51,570 research outputs found

    Protosymbols that integrate recognition and response

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    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

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    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

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    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

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    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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