The Neuronal Replicator Hypothesis

@article{Fernando2010TheNR,
  title={The Neuronal Replicator Hypothesis},
  author={Chrisantha Fernando and Richard A. Goldstein and E{\"o}rs Szathm{\'a}ry},
  journal={Neural Computation},
  year={2010},
  volume={22},
  pages={2809-2857}
}
We propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cognitive architectures. Replicators overcome some limitations of selectionist models of neuronal search. Hebbian learning is combined with replication to structure exploration on the… 
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56 Citations
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56 Citations

Neuronal boost to evolutionary dynamics

It is shown that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place, and the synergy between learning and selection is more efficient than the equivalent search by mutation selection.

Coupled Hebbian learning and evolutionary dynamics in a formal model for structural synaptic plasticity

It is argued that once presented with a problem, different putative solutions are generated in parallel by different groups or local neuronal complexes, with the subsequent stabilization and spread of the best solutions accelerates finding the right solutions.

Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain

It is shown how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which has been proposed elsewhere could operate at somatic timescales in the brain.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

It is demonstrated that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes and the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained.

Breeding novel solutions in the brain: a model of Darwinian neurodynamics.

It is hypothesized that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants, in response to Darwinian evolutionary search.

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

It is hypothesized that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants, in order to model Darwinian search.

An Attractor Network-Based Model with Darwinian Dynamics

It is argued that this type of evolutionary search with learning can be the basis of high-level cognitive processes, such as problem solving or language, and is more efficient than both natural selection on genetic inheritance or learning on their own.

Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal

A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space.

Darwinian dynamics over recurrent neural computations for combinatorial problem solving

It is shown that a population of reservoir computing units, arranged in one or two-dimensional topologies, is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes and points at the importance of neural representation, akin to genotype-phenotype maps, in determining the efficiency of any evolutionary search in the brain.

Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem

It is shown that a neurally implemented a cognitive architecture with evolutionary dynamics can solve the four-tree problem and that Darwinian Neurodynamics is a promising model of human problem solving that deserves further investigation.
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