Multiplicative mutations lead to sparse systems

Community Lounge
1

Evolution with multiplicative mutations leads to sparse modular systems, according to this paper:
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0070444
The paper also says most biological mutations are multiplicative.

2

Deleted.

3

Crossover also seems to be quite similar. Multiplicative weight updates are involved according to some people.
https://youtu.be/cWv-s6KuDlM
Anyway I’m retired from the subject area so yeh…

4

Deep learning via multiplicative weight updates:
https://youtu.be/F8aPV6chfyA
See the more section for the paper and code.

5

Blog on Multiplicative Weight Updates.
https://jeremykun.com/2017/02/27/the-reasonable-effectiveness-of-the-multiplicative-weights-update-algorithm/

6

Can such ideas help with continual learning in neural networks?
https://continualai.discourse.group/t/multiplicative-weight-updates/321

7

Here is another interesting article on the topic, which comes to a similar conclusion: that analyzing biological networks (aka gene regulatory networks) is actually a much more tractable problem than people initially assume.

Degeneracy in the regulation of short-term plasticity
and synaptic filtering by presynaptic mechanisms
Chinmayee L. Mukunda and Rishikesh Narayanan
2017

Key points

  • We develop a new biophysically rooted, physiologically constrained conductance-based synaptic model to mechanistically account for short-term facilitation and depression, respectively through residual calcium and transmitter depletion kinetics.
  • We address the specific question of how presynaptic components (including voltage-gated ion channels, pumps, buffers and release-handling mechanisms) and interactions among them define synaptic filtering and short-term plasticity profiles.
  • Employing global sensitivity analyses (GSAs), we show that near-identical synaptic filters and short-term plasticity profiles could emerge from disparate presynaptic parametric combinations with weak pairwise correlations.
  • Using virtual knockout models, a technique to address the question of channel-specific contributions within the GSA framework, we unveil the differential and variable impact of each ion channel on synaptic physiology.
  • Our conclusions strengthen the argument that parametric and interactional complexity in biological systems should not be viewed from the limited curse-of-dimensionality standpoint, but from the evolutionarily advantageous perspective of providing functional robustness through degeneracy.