Rossi Pool, Román (2011) Plasticidad y dinámica en redes neuronales. / Plasticity and dynamics in neural systems. Tesis Doctoral en Física, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
En esta tesis se analizan diversos aspectos de plasticidad neuronal y de dinámica de redes neuronales. En primer lugar, se estudia la plasticidad dependiente del tiempo desde un punto de vista de la optimización de entropía condicional. Se analizan los efectos de los tipos de dinámica neuronal (Tipo I o Tipo II) sobre las ventanas de plasticidad obtenidas con este principio. Se utilizan para ésto modelos neuronales reducidos con una función de escape que depende del valor de voltaje neuronal e introduce aleatoriedad en el sistema. Sin embargo, este modelo no permite analizar los efectos de las diferentes distribuciones de ruido sobre las ventanas de plasticidad y sólo es una buena aproximación para el ruido Gaussiano blanco. Debido a esto, se utilizan luego otros tipos de ruido para las evoluciones, haciéndose especial hincapié en ruidos simétricos y asimétricos. Se observa que algunas características de estas distribuciones tienen un gran efecto sobres las ventanas de plasticidad obtenidas. En particular, ruidos con más intensidad dan lugar a mayor LTD (long term depression) y ruidos más asimétricos separan las constantes temporales de LTD y LTP (long term potentiation). Utilizando estos dos tipos de enfoques, se obtienen una amplia gama de curvas STDP, que explican la gran mayoría de las curvas medidas en forma experimental. Se muestra luego, que la variabilidad en la corriente de entrada que una neurona recibe, puede ser explicada a través de estados balanceados de redes neuronales de gran tamaño. Se estudian estas fluctuaciones y se analiza con métodos de correlación inversa la posibilidad de identificar el tipo neuronal cuando el ruido es generado por la misma red. Además, se muestra que el ruido Gaussiano con autocorrelación exponencial, es una buena aproximación para las fluctuaciones que una neurona inmersa en este tipo de redes recibe. Finalmente, se estudia el desarrollo de las conexiones de una red recurrente con dinámica neuronal de tasa de disparo, que representa una hiper-columna de la corteza. Se obtiene, que la plasticidad Hebbiana o BCM (Bienenstock, Cooper y Munro) por si solas dan lugar a inestabilidades en el desarrollo de las sinapsis y rompen la simetría de la matriz de conexiones. Sin embargo, con la incorporación de procesos de homeostasis que tienden a estabilizar el sistema llevando la actividad neuronal a un valor esperado (actividad blanco), se logran desarrollar conexiones que cumplen las características y funciones esperadas.
Resumen en inglés
In this work several aspects of neuronal plasticity and neural-network dynamics have been analyzed. The spike-timing-dependent plasticity (STDP) was studied, using an optimization principle for the conditional entropy. This principle was applied to two types of postsynaptic dynamics, designated type I and type II. The objective is to evaluate the effect of neuronal dynamics on the plasticity profile. Reduced neuronal models with an escape-rate approximation for the stochastic part of the neural dynamic have been used. This does not allow one to analyze the effect of the noise distribution on the STDP window, and it is only a good approximation if the noise is Gaussian and white. Therefore, direct numerical simulations for the evolution of a simple neuron were performed with several noise distributions, focusing on the differences between symmetric and asymmetric ones. Some characteristic of the noise distribution have an important effect on the optimal plasticity. Stronger fluctuations give rise to larger depression and asymmetric noise distributions tend to have depressing regions with longer time scales. Depending on the parameters of the evolution and the type of noise, the optimization principle can give rise to a wide variety of STDP profiles, explaining most of the profiles measured experimentally. After that, the fluctuations of the inputs to a neuron in conductance-based balanced networks are characterized. It is possible to use reverse correlation methods to quantify the dynamical properties of single neurons and to accurately identify the types of neurons in the network. The dynamic fluctuations have a Gaussian distribution with an approximately exponential autocorrelation function. Finally, the development of a recurrent network with activity-dependent synaptic plasticity is studied. A simple model of hypercolumn of the visual cortex with recurrent connections is used. The covariance-based rules alone (Hebbian and BCM) tend to generate instabilities and to break the translational symmetry of the connections matrix. The incorporation of homeostatic plasticity in the model tends to stabilize the system by setting a target for the postsynaptic firing rate. The presence of homeostatic plasticity together with non-plastic uniform inhibition stabilizes the effect of Hebbian plasticity. The system can reach recurrent intracortical connections that are strongly modulated and display contrast invariance.
| Tipo de objeto: | Tesis (Tesis Doctoral en Física) |
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| Palabras Clave: | Neural networks; Redes neuronales; Plasticity; Plasticidad; Homeostasis; Optimization principles; Principios de optimizción; Balanced networks; Redes balanceadas; Recurrent networks; Redes recurrentes |
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| Materias: | Matemática > Estadística aplicada Medicina > Neurociencias |
| Divisiones: | Investigación y aplicaciones no nucleares > Física > Física estadística |
| Código ID: | 337 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 11 Jul 2012 11:32 |
| Última Modificación: | 11 Jul 2012 11:32 |
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