Population and individual firing behaviors in sparsely synchronized rhythms in the hippocampal dentate gyrus

Abstract

We investigate population and individual firing behaviors in sparsely synchronized rhythms (SSRs) in a spiking neural network of the hippocampal dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all competition, leading to sparse activation of the GCs, occurs in each GC cluster. Such sparsity has been thought to enhance pattern separation performed in the DG. During the winner-take-all competition, SSRs are found to appear in each population of the GCs and the BCs through interaction of excitation of the GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear successively with the population frequency fp (= 13 Hz) in the raster plots of spikes. We also note that excitatory hilar mossy cells (MCs) control the firing activity of the GC-BC loop by providing excitation to both the GCs and the BCs. SSR also appears in the population of MCs via interaction with the GCs (i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively characterized in terms of the synchronization measures. In addition, we investigate individual firing activity of GCs, BCs, and MCs in the SSRs. Individual GCs exhibit random spike skipping, leading to a multi-peaked interspike-interval histogram, which is well characterized in terms of the random phase-locking degree. In this case, population-averaged mean-firing-rate (MFR) f (GC) i is less than the population frequency fp. On the other hand, both BCs and MCs show "intrastripe" burstings within stripes, together with "interstripe" random spike skipping. Thus, the population-averaged MFR f (X) i (X = MC and BC) is larger than fp, in contrast to the case of the GCs. MC loss may occur during epileptogenesis. With decreasing the fraction of the MCs, changes in the population and individual firings in the SSRs are also studied. Finally, quantitative association between the population/individual firing behaviors in the SSRs and the winner-take-all competition is discussed.

Effect of Adult-Born Immature Granule Cells on Pattern Separation in The Hippocampal Dentate Gyrus

“…Based on the anatomical information given in [16][17][18][19]21], we choose the numbers of the GCs, BCs, MCs, and HIPP cells in the DG and the EC cells. As in our prior works [44,56,57], we develop a scaled-down spiking neural network where the total number of excitatory GCs (N GC ) is 2,000, corresponding to 1 500 of the 10 6 GCs found in rats [61]. The fraction of imGCs in the whole population of the GCs is 10 %, and hence the number of the imGCs (mGCs) is 200 (1800).…”

“…As elements of our DG spiking neural network for the adult neurogenesis, we choose leaky integrate-and-fire (LIF) neuron models with additional afterhyperpolarization (AHP) currents which determines refractory periods, as in our prior DG networks [44,56,57]. This LIF neuron model is one of the simplest spiking neuron models [77].…”

“…(11) Here, Θ(t) is the Heaviside step function: Θ(t) = 1 for t ≥ 0 and 0 for t < 0, and τ In comparison with our prior DG networks [44,56,57], we include more synaptic connections with a high degree of anatomical and physiological realism [58,59], and incorporate the imGCs. Thus, a new feedforward inhibition, mediated by the BCs, is provided to the mGCs, and there appear two feedback loops of mGC-BC and mGC-HIPP, (projecting feedback inhibition to the mGCs), the activities of which are controlled by the two control loops of MC-BC and MC-HIPP (MCs: controllers).…”

“…In this section, we describe our spiking neural network for the adult neurogenesis in the DG. Based on the anatomical and the physiological properties described in [16, 17, 21], we developed the DG spiking neural networks in the works for the winner-take-all competition [44], the sparsely synchronized rhythm [56], and the pattern separation [57]. Here, we first refine our prior spiking neural networks to include more synaptic connections with a high degree of anatomical and physiological realism [58, 59], and then incorporate the young adult-born imGCs to complete structure of our spiking neural network for the adult neurogenesis.…”

“…With the above information on the numbers of the relevant cells and the synaptic connections between them, we develop a one-dimensional ring network for the adult neurogenesis in the DG, as in our prior works [44, 56, 57]; e.g., refer to Figs. 1(b1)-1(b3) in [57] for the schematic diagrams of the ring networks.…”