Cristina Vittoria Dieni

Cristina Vittoria Dieni

University of Perugia, Italy

Title: Low excitatory innervation of immature neurons enhances pattern separation


Cristina Vittoria Dieni has completed her PhD at the Department of Physiology of University of Perugia (Italy) and the Post-doctoral studies at the Department of Neurobiology of University of Alabama at Birmingham (USA). She is a Research Associate at the Department of Experimental Medicine of University of Perugia School of Medicine. She has published more than 14 papers in reputed journals and has been serving as an Editorial Board Member of repute.


One hallmark of neural activity in the dentate gyrus (DG) is sparse population coding such that only a few percentage of the principle granule cells (GCs) are activated during sensory stimulation. Sparse activation is evident by the minimal activation of GCs in response to afferent input from the perforant path that is primarily maintained by strong synaptic inhibition provided by local GABAergic interneurons. Within the DG, adult neurogenesis continually produces a small population of immature GCs whit high intrinsic excitability and low levels of inhibition that are predicted to be more responsive to afferent inputs from perforant path than pre-existing mature GCs. It has been suggested that the immature GCs are necessary for generating distinct neural representations of similar contexts process also known as pattern separation. But it is surprising that broadly responsive neurons contribute to the pattern discrimination since in network models the addition of excitable immature GCs degrades rather than improve pattern separation. Yet, it is still unclear how immature GCs contribute to DG network activity and pattern discrimination. Here we show that immature GCs display low excitatory innervation that limits their recruitment by stimulation of the perforant path. Moreover using a statistical model that focuses on excitatory synaptic connectivity we found that immature GCs with low connectivity expand the dynamic range of effective pattern separation during low levels of cortical activity, with a small percentage of immature neurons optimal for expansion. Our results predict that small numbers of excitable but poorly innervated immature GCs can facilitate input-output transformations in the DG by maintaining discrete network representations during low levels of entorhinal cortex activity.