Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

Abstract

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits.

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

“…5A-B), as was shown before in [20] and [18] , in which eSTDP and SN were 316 complemented with structural plasticity. We here use a different approach, leaving out 317 structural plasticity, but applying a small bias in the eSTDP rule, in favour of 318 potentiation.…”

“…Lognormal-like distributions have been observed in the weights of cortical [5,8] [18,23,56]). We conclude that I-E and E-E 344 weights in our network may form through the combination of potentiating STDP and 345 SN a right-skewed long-tailed distribution that resembles a lognormal distribution, but 346 as in the experimental observations, is not in fact precisely and entirely lognormal.…”

“…[18,20]) there is no intrinsic homeostatic plasticity 91 and no structural plasticity in this model. This choice was made in order to allow a 92 broad distribution of firing rates to emerge from Gaussian variability in spiking 93 thresholds, and to demonstrate that the STDP rules and the synaptic normalisation are 94 sufficient to account for stable activity levels.…”

Predicting the Dynamics of Network Connectivity in the Neocortex

“…For example, it has been shown that the age (Grutzendler et al, 2002;Trachtenberg et al, 2002;Holtmaat et al, 2005;Zuo et al, 2005;Keck et al, 2008) and size (Grutzendler et al, 2002;Holtmaat et al, 2005;Zuo et al, 2005;Majewska et al, 2006) of the spine are negatively correlated with its turnover rate. This suggests that distinct morphological features of a spine could be used as predictors of their stability.…”

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

“…Various regulatory mechanisms preventing this instability have been proposed theoretically [14,31,[33][34][35][36] and found experimentally [37][38][39]. Synaptic scaling [37,40] operates on a timescale of hours to days [38] by reducing all the afferent synapses to a given neuron by the same amount so that relative differences in synaptic strengths are preserved.…”