Perspectives on Applications of a Stochastic Spiking Neuron Model to Neural Network Modeling

Perspectives on Applications of a Stochastic Spiking Neuron Model to Neural Network ModelingLecture by Antonio C. Roque.

This talk presents results of analytical and numerical investigations on the use of the Galves-Löcherbach stochastic neuron to model networks of spiking neurons. The work is done in collaboration with NeuroMat researchers Dr. Ludmila Brochini, Dr. Ariadne Costa, Dr. Miguel Abadi, Dr. Jorge Stolfi, Dr. Osame Kinouchi and students Renan Shimoura and Vinícius Cordeiro.Presented.

Presented at Perspectives in Nonlinear Dynamics conference, Berlin, July 26, 2016.

A stochastic version of the Potjans-Diesmann cortical column model

Vinicius L. Cordeiro, Renan O. Shimoura, Nilton L. Kamiji, Osame Kinouchi and Antonio C. Roque

Experimental evidence suggests that neurons and neural circuits display stochastic variability [1] and, therefore, it is important to have neural models that capture this stochasticity. There are basically two types of noise model for a neuron [2]: (1) spike generation is modeled deterministically and noise enters the dynamics via additional stochastic terms; or (2) spike generation is directly modeled as a stochastic process. Recently, Galves and Löcherbach [3] introduced a neural model of the latter type in which the firing of a neuron at a given time t is a random event with probability given by a monotonically increasing function of its membrane potential V. The model of Galves and Löcherbach (GL) has as one of its components a graph of interactions between neurons. In this work we consider that this graph has the structure of the Potjans and Diesmann network model of a cortical column [4]. The model of Potjans and Diesmann has four layers and two neuron types, excitatory and inhibitory, so that there are eight cell populations. The population-specific neuron densities and connectivity are taken from comprehensive anatomical and electrophysiological studies [5-6], and the model has approximately 80,000 neurons and 300,000,000 synapses. We adjusted the parameters of the firing probability of the GL model to reproduce the firing behavior of regular (excitatory) and fast (inhibitory) spiking neurons [7]. Then, we replaced the leaky integrate-and-fire neurons of the original Potjans-Diesmann model by these stochastic neurons to obtain a stochastic version of the Potjans-Diesmann model. The parameters of the model are the weights we and wi of the excitatory and inhibitory synaptic weights of the GL model [3]. We studied the firing patterns of the eight cell populations of the stochastic model in the absence of external input and characterized their behavior in the two-dimensional diagram spanned by the excitatory and inhibitory synaptic weights. For a balanced case in which the network activity is asynchronous and irregular the properties of the stochastic model are similar to the properties of the original Potjans-Diesmann model. Different neural populations have different firing rates and inhibitory neurons have higher firing rates than excitatory neurons. In particular, the stochastic model emulates the very low firing rates of layer 2/3 observed in the original model and also experimentally [4]. We also submitted the network to random input spikes applied to layers 4 and 6 to mimic thalamic inputs, as done by Potjans and Diesmann [4], and studied the propagation of activity across layers. In conclusion, the stochastic version of the Potjans-Diesmann model can be a useful replacement for the original Potjans-Diesmann model in studies that require a comparison between stochastic and deterministic models.

A Review of Guidelines and Models for Representation of Provenance Information from Neuroscience Experiments

Margarita Ruiz-Olazar, Evandro S. Rocha, Sueli S. Rabaça, Carlos Eduardo Ribas, Amanda S. Nascimento, Kelly R. Braghetto

To manage raw data from Neuroscience experiments we have to cope with the heterogeneity of data formats and the complexity of additional metadata, such as its provenance information, that need to be collected and stored. Although some progress has already been made toward the elaboration of a common description for Neuroscience experimental data, to the best of our knowledge, there is still no widely adopted standard model to describe this kind of data. In order to foster neurocientists to find and to use a structured and comprehensive model with a robust tracking of data provenance, we present a brief evaluation of guidelines and models for representation of raw data from Neuroscience experiments, focusing on how they support provenance tracking.

What the frontier of science has to do with Wikipedia

Two months ago a task force with journalists and scientists officially began to improve the content of the mathematical theory of the brain in the Portuguese Wikipedia. This is an initiative of "wiki scientific dissemination", developed in NeuroMat, one of FAPESP's Research, Innovation and Dissemination centers, hosted by the University of São Paulo (USP). Report by Célio Costa Filho, ARede Educa, 6/13/2016. (In Portuguese.)

Motor planning of goal-directed action is tuned by the emotional valence of the stimulus: a kinematic study

P. O. Esteves, L. A. S. Oliveira, A. A. Nogueira-Campos, G. Saunier, T. Pozzo, J. M. Oliveira, E. C. Rodrigues, E. Volchan & C. D. Vargas

The basic underpinnings of homeostatic behavior include interacting with positive items and avoiding negative ones. As the planning aspects of goal-directed actions can be inferred from their movement features, we investigated the kinematics of interacting with emotion-laden stimuli. Participants were instructed to grasp emotion-laden stimuli and bring them toward their bodies while the kinematics of their wrist movement was measured. The results showed that the time to peak velocity increased for bringing pleasant stimuli towards the body compared to unpleasant and neutral ones, suggesting higher easiness in undertaking the task with pleasant stimuli. Furthermore, bringing unpleasant stimuli towards the body increased movement time in comparison with both pleasant and neutral ones while the time to peak velocity for unpleasant stimuli was the same as for that of neutral stimuli. There was no change in the trajectory length among emotional categories. We conclude that during the “reach-to-grasp” and “bring-to-the-body” movements, the valence of the stimuli affects the temporal but not the spatial kinematic features of motion. To the best of our knowledge, we show for the first time that the kinematic features of a goal-directed action are tuned by the emotional valence of the stimuli.




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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