F. Montani

Fernando Montani

Quantifying higher-order correlations in a neuronal pool

Medio de publicación: 
Physica A: Statistical Mechanics and its Applications
Autores: 
Lisandro Montangie and Fernando Montani
Año de publicación: 
2014

 Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically.

Entropy-Complexity Characterization of Brain Development in Chickens

Medio de publicación: 
Entropy
Autores: 
Fernando Montani, Osvaldo A Rosso
Año de publicación: 
2014

 EEGs reflect the electrical activity of the brain that can be considered chaotic and ruled by a nonlinear dynamics. Chickens exhibit a protracted period of maturation and this temporal separation of the synapse formation and maturation phases is analogous to human neural development, though the changes in chickens occur in weeks compared with years in humans. The development of synaptic networks in the chicken brain can be regarded as occurring in two broadly defined phases.

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

Medio de publicación: 
Physica A: Statistical Mechanics and its Applications
Autores: 
Fernando Montani, Emilia B. Deleglise, Osvaldo Rosso
Año de publicación: 
2014

 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.

Modelos Computacionales para Investigar la Dinámica Neuronal

Medio de publicación: 
Trabajo de Diploma; Departamento de Física - Fac. de Cs. Exactas, Universidad Nacional de La Plata
Autores: 
Lic. Emilia B Deleglise, Director Dr. Fernando Montani
Año de publicación: 
2013

 Uno de los modelos computacionales más simples de neuronas, es el Modelo Simple de Neurona

Disparando de E. Izhikevich [11], que utiliza sólo dos variables dinámicas y cuatro parámetros para

generar 20 tipos distintos de dinámicas neuronales. Un modelo de dinámica lineal de membrana

simplificado aún más es el demostrado por Mihalas y Niebur [28], basado en los trabajos de Hodgkin

y Huxley [4]. A pesar del éxito de estos modelos para emular a la dinámica neuronal ([29] a [32]),

Statistical modelling of higher-order correlations in pools of neural activity

Medio de publicación: 
Physica A (2013) 392 (14) 3066–3086.
Autores: 
Fernando Montani, Elena Phoka, Mariela Portesi, Simon R. Schultz
Año de publicación: 
2013

 Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains.

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

Medio de publicación: 
BMC Neuroscience (2012) Vol. 13 Issue 1, 96-114.
Autores: 
Andriy Oliynyk, Claudio Bonifazzi, Fernando Montani, Luciano Fadiga
Año de publicación: 
2012

 Abstract

Background: Understanding how neurons contribute to perception, motor functions and cognition requires the

reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An

important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort

the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting

exist, the problem of accurate and fast online sorting still remains a challenging issue.

Fernando Fabian Montani

 

Tel: + 54 221 4233283     interno: 32     E-mail:fmontani@gmail.com

 Dr. Fernando Fabian Montani 

Tesis de Grado y de Doctorado realizadas en el Grupo

 


Tesis de Grado

Juan Muglia (2011) - Licenciatura en Física, Facultad de Ciencias Exactas, UNLP.

Trabajo de Diploma: Simulaciones computacionales del Modelo de Ising con gradiente térmico

  


Tesis Doctorales

Gabriel Baglietto  (2011) - UNLP.

Spontaneous Symmetry breaking and the energy of the Isobaric Analog State

Medio de publicación: 
PhysRev. C 61, (2000) 064306
Autores: 
O. Civitarese, F. Montani, M. Reboiro and H. Toki
Año de publicación: 
2000

The asymmetry between proton and neutron numbers in a nucleus is viewed as a consequence of the
spontaneous symmetry breaking of the isospin symmetry. The signatures of this effect, as it was suggested by
Danchev, Khanna, and Umezawa and co-workers, may have been seen already in the energetics of the nuclear
isobaric analog resonance state and in the systematic of double-odd double-even mass differences. In order to
account for finite size effects, not included in Umezawa’s approach, we have calculated the mean field term of

Dynamics of damage spreading in the two-dimensional Ising magnet at Critically

Medio de publicación: 
Phys. Lett. A 202, (1995)
Autores: 
Fernando Montani, Ezequiel V. Albano
Año de publicación: 
1995

The spreading dynamics of an initially small damage is studied for the two-dimensional Ising model at criticality using the Glauber dynamics. The number of damaged sites, Nd(t), the survival probability of the damage, P(t), and the mean square distance over which the damage spreads, R2(t), obey a simple power law behavior with critical exponents ç 1.11 ± 0.03, ä 0.58 ± 0.03 and z* 1.19 ± 0.03, respectively. It is found that the scaling relation df = 2ç/z* gives the fractal dimension of the Ising droplets.