Keywords: Information Theory, Hippocampus, Learning and Memory, Statistics, Synaptic Weight, Long Term Potentiation.
TL;DR: Synapses in different hippocampal regions have different synaptic information storage capacities and these are not fixed properties but increase during long-term potentiation.
Abstract: Exploring different aspects of synaptic plasticity processes in the hippocampus is crucial to understanding mechanisms of learning and memory, improving artificial intelligence algorithms, and neuromorphic computers. Synapses from the same axon onto the same dendrite have a common history of coactivation and have similar spine head volumes, suggesting that synapse function precisely modulates structure. We have applied Shannon information theory to obtain a new analysis of synaptic information storage capacity (SISC) using non-overlapping dimensions of dendritic spine head volumes as a measure of synaptic weights with distinct states. Spine head volumes in the stratum radiatum of hippocampal area CA1 occupied 24 distinct states (4.1 bits). In contrast, spine head volumes in the middle molecular layer of control dentate gyrus occupied only 5 distinct states (2 bits). Thus, synapses in different hippocampal regions had different synaptic information storage capacities. Moreover, these were not fixed properties but increased during long-term potentiation, such that by 30 min following induction, spine head volumes in the middle molecular layer increased to occupy 10 distinct states (3 bits), and this increase lasted for at least 2 hours. Measurement of the Kullback-Liebler divergence revealed that synaptic states evolved closer to storing the maximum amount of information during long-term potentiation. These results show that our new SISC analysis provides an improved and reliable estimate of information storage capacity of synapses. SISC revealed that the Shannon information after long-term potentiation is nearly maximized for the number of distinguishable states.
In-person Presentation: no