Silent synapses generate sparse and orthogonal action potential firing in adult-born hippocampal granule cells

Abstract

In adult neurogenesis young neurons connect to the existing network via formation of thousands of new synapses. At early developmental stages, glutamatergic synapses are sparse, immature and functionally 'silent', expressing mainly NMDA receptors. Here we show in 2- to 3-week-old young neurons of adult mice, that brief-burst activity in glutamatergic fibers is sufficient to induce postsynaptic AP firing in the absence of AMPA receptors. The enhanced excitability of the young neurons lead to efficient temporal summation of small NMDA currents, dynamic unblocking of silent synapses and NMDA-receptor-dependent AP firing. Therefore, early synaptic inputs are powerfully converted into reliable spiking output. Furthermore, due to high synaptic gain, small dendritic trees and sparse connectivity, neighboring young neurons are activated by different distinct subsets of afferent fibers with minimal overlap. Taken together, synaptic recruitment of young neurons generates sparse and orthogonal AP firing, which may support sparse coding during hippocampal information processing.

Ion-channel regulation of response decorrelation in a multi-scale model of the dentate gyrus

“…(iv) input-driven or afferent heterogeneity, where all neurons in the GC and BC populations received either identical inputs (absence of afferent heterogeneity) from the EC, or each GC and BC received unique inputs (presence of afferent heterogeneity) from the EC (Mishra and Narayanan, 2019). Afferent heterogeneity models incorporate sparse and orthogonal afferent connectivity from the EC to the DG (Aimone et al, 2006;Andersen et al, 2006;Amaral et al, 2007;Aimone et al, 2009;Aimone et al, 2010Aimone et al, 2014;Li et al, 2017;Lodge and Bischofberger, 2019).…”

“…We tested the impact of virtually knocking out individual ion channels in networks endowed with different combinations of biological heterogeneities, to ensure that our conclusions were not reflections of narrow parametric choices and to ask if the expression of heterogeneities enhances the robustness of the network to ion channel perturbations. There are several lines of evidence that the synaptic connectivity to immature neurons are low, and that this low connectivity counterbalances their high excitability (Mongiat et al, 2009;Dieni et al, 2013;Dieni et al, 2016;Li et al, 2017). To account for these, we reduced the overall afferent drive in scenarios that involved neurogenesis-induced structural differences (i.e., the fully immature population or the heterogeneous age population).…”

“…Uniquely, with reference to afferent connectivity, adult neurogenesis provides a substrate for orthogonal, non-overlapping processing and storage of afferent information from upstream cortex. The expression of these amplified heterogeneities, in conjunction with specific characteristics of the local network has resulted in postulates and lines of evidence that the DG network is an ideal system to execute channel decorrelation and pattern separation (Aimone et al, 2006;Leutgeb et al, 2007;Aimone et al, 2009;Sahay et al, 2011;Dieni et al, 2013;Aimone et al, 2014;Kropff et al, 2015;Li et al, 2017;Lodge and Bischofberger, 2019;Mishra and Narayanan, 2019).…”

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

“…Importantly, this does not preclude that additional, long range projections may add further complexity 74 . Also note that in addition to the instantaneous pattern separation mechanisms investigated here, potentially complementary mechanisms at much longer time scales have been proposed involving ongoing neurogenesis [75][76][77][78][79][80] .…”

Plasticity manifolds: Conjunctive changes in multiple ion channels mediate activity-dependent plasticity in hippocampal granule cells

“…The concomitant roles of synaptic plasticity and neuron-specific intrinsic plasticity as putative cellular substrates of learning and memory are well established (Zhang and Linden, 2003;Kim and Linden, 2007;Johnston and Narayanan, 2008;Nelson and Turrigiano, 2008;Neves et al, 2008;Mozzachiodi and Byrne, 2010;Mayford et al, 2012;Narayanan and Johnston, 2012;Titley et al, 2017;Lisman et al, 2018;Rathour and Narayanan, 2019). The DG has been implicated in spatial navigation, response decorrelation, pattern separation, engram formation, learning and memory (Bliss and Lomo, 1973;Amaral et al, 2007;Leutgeb et al, 2007;McHugh et al, 2007;Bakker et al, 2008;Sahay et al, 2011;Aimone et al, 2014;Neunuebel and Knierim, 2014;Kropff et al, 2015;Diamantaki et al, 2016;Heigele et al, 2016;Danielson et al, 2017;GoodSmith et al, 2017;Li et al, 2017;Senzai and Buzsaki, 2017;Tonegawa et al, 2018;Mishra and Narayanan, 2019). The mechanisms behind and implications for synaptic plasticity to these physiological roles of the DG have been thoroughly investigated (Bliss and Lomo, 1973;Schmidt-Hieber et al, 2004;Govindarajan et al, 2006;McHugh et al, 2007;Aimone et al, 2014;Tonegawa et al, 2018).…”