The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules

Carolin Scholl, Michael E Rule, Matthias H Hennig

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere1009458
Number of pages23
JournalPLoS Computational Biology
Issue number10
Publication statusPublished - 11 Oct 2021


  • Algorithms
  • Animals
  • Computational Biology
  • Humans
  • Information Theory
  • Models, Neurological
  • Nerve Net/growth & development
  • Neural Networks, Computer
  • Neurons/physiology
  • Synapses/physiology


Dive into the research topics of 'The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules'. Together they form a unique fingerprint.

Cite this