The Effects of Realistic Synaptic Distribution and 3D Geometry on Signal Integration and Extracellular Field Generation of Hippocampal Pyramidal Cells and Inhibitory Neurons

Abstract

In vivo and in vitro multichannel field and somatic intracellular recordings are frequently used to study mechanisms of network pattern generation. When interpreting these data, neurons are often implicitly considered as electrotonically compact cylinders with a homogeneous distribution of excitatory and inhibitory inputs. However, the actual distributions of dendritic length, diameter, and the densities of excitatory and inhibitory input are non-uniform and cell type-specific. We first review quantitative data on the dendritic structure and synaptic input and output distribution of pyramidal cells (PCs) and interneurons in the hippocampal CA1 area. Second, using multicompartmental passive models of four different types of neurons, we quantitatively explore the effect of differences in dendritic structure and synaptic distribution on the errors and biases of voltage clamp measurements of inhibitory and excitatory postsynaptic currents. Finally, using the 3-dimensional distribution of dendrites and synaptic inputs we calculate how different inhibitory and excitatory inputs contribute to the generation of local field potential in the hippocampus. We analyze these effects at different realistic background activity levels as synaptic bombardment influences neuronal conductance and thus the propagation of signals in the dendritic tree. We conclude that, since dendrites are electrotonically long and entangled in 3D, somatic intracellular and field potential recordings miss the majority of dendritic events in some cell types, and thus overemphasize the importance of perisomatic inhibitory inputs and belittle the importance of complex dendritic processing. Modeling results also suggest that PCs and inhibitory neurons probably use different input integration strategies. In PCs, second- and higher-order thin dendrites are relatively well-isolated from each other, which may support branch-specific local processing as suggested by studies of active dendritic integration. In the electrotonically compact parvalbumin- and cholecystokinincontaining interneurons, synaptic events are visible in the whole dendritic arbor, and thus the entire dendritic tree may form a single integrative element. Calretinin-containing interneurons were found to be electrotonically extended, which suggests the possibility of complex dendritic processing in this cell type. Our results also highlight the need for the integration of methods that allow the measurement of dendritic processes into studies of synaptic interactions and dynamics in neural networks.

Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States

“…The fact that signals overlap makes these recordings difficult to parse, i.e., it isn’t clear which populations are involved in different kinds of activity (Kajikawa and Schroeder, 2011; Buzsáki et al, 2012; Kajikawa and Schroeder, 2015). Current source density (CSD) analysis helps reduce signal spread by focusing on current sinks and sources, but it may still be difficult to parse which cell populations are involved, particularly in the neocortex where neuronal populations are very dense (Gulyás et al, 2016).…”

Different output properties of perisomatic region‐targeting interneurons in the basal amygdala

“…The firing of CCK/CB1BCs might be driven either in a feedforward manner (Basu et al, 2013) or by co-activation of feedforward and feedback excitatory circuits (Glickfeld & Scanziani, 2006). Although the conductance values at single excitatory synapses received by CCK/CB1BCs are unknown, it is likely that more simultaneously active PNs are needed to discharge this BC type than PV-expressing interneurons (Gulyas et al, 2016).…”

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

“…• relational biomarker expression inferences (White et al, 2020) v1.6 • firing pattern phenotypes (Gulyás et al, 2016) Lists of subthreshold physiological properties for multicompartmental modeling (Skene and Grant, 2016) Catalog of CA1 Interneuron types (Faghihi and Moustafa, 2017) Diversity of hippocampal neuron types and morphological neuronal features (Puighermanal et al, 2017) Biomarker expression in CA1 interneurons (Depannemaecker et al, 2020) Parameter values for a model of synaptic neurotransmission (Ecker et al, 2020) Evidence that CA1 interneurons express multiple overlapping chemical markers (Hunsberger and Mynlieff, 2020) Cell identification based on firing properties (Schumm et al, 2020) Directionality of connections in the hippocampus (Aery Jones et al, 2021) Local connectivity of CA1 PV+ interneurons (Ciarpella et al, 2021) Lists of hippocampal genes (Luo et al, 2021) Confirmation of multiple hippocampal neuron types (Mehta et al, 2021) Connectome model inspired by entorhinal-CA1 circuit (Obafemi et al, 2021) Principal channels of information processing are DG Granule cells and CA1-3 Pyramidal cells (Sáray et al, 2021) Membrane biophysics values for CA1 Pyramidal cells (Smith et al, 2021) Omni-directionality of axons of CA1 Pyramidal cells (Venkadesh and Van Horn, 2021) Example of a brain region's mesoscopic structural connectivity (Walker et al, 2021) Reference to morphological and molecular characteristics of hippocampal principal cells and interneurons (Wynne et al, 2021) Example brain region with a variety of cell types (Kopsick et al, 2022) Utilize accumulated knowledge as the basis for simulations (Schumm et al, 2022) Hippocampal morphology, biomarker expression, connectivity, and typing of neurons (Zagrean et al, 2022) Diversity of hippocampal neuronal types and their properties…”