Chaos breeds autonomy: connectionist design between bias and baby-sitting

Leeuwen, Cees van

doi:10.1007/s10339-007-0193-8

Cited by 14 publications

(14 citation statements)

References 91 publications

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“…Furthermore, the notion of Milnor or ruined attractors underlies much of the technical and cognitive literature on itinerant dynamics. For example, one can explain “a range of phenomena in biological vision, such as mental rotation, visual search, and the presence of multiple time scales in adaptation” using the concept of weakly attracting sets [92], see also [93]. It is this sort of dynamical behaviour that may underpin generalised policies that are specified directly in terms of equations of motion (as opposed to value functions in optimal control).…”

Section: Generalised (Itinerant) Policiesmentioning

confidence: 99%

Free Energy, Value, and Attractors

Friston

2012

Computational and Mathematical Methods in Medicine

109

129

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It has been suggested recently that action and perception can be understood as minimising the free energy of sensory samples. This ensures that agents sample the environment to maximise the evidence for their model of the world, such that exchanges with the environment are predictable and adaptive. However, the free energy account does not invoke reward or cost-functions from reinforcement-learning and optimal control theory. We therefore ask whether reward is necessary to explain adaptive behaviour. The free energy formulation uses ideas from statistical physics to explain action in terms of minimising sensory surprise. Conversely, reinforcement-learning has its roots in behaviourism and engineering and assumes that agents optimise a policy to maximise future reward. This paper tries to connect the two formulations and concludes that optimal policies correspond to empirical priors on the trajectories of hidden environmental states, which compel agents to seek out the (valuable) states they expect to encounter.

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Section: Generalised (Itinerant) Policiesmentioning

confidence: 99%

Free Energy, Value, and Attractors

Friston

2012

Computational and Mathematical Methods in Medicine

109

129

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“…These ideas are consistent with proposals made by Friston et al (2012), who argue that a characteristic feature of the brain is its tendency to wander, or not settle in to any particular state. By retaining an optimal degree of instability, a brain system can explore alternative hypotheses about the causes of incoming stimuli and permits the brain to learn by discovery (van Leeuwen, 2008). These wandering dynamics allow the system to converge on optimal responses to environmental demands.…”

Section: Introductionmentioning

confidence: 99%

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

McDonough

Nashiro

2014

Front. Hum. Neurosci.

114

164

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An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.

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“…To keep things simple, we will just consider the fitness of a pattern in terms of its hamming distance to some target pattern. This means, we are effectively using selectionist and evolutionary schemes to optimise the connections (and ensuing dynamics) to recover a target pattern.”When talking about the utility of dynamical instability in providing a basis for selection, you might want to refer to the work of Ivan Tyukin and colleagues 1 , 2 . These authors have studied chaotic systems in the context optimisation – and their neuronal counterparts.…”

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confidence: 99%

“…When talking about the utility of dynamical instability in providing a basis for selection, you might want to refer to the work of Ivan Tyukin and colleagues 1 , 2 . These authors have studied chaotic systems in the context optimisation – and their neuronal counterparts.…”

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confidence: 99%

Breeding novel solutions in the brain: A model of Darwinian neurodynamics

et al. 2017

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The fact that surplus connections and neurons are pruned during Background development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain.: We combine known components of the brain -recurrent neural Methods networks (acting as attractors), the action selection loop and implicit working memory -to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory.: We document two processes: selection of stored solutions and Results evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i) noisy recall of patterns from the attractor networks, (ii) noise during transmission of candidate solutions as messages between networks, and, (iii) spontaneously generated, untrained patterns in spurious attractors.: Attractor dynamics of recurrent neural networks can be used to Conclusions model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants. Keywords

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Chaos breeds autonomy: connectionist design between bias and baby-sitting

Cited by 14 publications

References 91 publications

Free Energy, Value, and Attractors

Free Energy, Value, and Attractors

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

Breeding novel solutions in the brain: A model of Darwinian neurodynamics

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