Estoy interesado en el estudio del sistema nervioso animal utilizando modelos computacionales teóricos.
Principalmente me centro en modelos de sistemas relacionados con la coordinación motora, prestando especial
atención a aquellos en los que aparecen firmas neuronales. Además de estos sistemas
contruyo modelos de:
Los Generadores Centrales de Patrones (CPGs).
La Oliva Inferior (IO).
Sistemas sensoriales y motores del clione.
Aunque principalmente me centro en redes neuronales naturales, también estoy interesado en aplicar las
características funcionales encontradas en estas redes a redes neuronales artificiales.
Abstract. In this paper we present a self-organizing neural
network paradigm that is able to discriminate information locally
using a strategy for information coding and processing inspired
in recent findings in living neural systems. The proposed neural
network uses: 1) neural signatures to identify each unit in the
network; 2) local discrimination of input information during the
processing; and 3) a multicoding mechanism for information
propagation regarding the who and the what of the information.
The local discrimination implies a distinct processing as a
function of the neural signature recognition and a local transient
memory. In the context of artificial neural networks none of
these mechanisms has been analyzed in detail, and our goal is
to demonstrate that they can be used to efficiently solve some
specific problems. To illustrate the proposed paradigm, we apply
it to the problem of multidimensional sorting, which can take
advantage of the local information discrimination. In particular,
we compare the results of this new approach with traditional
methods to solve jigsaw puzzles and we analyze the situations
where the new paradigm improves the performance.
R. Latorre, F.B. Rodriguez, P. Varona. 2007. Reaction to neural signatures through excitatory synapses in central pattern generator models.
Neurocomputing 70(10-12): 1797-1801. (0.865)
Abstract. The activity of central pattern generator (CPG) neurons is processed by
several different readers: neurons within the same CPG, neurons in other interconnected
CPGs and muscles. Taking this into account, it is not surprising that CPG neurons may use
different codes in their activity. In this paper, we study the capability of a CPG model
to react to neural signatures through excitatory synapses. Neural signatures are cell-specific
intraburst spike timings within their spiking bursting activity. These fingerprints are encoded
in the activity of the cells in addition to the information provided by their slow wave rhythm
and phase relationships. The results shown in this paper suggest that neural signatures can be
a mechanism to induce fast changes in the rhythm generated by a CPG through excitatory synapses.
R. Latorre, F.B. Rodríguez, P. Varona. 2006. Neural Signatures: Multiple Coding in Spiking Bursting Cells.
Biological Cybernetics
95(2): 169-183 (1.474)
Abstract. Recent experiments have revealed the existence of neural signatures in
the activity of individual cells of the pyloric Central Pattern
Generator (CPG) of crustacean. The neural signatures consist of
cell-specific spike timings in the bursting activity of the
neurons. The role of these intraburst neural fingerprints is still unclear. It
has been reported previously that some muscles can reflect small
changes in the spike timings of the neurons that innervate
them. However, it is unclear to what extent neural signatures
contribute to the command message that the muscles receive from the
motoneurons. It is also unknown whether the signatures have any
functional meaning for the neurons that belong to the same CPG or to
other interconnected CPGs. In this paper, we use realistic neural
models to study the ability of single cells and small circuits to
recognize individual neural signatures. We show that model cells and
circuits can adapt their behavior in response to the incoming neural
fingerprints in addition to the properties of the slow depolarizing
waves. Our results suggest that neural signatures can be a general
mechanism of spiking-bursting cells to implement multicoding.
Abstract. This paper presents a mechanism to optimize the access to a
database for Java 2 Platform Enterprise Edition (J2EE) applications. This mechanism
was used in the migration of a PHP application to the J2EE platform to solve the
limitations found in the standard database access. The proposed mechanism
is optimal for applications with complex database queries and/or a high number of them
per time unit. It presents an alternative to the traditional access, which can produce a database
overhead in applications with these characteristics. The proposed database access
mechanism is based on the idea of sharing precompiled statements and connections between
different users. When a part of the application wants to execute an SQL statement, either a new
object is created representing it, or an existing one, created previously, is reused.
R. Latorre, F.B. Rodríguez, P. Varona. 2004. Effect of individual spiking activity on rhythm generation of Central Pattern Generators.
Neurocomputing
58-60: 535-540 (0.641)
Abstract. Central pattern generators (CPGs) are highly specialized
neural networks often with redundant elements that allow the system to act
properly in case of error. CPGs are multifunctional circuits, i.e. the
same CPG can produce many different rhythms in response to modulatory
or sensory inputs. All these rhythms have to be optimal for motor control
and coordination. In this paper, we use a model of the well-known pyloric
CPG of crustacean to analyze the importance of redundant connections and
individual spiking activity in the generation of its rhythm. In particular,
we study the effect of different individual spike distributions on the network
behavior.
R. Latorre, F.B.Rodríguez, P. Varona. 2002. Characterization of Triphasic Rhythms in Central Pattern Generators (I): Interspike Interval Analysis.
Lect. Notes Comput. Sc. 2415: 160-166 (0.515)
Abstract. Central Pattern generators (CPGs) neurons produce
patterned signals to drive rhythmic behaviors in a robust and flexible manner. In this paper we
use a well known CPG circuit and two different models of spiking-bursting neurons to analyze
the presence of individual signatures in the behavior of the network. These signatures
consist of characteristic interspike interval profiles in the activity of each cell. The
signatures arise within the particular triphasic rhythm generated by the CPG network. We discuss
the origin and role of this type of individuality observed in these circuits.
F.B.Rodríguez, R. Latorre, P. Varona. 2002. Characterization of Triphasic Rhythms in Central Pattern Generators (II): Burst Information Analysis.
Lect. Notes Comput. Sc. 2415: 167-173 (0.515)
Abstract. Central Pattern generators (CPGs) are neural circuits
that produce patterned signals to drive rhythmic behaviors in a robust and flexible manner. In
this paper we analyze the triphasic rhythm of a well known CPG circuit using two different
models of spiking-bursting neurons and several network topologies. By means of a measure of
mutual information we calculate the degree of information exchange in the bursting activity
between neurons. We discuss the precision and robustness of different network configurations.
Otras publicaciones (conferencias, proceedings, abstracts, etc)
R. Latorre, F.B.Rodríguez, P. Varona. 2007. Origin and role of neural signatures in bursting neurons. In Cooperative Behavior in Neural Systems.
AIP Conference Proceedings, vol. 887: 51-60
Firmas Neuronales y Multicódigos en Neuronas con Comportamiento en Ráfagas
Escuela Politécnica Superior. Universidad Autónoma de Madrid.
Tutor: Pablo Varona
Fecha de defensa: 4 julio de 2008
Resumen.
Los Generadores Centrales de Patrones (CPGs) son redes
neuronales con un grado de especialización muy elevado. Sus neuronas
generan señales cuya misión es controlar comportamientos rítmicos de
una forma robusta y flexible. Experimentos realizados en preparaciones
in vitro del CPG pilórico de la langosta han revelado la
existencia de una firma neuronal identificativa de cada célula
de la red. Las firmas neuronales consisten en una distribución
temporal específica de los potenciales de acción en la actividad en
ráfagas de estas neuronas. Estas estructuras temporales coexisten con
la información codificada en la frecuencia de la onda lenta y en las
relaciones de fase entre señales generadas por distintas neuronas. Los
experimentos in vitro no han revelado cuál es el papel
funcional que pueden desempeñar.
Utilizando modelos teóricos, en esta tesis doctoral discutimos el
origen y posible significado funcional de las firmas neuronales como
parte de una estrategia de codificación multicódigo para
neuronas individuales en distintos tipos de redes neuronales. En los
modelos, la aparición de estas estructuras temporales tan precisas se
debe principalmente a la conectividad de la red y, en menor medida, a
la dinámica individual de cada célula. Los modelos señalan la
posibilidad de que las firmas neuronales forman parte de una
estrategia para el procesamiento de información contextualizado en la
actividad en ráfagas. Los resultados presentados en esta tesis
muestran que en los modelos dinámicos es posible implementar
mecanismos de multiplexación, reconocer firmas específicas y
procesarlas de forma independiente o junto con la información
codificada en la onda lenta. Estos resultados apoyan la hipótesis de
que las firmas neuronales pueden tener importantes implicaciones para
comprender el origen de los ritmos, su rápida respuesta a la entrada
modulatoria y los mecanismos de comunicación con los músculos que
coordinan los CPGs.
En la última parte de la tesis, presentamos un paradigma de red
neuronal artificial auto-organizativa inspirado en los resultados
obtenidos de nuestros modelos biológicos. La red neuronal propuesta
hace uso de (i) firmas neuronales para identificar cada neurona,
(ii) discriminación de la información recibida por los receptores en
función del origen de la señal y (iii) transmisión
multicódigo para propagar la información por la red. En el contexto de
las redes neuronales artificiales, ninguno de estos aspectos se ha
estudiado en detalle previamente. Nuestros resultados demuestran que
los paradigmas de computación artificial pueden utilizar estos
mecanismos para resolver un problema de forma eficiente.
