Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents

Abstract

The topographic organization of afferents to the hippocampal CA3 subfield are well-studied, but their role in influencing the spatiotemporal dynamics of population activity is not understood. Using a large-scale, computational neuronal network model of the entorhinal-dentate-CA3 system, the effects of the perforant path, mossy fibers, and associational system on the propagation and transformation of network spiking patterns were investigated. A correlation map was constructed to characterize the spatial structure and temporal evolution of pairwise correlations which underlie the emergent patterns found in the population activity. The topographic organization of the associational system gave rise to changes in the spatial correlation structure along the longitudinal and transverse axes of the CA3. The resulting gradients may provide a basis for the known functional organization observed in hippocampus.

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

“…Linearly unreliable connections (R 2 < 0.1) with slopes below 0.1 might suggest a plethora of weak synapses or targets that require large numbers of synchronous inputs to achieve target neuron spiking (Poli et al, 2018a;Sherrill et al, 2020). Such weak pairwise correlations that we found here that differed between subregions provide the underpinning of emergent spatiotemporal patterns of population activity, as highlighted by Yu et al (2020) in earlier studies by Halliday (2000); Schneidman et al (2006), Kriener et al (2009), and Renart et al (2010). Earlier studies also found evidence for sharing propagation of the correlation through multiple layers (Kumar et al, 2010;Rosenbaum and Josic, 2011;Rosenbaum et al, 2017;Darshan et al, 2018).…”

“…Not surprisingly, large fractions of target neurons were stimulated in the CA3 or CA1 to produce a population spike, but not spike dynamics of individual neurons. Only recently has a cluster of target responses been associated with the spatial extent of each terminal field of axons (Hendrickson et al, 2016), followed by appreciation of differences in topographic organization along the longitudinal and traverse axes of the CA3 subregion (Yu et al, 2020). Pairwise spike correlations between cortical neurons in close proximity capture most statistical properties of a single neuron and provide a measure of population activity (Helias et al, 2014;Dettner et al, 2016).…”

“…The patterns of feed-forward and feedback dynamics among different subregions of the hippocampus may hold the key to memory processing and consolidation for brain circuit prosthesis (Song et al, 2018). The feedback pathways are often related to the control of forwarding neuronal transmission, particularly in modulating excessive excitatory stimulations downstream (Yu et al, 2020). Repeating stimulation patterns, once learned, may elicit feedback transmission to decrease further stimulation of the same patterns to preserve neural subnetwork bandwidth and, thus, improve efficiency (Bein et al, 2020).…”

An inhibitory plasticity mechanism for world structure inference by hippocampal replay

“…We next asked whether sSTDP of recurrent E→E and I→E synapses is sufficient to suppress distractor inputs during offline replay in a biophysical model of the hippocampal CA3 network. We modeled CA3 using 260 multicomparmental and biophysically-detailed pyramidal neurons (Yu et al 2020) and 30 interneurons with biophysically realistic electrophysiological properties (Bezaire et al 2016, Bezaire & Soltesz 2013) (see Methods ). During online learning, PCs and INs receive input structured into distinct dendritic input streams, consistent with in vivo data (Losonczy et al 2008, Makara & Magee 2013, Druckmann et al 2014, Bezaire & Soltesz 2013, Adoff et al 2021).…”

“…We next asked whether sSTDP of recurrent E→E and I→E synapses is sufficient to suppress distractor inputs during offline replay in a biophysical model of the hippocampal CA3 network. We modeled CA3 using 260 multicomparmental and biophysically-detailed pyramidal neurons (Yu et al 2020) and 30 interneurons with biophysically realistic electrophysiological properties (Bezaire et Specifically, 80% of PCs receive spatially structured place and grid input via a mossy fiber and medial entorhinal cortical (MEC) pathway, respectively (Knierim 2015, Moser et al 2008, Knierim et al 2014. The remaining 20% of PCs receive a distractor input via a lateral entorhinal cortical (LEC) pathway (Knierim et al 2014) (Fig 2a).…”