814 research outputs found
Markov Blankets in the Brain
Recent characterisations of self-organising systems depend upon the presence
of a Markov blanket: a statistical boundary that mediates the interactions
between what is inside of and outside of a system. We leverage this idea to
provide an analysis of partitions in neuronal systems. This is applicable to
brain architectures at multiple scales, enabling partitions into single
neurons, brain regions, and brain-wide networks. This treatment is based upon
the canonical micro-circuitry used in empirical studies of effective
connectivity, so as to speak directly to practical applications. This depends
upon the dynamic coupling between functional units, whose form recapitulates
that of a Markov blanket at each level. The nuance afforded by partitioning
neural systems in this way highlights certain limitations of modular
perspectives of brain function that only consider a single level of
description.Comment: 25 pages, 5 figures, 1 table, Glossar
Bits from Biology for Computational Intelligence
Computational intelligence is broadly defined as biologically-inspired
computing. Usually, inspiration is drawn from neural systems. This article
shows how to analyze neural systems using information theory to obtain
constraints that help identify the algorithms run by such systems and the
information they represent. Algorithms and representations identified
information-theoretically may then guide the design of biologically inspired
computing systems (BICS). The material covered includes the necessary
introduction to information theory and the estimation of information theoretic
quantities from neural data. We then show how to analyze the information
encoded in a system about its environment, and also discuss recent
methodological developments on the question of how much information each agent
carries about the environment either uniquely, or redundantly or
synergistically together with others. Last, we introduce the framework of local
information dynamics, where information processing is decomposed into component
processes of information storage, transfer, and modification -- locally in
space and time. We close by discussing example applications of these measures
to neural data and other complex systems
Sixty years of cybernetics: cybernetics still alive
summary:This informal essay, written on the occasion of 60th anniversary of Wienerian cybernetics, presents a series of themes and ideas that has emerged during last several decades and which have direct or indirect relationships to the principal concepts of cybernetics. Moreover, they share with original cybernetics the same transdisciplinary character
Neuronal oscillations, information dynamics, and behaviour: an evolutionary robotics study
Oscillatory neural activity is closely related to cognition and behaviour, with synchronisation mechanisms playing a key role in the integration and functional organization of different cortical areas. Nevertheless, its informational content and relationship with behaviour - and hence cognition - are still to be fully understood.
This thesis is concerned with better understanding the role of neuronal oscillations and information dynamics towards the generation of embodied cognitive behaviours and with investigating the efficacy of such systems as practical robot controllers. To this end, we develop a novel model based on the Kuramoto model of coupled phase oscillators and perform three minimally cognitive evolutionary robotics experiments. The analyses focus both on a behavioural level description, investigating the robotās trajectories, and on a mechanism level description, exploring the variablesā dynamics and the information transfer properties within and between the agentās body and the environment.
The first experiment demonstrates that in an active categorical perception task under normal and inverted vision, networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally, and to adapt to different behavioural conditions. The second experiment relates assembly constitution and phase reorganisation dynamics to performance in supervised and unsupervised learning tasks. We demonstrate that assembly dynamics facilitate the evolutionary process, can account for varying degrees of stimuli modulation of the sensorimotor interactions, and can contribute to solving different tasks leaving aside other plasticity mechanisms. The third experiment explores an associative learning task considering a more realistic connectivity pattern between neurons. We demonstrate that networks with travelling waves as a default solution perform poorly compared to networks that are normally synchronised in the absence of stimuli.
Overall, this thesis shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, produce an asymmetric flow of information and can generate minimally cognitive embodied behaviours
A view of Neural Networks as dynamical systems
We consider neural networks from the point of view of dynamical systems
theory. In this spirit we review recent results dealing with the following
questions, adressed in the context of specific models.
1. Characterizing the collective dynamics; 2. Statistical analysis of spikes
trains; 3. Interplay between dynamics and network structure; 4. Effects of
synaptic plasticity.Comment: Review paper, 51 pages, 10 figures. submitte
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