Cellular and network mechanisms may generate sparse coding of sequential object encounters in hippocampal-like circuits

Description
Title: Cellular and network mechanisms may generate sparse coding of sequential object encounters in hippocampal-like circuits
Authors: Trinh, Anh-Tuan
Clarke, Stephen E.
Harvey-Girard, Erik
Maler, Leonard
Date: 2019
Abstract: The localization of distinct landmarks plays a crucial role in encoding new spatial memories. In mammals, this function is performed by hippocampal neurons that sparsely encode an animal’s location relative to surrounding objects. Similarly, the dorsal lateral pallium (DL) is essential for spatial learning in teleost fish. The DL of weakly electric gymnotiform fish receives both electrosensory and visual input from the preglomerular nucleus (PG), which has been hypothesized to encode the temporal sequence of electrosensory or visual landmark/food encounters. Here, we show that DL neurons in the Apteronotid fish and in the Carassius auratus (goldfish) have a hyperpolarized resting membrane potential combined with a high and dynamic spike threshold that increases following each spike. Current-evoked spikes in DL cells are followed by a strong small-conductance calcium-activated potassium channel (SK) mediated after-hyperpolarizing potential (AHP). Together, these properties prevent high frequency and continuous spiking. The resulting sparseness of discharge and dynamic threshold suggest that DL neurons meet theoretical requirements for generating spatial memory engrams by decoding the landmark/food encounter sequences encoded by PG neurons. Thus, DL neurons in teleost fish may provide a promising, simple system to study the core cell and network mechanisms underlying spatial memory.
URL: http://hdl.handle.net/10393/39306
DOI: 10.20381/ruor39306
CollectionMédecine cellulaire et moléculaire // Cellular and Molecular Medicine
Files
h_inf.mMatlab action potential model 1153 BMATLABOpen
iEIF.mMatlab action potential model 25.45 kBMATLABOpen
vline.mMatlab action potential model 32.95 kBMATLABOpen