Abstract
The strength of cortical synapses distributes lognormally, with a long tail of strong synapses. Various properties of neuronal activity, such as the average firing rates of neurons, the rate and magnitude of spike bursts, the magnitude of population synchrony, and the correlations between presynaptic and postsynaptic spikes, also obey lognormal-like distributions reported in the rodent hippocampal CA1 and CA3 areas. Theoretical models have demonstrated how such a firing rate distribution emerges from neural network dynamics. However, how the other properties also display lognormal patterns remain unknown. Because these features are likely to originate from neural dynamics in CA3, we model a recurrent neural network with the weights of recurrent excitatory connections distributed lognormally to explore the underlying mechanisms and their functional implications. Using multi-timescale adaptive threshold neurons, we construct a low-frequency spontaneous firing state of bursty neurons. This state well replicates the observed statistical properties of population synchrony in hippocampal pyramidal cells. Our results show that the lognormal distribution of synaptic weights consistently accounts for the observed long-tailed features of hippocampal activity. Furthermore, our model demonstrates that bursts spread over the lognormal network much more effectively than single spikes, implying an advantage of spike bursts in information transfer. This efficiency in burst propagation is not found in neural network models with Gaussian-weighted recurrent excitatory synapses. Our model proposes a potential network mechanism to generate sharp waves in CA3 and associated ripples in CA1 because bursts occur in CA3 pyramidal neurons most frequently during sharp waves.
SIGNIFICANCE STATEMENT The wiring structure of local cortical networks is known to be far from random. Here, we propose a recurrent neural network model with a long-tailed synaptic weight distribution to account for various properties of the synchronous firing of hippocampal neurons observed typically during sharp wave ripples. Furthermore, the model predicts that pathways of strong synapses route spike bursts much more efficiently than single spikes. Sharp wave ripples are crucial for memory encoding, but the underlying mechanism remains unknown. Our model suggests the crucial role of internal dynamics of nonrandom hippocampal circuits in generating and routing such activity patterns. Our results will have significant implications in understanding the mechanism of memory encoding by the hippocampus.
