The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules.
PLoS Comput Biol
Public Library of Science (PLoS)
MetadataShow full item record
Scholl, C., Rule, M., & Hennig, M. H. (2021). The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules.. PLoS Comput Biol, 17 (10) https://doi.org/10.1371/journal.pcbi.1009458
Funder: Studienstiftung des Deutschen Volkes; funder-id: http://dx.doi.org/10.13039/501100004350
Funder: Bundesministerium für Bildung und Forschung; funder-id: http://dx.doi.org/10.13039/501100002347
Funder: Max-Planck-Gesellschaft; funder-id: http://dx.doi.org/10.13039/501100004189
During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning. Why networks should initially be over-populated, and the processes that determine which synapses and neurons are ultimately pruned, remains unclear. We study the mechanisms and significance of neural pruning in model neural networks. In a deep Boltzmann machine model of sensory encoding, we find that (1) synaptic pruning is necessary to learn efficient network architectures that retain computationally-relevant connections, (2) pruning by synaptic weight alone does not optimize network size and (3) pruning based on a locally-available measure of importance based on Fisher information allows the network to identify structurally important vs. unimportant connections and neurons. This locally-available measure of importance has a biological interpretation in terms of the correlations between presynaptic and postsynaptic neurons, and implies an efficient activity-driven pruning rule. Overall, we show how local activity-dependent synaptic pruning can solve the global problem of optimizing a network architecture. We relate these findings to biology as follows: (I) Synaptic over-production is necessary for activity-dependent connectivity optimization. (II) In networks that have more neurons than needed, cells compete for activity, and only the most important and selective neurons are retained. (III) Cells may also be pruned due to a loss of synapses on their axons. This occurs when the information they convey is not relevant to the target population.
Research Article, Biology and life sciences, Medicine and health sciences, Computer and information sciences, Social sciences
Engineering and Physical Sciences Research Council (EP/L027208/1)
External DOI: https://doi.org/10.1371/journal.pcbi.1009458
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330615