51,570 research outputs found
Protosymbols that integrate recognition and response
We explore two controversial hypotheses through robotic implementation: (1) Processes involved in recognition and response are tightly coupled both in their operation and epigenesis; and (2) processes involved in symbol emergence should respect the integrity of recognition and response while exploiting the periodicity of biological motion. To that end, this paper proposes a method of recognizing and generating motion patterns based on nonlinear principal component neural networks that are constrained to model both periodic and transitional movements. The method is evaluated by an examination of its ability to segment and generalize different kinds of soccer playing activity during a RoboCup match
A circular model for song motor control in Serinus canaria
Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture.Fil: Alonso, Rodrigo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Trevisan, Marcos Alberto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Amador, Ana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Goller, Franz. University Of Utah. Department Of Biology; Estados UnidosFil: Mindlin, Bernardo Gabriel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation
This paper addresses two questions in the context of neuronal networks
dynamics, using methods from dynamical systems theory and statistical physics:
(i) How to characterize the statistical properties of sequences of action
potentials ("spike trains") produced by neuronal networks ? and; (ii) what are
the effects of synaptic plasticity on these statistics ? We introduce a
framework in which spike trains are associated to a coding of membrane
potential trajectories, and actually, constitute a symbolic coding in important
explicit examples (the so-called gIF models). On this basis, we use the
thermodynamic formalism from ergodic theory to show how Gibbs distributions are
natural probability measures to describe the statistics of spike trains, given
the empirical averages of prescribed quantities. As a second result, we show
that Gibbs distributions naturally arise when considering "slow" synaptic
plasticity rules where the characteristic time for synapse adaptation is quite
longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure
Noise, transient dynamics, and the generation of realistic interspike interval variation in square-wave burster neurons
First return maps of interspike intervals for biological neurons that
generate repetitive bursts of impulses can display stereotyped structures
(neuronal signatures). Such structures have been linked to the possibility of
multicoding and multifunctionality in neural networks that produce and control
rhythmical motor patterns. In some cases, isolating the neurons from their
synaptic network revealsirregular, complex signatures that have been regarded
as evidence of intrinsic, chaotic behavior.
We show that incorporation of dynamical noise into minimal neuron models of
square-wave bursting (either conductance-based or abstract) produces signatures
akin to those observed in biological examples, without the need for fine-tuning
of parameters or ad hoc constructions for inducing chaotic activity. The form
of the stochastic term is not strongly constrained, and can approximate several
possible sources of noise, e.g. random channel gating or synaptic bombardment.
The cornerstone of this signature generation mechanism is the rich,
transient, but deterministic dynamics inherent in the square-wave
(saddle-node/homoclinic) mode of neuronal bursting. We show that noise causes
the dynamics to populate a complex transient scaffolding or skeleton in state
space, even for models that (without added noise) generate only periodic
activity (whether in bursting or tonic spiking mode).Comment: REVTeX4-1, 18 pages, 9 figure
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