Abstract
The membrane potentials of cortical neurons in vivo exhibit spontaneous fluctuations between a depolarized UP state and a resting DOWN state during the slow-wave sleeps or in the resting states. This oscillatory activity is believed to engage in memory consolidation although the underlying mechanisms remain unknown. Recently, it has been shown that UP-DOWN state transitions exhibit significantly different temporal profiles in different cortical regions, presumably reflecting differences in the underlying network structure. Here, we studied in computational models whether and how the connection configurations of cortical circuits determine the macroscopic network behavior during the slow-wave oscillation. Inspired by cortical neurobiology, we modeled three types of synaptic weight distributions, namely, log-normal, sparse log-normal and sparse Gaussian. Both analytic and numerical results suggest that a larger variance of weight distribution results in a larger chance of having significantly prolonged UP states. However, the different weight distributions only produce similar macroscopic behavior. We further confirmed that prolonged UP states enrich the variety of cell assemblies activated during these states. Our results suggest the role of persistent UP states for the prolonged repetition of a selected set of cell assemblies during memory consolidation.
Highlights
Various types of oscillations appear in neural systems depending on the state of the animal[1]
While the temporal patterns are synchronized in LECIII with those of UP-DOWN transitions in neocortical areas, UP states often continue in MECIII during several cycles of neocortical UP-DOWN transitions
We constructed three neural network models with different distributions of synaptic weights and investigated the statistical properties of UP-DOWN state transitions generated by these models
Summary
We use randomly-connected-recurrent networks of spiking neurons. Our spiking neuron model is based on the adaptive exponential integrate-and-fire model (AdEx)[18]. One should note that γE/γI can be interpreted as the excitatory-inhibitory ratio for physical connections, e.g. number of synapses and destiny of spines It should not be confused with the postsynaptic current ratio reported by Beed et al.[26]. The strength of connections between neurons, JψEE, JψEI, JψIE and JψII, obeys one of the following statistical distributions: (1) log-normal distribution, (2) sparse-Gaussian distribution and (3) sparse-log-normal distribution. Since the inhibitory connection has a smaller variance (σ2 = 1.0), configuration detail of the excitatory input towards inhibitory neurons should not make a significant difference to the inhibitory feedback This setting enabled us to simplify the analysis of the model using the mean-field analysis to search for condition for UP-state occurrence.
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