AI Article Synopsis
- The study explores how the interplay between structural changes and functional behavior in neural networks influences their ability to process and store information.
- It demonstrates that a temporary phase of high synaptic connectivity is essential for memory recovery, especially in noisy environments, and that maintaining intermediate synaptic densities optimizes energy use during development.
- The findings provide insights into the characteristic patterns of synaptic pruning in the brain and offer guidance for designing neural networks that mimic biological systems for enhanced information processing.
Article Abstract
The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities.
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| http://dx.doi.org/10.1016/j.neunet.2021.04.027 | DOI Listing |
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