spiking (LTS) type. Synapses were modeled as event-based, and two types of synaptic dynamics were considered: one without synaptic plasticity in which the synaptic weight received a fixed increment after the pre-synaptic event and decayed exponentially after that, and one with synaptic plasticity in which the synapse obeyed an asymmetric spike-timing dependent plasticity (STDP) rule described by [2]. Neurons were organized into four layers (2/3, 4, 5 and 6) with layer- and cell-specific statistical connectivity rules based on [3]. The total number of neurons in the model was about 4,000. Two experiments were done: one with all synapses described by the model without synaptic plasticity, and the other with synapses between excitatory neurons described by the STDP rule while the remaining synapses were described by the model without synaptic plasticity. In both cases, the model was stimulated by a current injection of random amplitude applied to neurons of layer 4 (L4), which is the main input layer of the cortex. The spiking activity of the network was evaluated by measures extracted from the raster plot of the spikes produced by the neurons, e.g. layer-specific and network mean and time-dependent firing rates. The structural and functional connectivities of the network were represented by the respective structural and functional adjacency matrices. The functional adjacency matrix was constructed by taking in consideration neuron pairs with strength of their synaptic coupling above a specific threshold. The topology of the adjacency matrices was characterized by graph-theoretic measures, e.g. clustering coefficient.

We determined a set of parameters for which the spiking activity generated in L4 by the external input propagated to the entire network. This network-wide activity was oscillatory, and we found that its mean frequency was higher for the network version with synaptic plasticity than for the version without synaptic plasticity. We also found that the formation of clusters of synchronous neural activity was facilitated in the case with LTS cells as inhibitory neurons. Our results suggest that synaptic plasticity may induce changes in the functional connectivity of the neocortical network with impact on its global activity.

A Naturalistic Assessment of the Organization of Children’s Memories Predicts Cognitive Functioning and Reading Ability

Natália Bezerra Mota, Janaína Weissheimer, Beatriz Madruga, Nery Adamy, Silvia A. Bunge, Mauro Copelli and Sidarta Ribeiro

To explore the relationship between memory and early school performance, we used graph theory to investigate memory reports from 76 children aged 6–8 years. The reports comprised autobiographical memories of events days to years past, and memories of novel images reported immediately after encoding. We also measured intelligence quotient (IQ) and theory of mind (ToM). Reading and Mathematics were assessed before classes began (December 2013), around the time of report collection (June 2014), and at the end of the academic year (December 2014). IQ and ToM correlated positively with word diversity and word-to-word connectivity, and negatively with word recurrence. Connectivity correlated positively with Reading in June 2014 as well as December 2014, even after adjusting for IQ and ToM. To our knowledge, this is the first study demonstrating a link between the structure of children's memories and their cognitive or academic performance.

Cerebral Dynamics during the Observation of Point-Light Displays Depicting Postural Adjustments

Eduardo F. Martins, Thiago Lemos, Ghislain Saunier, Thierry Pozzo, Daniel Fraiman and Claudia D. Vargas

Objective: As highly social creatures, human beings rely part of their skills of identifying, interpreting, and predicting the actions of others on the ability of perceiving biological motion. In the present study, we aim to investigate the electroencephalographic (EEG) cerebral dynamics involved in the coding of postural control and examine whether upright stance would be codified through the activation of the temporal-parietal cortical network classically enrolled in the coding of biological motion.

Design: We registered the EEG activity of 12 volunteers while they passively watched point light displays (PLD) depicting quiet stable (QB) and an unstable (UB) postural situations and their respective scrambled controls (QS and US). In a pretest, 13 volunteers evaluated the level of stability of our two biological stimuli through a stability scale.

Results: Contrasting QB vs. QS revealed a typical ERP difference in the right temporal-parietal region at an early 200–300 ms time window. Furthermore, when contrasting the two biological postural conditions, UB vs. QB, we found a higher positivity in the 400–600 ms time window for the UB condition in central-parietal electrodes, lateralized to the right hemisphere.

Conclusions: These results suggest that PLDs depicting postural adjustments are coded in the brain as biological motion, and that their viewing recruit similar networks with those engaged in postural stability control. Additionally, higher order cognitive processes appear to be engaged in the identification of the postural instability level. Disentangling the EEG dynamics during the observation of postural adjustments could be very useful for further understanding the neural mechanisms underlying postural control.

Correlations induced by depressing synapses in critically self-organized networks with quenched dynamics

João Guilherme Ferreira Campos, Ariadne de Andrade Costa, Mauro Copelli and Osame Kinouchi

In a recent work, mean-field analysis and computer simulations were employed to analyze critical self-organization in networks of excitable cellular automata where randomly chosen synapses in the network were depressed after each spike (the so-called annealed dynamics). Calculations agree with simulations of the annealed version, showing that the nominal branching ratio σ converges to unity in the thermodynamic limit, as expected of a self-organized critical system. However, the question remains whether the same results apply to the biological case where only the synapses of firing neurons are depressed (the so-called quenched dynamics). We show that simulations of the quenched model yield significant deviations from σ = 1 due to spatial correlations. However, the model is shown to be critical, as the largest eigenvalue of the synaptic matrix approaches unity in the thermodynamic limit, that is, λc = 1. We also study the finite size effects near the critical state as a function of the parameters of the synaptic dynamics.

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