Can vocal conditioning trigger a semiotic ratchet in marmosets?

Hjalmar K. Turesson and Sidarta Ribeiro

The complexity of human communication has often been taken as evidence that our language reflects a true evolutionary leap, bearing little resemblance to any other animal communication system. (...) We hypothesize that increasingly-complex vocal conditioning of an appropriate animal model may be sufficient to trigger a semiotic ratchet, evidenced by progressive sign complexification, as spontaneous contact calls become indexes, then symbols and finally arguments (strings of symbols). To test this hypothesis, we outline a series of conditioning experiments in the common marmoset (Callithrix jacchus). The experiments are designed to probe the limits of vocal communication in a prosocial, highly vocal primate 35 million years far from the human lineage, so as to shed light on the mechanisms of semiotic complexification and cultural transmission, and serve as a naturalistic behavioral setting for the investigation of language disorders.

Sub-Gaussian mean estimators

Luc Devroye, Matthieu Lerasle, Gabor Lugosi, Roberto I. Oliveira

We discuss the possibilities and limitations of estimating the mean of a real-valued random variable from independent and identically distributed observations from a non-asymptotic point of view. In particular, we define estimators with a sub-Gaussian behavior even for certain heavy-tailed distributions. We also prove various impossibility results for mean estimators.

Hydrodynamic Limit for Spatially Structured Interacting Neurons

Aline Duarte, Guilherme Ost and Andrés A. Rodríguez

We study the hydrodynamic limit of a stochastic system of neurons whose interactions are not of mean-field type and are produced by chemical and electrical synapses, and leak currents. The system consists of ε−2 neurons embedded in [0, 1)2, each spiking randomly according to a point process with rate depending on both its membrane potential and position. When neuron i spikes, its membrane potential is reset to 0 while the membrane potential of j is increased by a positive value ε2a(i, j), if i influences j.

Automated analysis of free speech predicts psychosis onset in high-risk youths

Gillinder Bedi, Facundo Carrillo, Guillermo A. Cecchi, Diego Fernández Slezak, Mariano Sigman, Natália B. Mota, Sidarta Ribeiro, Daniel C. Javitt, Mauro Copelli and Cheryl M. Corcoran

Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.

An investigation of Hebbian phase sequences as assembly graphs

Daniel G. Almeida-Filho, Vitor Lopes-dos-Santos, Nivaldo A. P. Vasconcelos, José G. V. Miranda, Adriano B. L. Tort, Sidarta Ribeiro

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior.

Primary Motor Cortex Representation of Handgrip Muscles in Patients with Leprosy

Vagner Wilian Batista e Sá, Maria Katia Gomes, Maria Luíza Sales Rangel, Tiago Arruda Sanchez, Filipe Azaline Moreira, Sebastian Hoefle, Inaiacy Bittencourt Souto, Antônio José Ledo Alves da Cunha, Ana Paula Fontana, Claudia Domingues Vargas

Leprosy is an endemic infectious disease caused by Mycobacterium leprae that predominantly attacks the skin and peripheral nerves, leading to progressive impairment of motor, sensory and autonomic function. Little is known about how this peripheral neuropathy affects corticospinal excitability of handgrip muscles. Our purpose was to explore the motor cortex organization after progressive peripheral nerve injury and upper-limb dysfunction induced by leprosy using noninvasive transcranial magnetic stimulation (TMS).

Sleep Deprivation and Gene Expression

Annie da Costa Souza, Sidarta Ribeiro

Sleep occurs in a wide range of animal species as a vital process for the maintenance of homeostasis, metabolic restoration, physiological regulation, and adaptive cognitive functions in the central nervous system. Long-term perturbations induced by the lack of sleep are mostly mediated by changes at the level of transcription and translation. This chapter reviews studies in humans, rodents, and flies to address the various ways by which sleep deprivation affects gene expression in the nervous system, with a focus on genes related to neuronal plasticity, brain function, and cognition.

Rényi Entropies and Large Deviations for the First Match Function

Miguel Natalio Abadi, Liliam Cardeño

We define the first match function Tn : C^n → {1, ... , n} where C is a finite alphabet. For two copies of x1^n ∈ C^n, this function gives the minimum number of steps one has to slide one copy of x1^n to get a match with the other one. For ergodic positive entropy processes, Saussol and coauthors proved the almost sure convergence of Tn/n. We compute the large deviation properties of this function. We prove that this limit is related to the Rényi entropy function, which is also proved to exist.

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

Pierre Hodara, Eva Löcherbach

In this paper, we build a model for biological neural nets where the activity of the network is described by Hawkes processes having a variable length memory. The particularity of this paper is to deal with an infinite number of components. We propose a graphical construction of the process and we build, by means of a perfect simulation algorithm, a stationary version of the process. To carry out this algorithm, we make use of a Kalikow-type decomposition technique.

A test of hypotheses for random graph distributions built from EEG data

Andressa Cerqueira, Daniel Fraiman, Claudia D. Vargas, Florencia Leonardi

The theory of random graphs is being applied in recent years to model neural interactions in the brain. While the probabilistic properties of random graphs has been extensively studied in the literature, the development of statistical inference methods for this class of objects has received less attention. In this work we propose a non-parametric test of hypotheses to test if two samples of random graphs were originated from the same probability distribution.




The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).


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