Comprehensive Estimates of Potential Synaptic Connections in Local Circuits of the Rodent Hippocampal Formation by Axonal-Dendritic Overlap

Abstract

A quantitative description of the hippocampal formation synaptic architecture is essential for understanding the neural mechanisms of episodic memory. Yet the existing knowledge of connectivity statistics between different neuron types in the rodent hippocampus only captures a mere 5% of this circuitry. We present a systematic pipeline to produce first-approximation estimates for most of the missing information. Leveraging the www.Hippocampome.org knowledge base, we derive local connection parameters between distinct pairs of morphologically identified neuron types based on their axonal-dendritic overlap within every layer and subregion of the hippocampal formation. Specifically, we adapt modern image analysis technology to determine the parcel-specific neurite lengths of every neuron type from representative morphologic reconstructions obtained from either sex. We then compute the average number of synapses per neuron pair using relevant anatomic volumes from the mouse brain atlas and ultrastructurally established interaction distances. Hence, we estimate connection probabilities and number of contacts for .1900 neuron type pairs, increasing the available quantitative assessments more than 11-fold. Connectivity statistics thus remain unknown for only a minority of potential synapses in the hippocampal formation, including those involving long-range (23%) or perisomatic (6%) connections and neuron types without morphologic tracings (7%). The described approach also yields approximate measurements of synaptic distances from the soma along the dendritic and axonal paths, which may affect signal attenuation and delay. Overall, this dataset fills a substantial gap in quantitatively describing hippocampal circuits and provides useful model specifications for biologically realistic neural network simulations, until further direct experimental data become available.

An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo

“…Hippocampome.org v.1.8 [ 23 ] defines 122 neuron types from the dentate gyrus (DG), CA3, CA2, CA1, the subiculum, and entorhinal cortex. Every type is characterized by a set of unique properties always including area- and laminar-specific axonal and dendritic patterns as well as its main neurotransmitter.…”

A model of the CA1 field rhythms

“…Neuron models were taken from articles (Bezaire, Raikov, Burk, Vyas, & Soltesz, 2016;Cutsuridis & Poirazi, 2015). The connections between the neurons were taken based on the database "hippocampome" (Tecuatl, Wheeler, Sutton, & Ascoli, 2020), GABA-A receptors were modeled -for inhibitory synapses, nicotine receptors, and AMPAR for excitatory connections. Gap junctions between PV basket cells and between neurogiaform cells were included in the model in accordance with the experimental data (Fukuda & Kosaka, 2000, 2003Price et al, 2005).…”

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

“…A multiplicative scaling factor of 2.44 was applied to convert mouse data to rat (Herculano-Houzel et al, 2006). Numerical densities were converted into neuronal numbers by multiplying with volumes of the corresponding anatomical parcels (Ero et al, 2018;Tecuatl et al, 2021).…”

“…This resource defines 122 neuron types based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites (Wheeler et al, 2015). Hippocampome.org further annotates every neuron type with its reported connectivity (Rees et al, 2016), electrophysiological (Komendantov et al, 2019), molecular (White et al, 2020), synaptic (Moradi et al, 2020), morphological (Tecuatl et al, 2021), and functional (Sanchez-Aguilera et al, 2021) properties, in all cases providing links to the underlying experimental evidence. The ultimate goal of Hippocampome.org is to create biologically plausible computational models of the hippocampus (Venkadesh et al, 2019).…”