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At least 1,603 records · Page 89Linked to original sources
PubMed2002

Extradimensional bypass.

We discuss the concept of extradimensional bypass as it was developed by the late theoretical biologist Michael Conrad. An evolving system that optimizes its performance by gradient ascent (hill climbing) can avoid being trapped in local maxima by increasing the effective dimensionality of its search space. Many local maxima may become saddle points in the higher dimensional space, such that gradient ascent can continue unimpeded. Extradimensional bypass as a concept has parallels in theories of open-ended learning and functional emergence, where new structural, functional, and informational primitives can increase the effective dimensionality of material systems.

Biological Evolution↗
PubMed2003

An example of design optimization for high evolvability: string rewriting grammar.

As an example of the optimization of an evolutionary system design, a string rewriting system is studied. A set of rewriting rules that defines the growth of a string is experimentarily optimized in terms of maximizing the 'replicative capacity', that is the occurrence ratio of self-replicating strings. It is shown that the most optimized rule set allows many strings to self-replicate by using a special character able to copy an original string sequentially. Then, using various different rewriting rule sets, the connectivity between self-replicating strings is studied. A set of 'hyperblobs' covering the self-replicating strings is extracted and their connectivity is studied. The experimental results show that a large replicative capacity assures strong connectivity between self-replicating genotypes, making the system highly evolvable.

Algorithms↗
PubMed2003

Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer.

The emergence derived from errors is the key importance for both novel computing and novel usage of the computer. In this paper, we propose an implementable experimental plan for the biological computing so as to elicit the emergent property of complex systems. An individual plasmodium of the true slime mold Physarum polycephalum acts in the slime mold computer. Modifying the Elementary Cellular Automaton as it entails the global synchronization problem upon the parallel computing provides the NP-complete problem solved by the slime mold computer. The possibility to solve the problem by giving neither all possible results nor explicit prescription of solution-seeking is discussed. In slime mold computing, the distributivity in the local computing logic can change dynamically, and its parallel non-distributed computing cannot be reduced into the spatial addition of multiple serial computings. The computing system based on exhaustive absence of the super-system may produce, something more than filling the vacancy.

Animals↗
PubMed2001

Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control.

Marr [J. Physiol. (1969) 202, 437-470] and Albus [Math. Biosci. (1971) 10, 25-61] hypothesized that cerebellar learning is facilitated by a granule cell sparse code, i.e. a neural code in which the fraction of active neurons is low at any one time. In this paper, we re-examine this hypothesis in light of recent experimental and theoretical findings. We argue that cerebellar motor learning is enhanced by a sparse code that simultaneously maximizes information transfer between mossy fibers and granule cells, minimizes redundancies between granule cell discharges, and re-codes the mossy fiber inputs with an adaptive resolution such that inputs corresponding to large errors are finely encoded. We then propose that a set of biologically plausible unsupervised learning rules can produce such a code. To maintain a low mean firing rate compatible with a sparse code, an activity-dependent homeostatic mechanism sets the cells' thresholds. Then, to maximize information transfer, the mossy fiber--granule cell synapses are adjusted by a Hebbian rule. Furthermore, to minimize redundancies between granule cell discharges, the inhibitory Golgi cell--granule cell synapses are tuned by an anti-Hebbian rule. Finally, to allow adaptive resolution, a performance-based neuromodulator-like signal gates these three plastic processes. We integrate these gated learning rules into a simplified model of the cerebellum for arm movement control, and show that unsupervised learning of granule cell sparse codes greatly improves cerebellar adaptive motor control in comparison to a "fixed" Marr--Albus-type model. Until recently, activity-dependent cerebellar plasticity was thought to be largely confined to the granule cell--Purkinje cell synapses. This static view of the cerebellum is, however, quickly being replaced by an extremely dynamic view in which plasticity is omnipresent. The present theoretical study shows how several forms of plasticity in the granular layer of the cerebellum can produce fast, accurate and stable cerebellar learning.

Algorithms↗
PubMedDate not supplied

Topological aspects of axonal regeneration.

The fibers making up any sensory system in the spinal cord come from the same cells as do the fibers in peripheral nerves yet severed nerve fibers in the adult spinal cord do not regenerate but damaged peripheral nerves - those in the extremities - do heal themselves. Why should spinal cord regeneration even be an issue, why should an inhibiting protein have evolved to prevent it and what causes this protein to be expressed? From a holistic perspective, an answer to this conundrum shows that these questions are intertwined, and suggests that: (1) The model of the neurons as 'wires' is too simplistic. (2) In humans, the 'map' of individual connections is (topologically, at least) locally variable, though the overall global topology and 'functionality' of each normal spinal cord is constant. Both of these issues have to be addressed if functional restoration is to be achieved.

Animals↗
PubMed1996

On the dimensionality of cortical graphs.

We propose to use a random-graph model of cortex as a tabula-rasa state, to be contrasted with various types of regular connectivity patterns. Key in our analysis is the notion of graph-theoretic dimensionality, closely linked to that of graph diameter. Our discussion focuses on patterns of synfire type, and on the synfire-superposition model proposed in previous papers.

Cerebral Cortex↗