Open access
Date
2015-04-22Type
- Journal Article
ETH Bibliography
yes
Altmetrics
Abstract
Performing more tasks in parallel is a typical feature of complex brains. These are characterized by the coexistence of excitatory and inhibitory synapses, whose percentage in mammals is measured to have a typical value of 20-30%. Here we investigate parallel learning of more Boolean rules in neuronal networks. We find that multi-task learning results from the alternation of learning and forgetting of the individual rules. Interestingly, a fraction of 30% inhibitory synapses optimizes the overall performance, carving a complex backbone supporting information transmission with a minimal shortest path length. We show that 30% inhibitory synapses is the percentage maximizing the learning performance since it guarantees, at the same time, the network excitability necessary to express the response and the variability required to confine the employment of resources. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000101080Publication status
publishedExternal links
Journal / series
Scientific ReportsVolume
Pages / Article No.
Publisher
NatureSubject
Applied physics; Biological physicsOrganisational unit
03733 - Herrmann, Hans Jürgen (emeritus) / Herrmann, Hans Jürgen (emeritus)
Funding
319968 - Fluid Flow in Complex and Curved Spaces (EC)
More
Show all metadata
ETH Bibliography
yes
Altmetrics