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computational neuroscience approaches for morphogenesis control

Candidate: concept label1 source documents3 linked claims
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Extracted Explainers

What the tool is doing

The review proposes using computational-neuroscience approaches to understand and control regenerative pattern formation. These approaches are motivated by how neural systems store memories and pursue goals.

Source 1DOIPubMed

Resources required

The abstract supports a conceptual dependence on computational-neuroscience methods and on biological systems with bioelectric information processing. It does not specify software, datasets, or hardware.

Source 1DOIPubMed

Alternatives

The review contrasts this framing with relying on molecular genetics alone to control large-scale anatomy.

Source 1DOIPubMed

Evidence Snippets

approaches used in computational neuroscience to understand goal-seeking neural systems offer a toolbox of techniques to model and control regenerative pattern formation
Evidence 1Source 1DOIPubMedprovenance

Supporting Sources

Linked Claims

Claim 1conceptual frameworksupports2015Source 1DOIPubMed

Approaches from computational neuroscience are proposed as a toolbox for modeling and controlling regenerative pattern formation.

Claim 2hypothesissupports2015Source 1DOIPubMed

Target morphology could be encoded within tissues as a kind of memory using molecular mechanisms and algorithms analogous to those exploited by the brain.

Claim 3problem statementsupports2015Source 1DOIPubMed

A fundamental challenge in regenerative medicine is translating progress in molecular genetics into control of large-scale organismal anatomy.