Abstract
The connectivity structure of neuronal networks in cortex is highly dynamic. This ongoing cortical rewiring is assumed to serve important functions for learning and memory. We analyze in this article a model for the self-organization of synaptic inputs onto dendritic branches of pyramidal cells. The model combines a generic stochastic rewiring principle with a simple synaptic plasticity rule that depends on local dendritic activity. In computer simulations, we find that this synaptic rewiring model leads to synaptic clustering, that is, temporally correlated inputs become locally clustered on dendritic branches. This empirical finding is backed up by a theoretical analysis which shows that rewiring in our model favors network configurations with synaptic clustering. We propose that synaptic clustering plays an important role in the organization of computation and memory in cortical circuits: we find that synaptic clustering through the proposed rewiring mechanism can serve as a mechanism to protect memories from subsequent modifications on a medium time scale. Rewiring of synaptic connections onto specific dendritic branches may thus counteract the general problem of catastrophic forgetting in neural networks.
Highlights
Long-term imaging studies of the living brain have revealed that the cortical connectivity structure is dynamic, with dendritic spines being added and deleted on the time scale of hours to days (Holtmaat et al, 2005; Stettler et al, 2006; Kasai et al, 2010; Loewenstein et al, 2011; Rumpel and Triesch, 2016)
We study synaptic rewiring based on simple synaptic plasticity rules in a spiking neuron model for pyramidal cells (PCs) with dendritic non-linearities
In computer simulations of the model, we find that rewiring leads to a clustering of temporally correlated inputs onto dendritic branches, supporting the synaptic clustering hypothesis
Summary
Long-term imaging studies of the living brain have revealed that the cortical connectivity structure is dynamic, with dendritic spines being added and deleted on the time scale of hours to days (Holtmaat et al, 2005; Stettler et al, 2006; Kasai et al, 2010; Loewenstein et al, 2011; Rumpel and Triesch, 2016). It has been proposed that this ongoing cortical rewiring serves important functions for learning and memory (Chklovskii et al, 2004; DeBello, 2008). According to this view, synaptic rewiring defines the connectivity structure of cortical circuits and interacts with synaptic plasticity of established synaptic connections. Rewiring can shape the connectivity structure on the sub-cellular level, defining the dendritic targets of synaptic connections onto pyramidal cells (PCs). There, it was shown that a supervised structural plasticity rule can optimize memory performance of a simple nonspiking model of a pyramidal cell with non-linear dendrites. Legenstein and Maass (2011) showed that branch-specific synaptic plasticity—without synaptic rewiring—can self-organize non-linear computations in neurons with non-linear branches
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