Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus

Kopsick, Jeffrey D.; Tecuatl, Carolina; Moradi, Keivan; Attili, Sarojini M.; Kashyap, Hirak J.; Xing, Jinwei; Chen, Kexin; Krichmar, Jeffrey L.; Ascoli, Giorgio A.

doi:10.1007/s12559-021-09954-2

Cited by 15 publications

(21 citation statements)

References 76 publications

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“…Although the above properties were largely measured from slice preparations, v1.9 made available measurements from in vivo recordings (Sanchez-Aguilera et al, 2021;hippocampome.org/in-vivo). Release v1.10 provided a compendium of cognitive functions linked to specific hippocampal neurons (Sutton and Ascoli, 2021; hippocampome.org/cognome), while the v1.11 neuron type census estimated the population counts for each neuron type (Attili et al, 2022; hippocampome.org/census). Finally, there are a set of properties pertaining not to individual neuron types but to synaptic connections between a pair of pre-and post-synaptic neuron types.…”

Section: Characterizing Properties Of Hippocampal Neuron Typesmentioning

confidence: 99%

“…An ongoing line of research in this regard focuses on a real-scale mouse model of CA3, with the eventual goal of investigating the cellular mechanisms of pattern completion. Initial work included excitatory Pyramidal cells and seven main inhibitory interneuron types: Axo-axonic cells (AAC), Basket cells (BC), Basket Cholecystokinin-positive cells (BC CCK+), Bistratified cells (BiC), Ivy cells, Mossy Fiber-Associated ORDEN (MFA ORDEN) cells, and QuadD-LM (QuadD) cells (Kopsick et al, 2022). Use of Hippocampome.org parameters for cell census, Izhikevich models, synaptic signals, and connection probabilities resulted in robust, realistic, rhythmic resting state activity for all neuron types (Figure 9A).…”

Section: Potential Applications To Connectomic Analyses and Spiking N...mentioning

confidence: 99%

“…features not yet released in the main CARLsim version, can be found at hippocampome.org/CARLsim (Kopsick et al, 2022).…”

Section: Constructing Hippocampomeorg Spiking Neural Simulationsmentioning

confidence: 99%

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

Wheeler

Kopsick

Sutton

et al. 2023

Preprint

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Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, incorporates over 50 new neuron types and extends the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

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Section: Characterizing Properties Of Hippocampal Neuron Typesmentioning

confidence: 99%

Section: Potential Applications To Connectomic Analyses and Spiking N...mentioning

confidence: 99%

See 1 more Smart Citation

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

Wheeler

Kopsick

Sutton

et al. 2023

Preprint

Self Cite

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“…Computational modeling of cortical dynamics that incorporates mesoscopic-scale connectivity features can offer a powerful framework for investigating neural network mechanisms in normal and pathological conditions (Venkadesh & Van Horn, 2021). However, current approaches for modeling the mesoscopic-scale dynamics of brain regions (Kopsick et al, 2022; Markram et al, 2015) are limited by the lack of an integrative concept (Frégnac, 2017) that coherently unifies the multitude of mechanisms that are present in real neural networks. This is primarily due to limited theoretical accounts for the emergent dynamical complexities in a network in terms of its constituent mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…Computational modeling of neuronal dynamics which incorporates mesoscopic-level connectivity features can potentially offer powerful frameworks for investigating neural network mechanisms [1,2]. However, a scalable network representation of key neurodynamical features at the mesoscopic level is currently lacking.…”

Section: Introductionmentioning

confidence: 99%

A spiking neural network model of cortical intraregional metastability

Venkadesh

Shaikh

Shakeri

et al. 2022

Preprint

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Transient synchronization of bursting activity in neural networks, which occurs in patterns of metastable phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neural circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, emerges in an inhibitory neural population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neural population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of neural oscillations and the features of emergent metastability. In addition, a novel metric is formulated to characterize collective transitions in metastable networks. Finally, we discuss potential applications of our compact representation of mesoscopic network metastability in modeling whole-brain resting-state dynamics using empirically estimated anatomical connectivity.

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Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

Kopsick,

Kilgore,

Adam

et al. 2024

J Comput Neurosci

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The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

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Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus

Cited by 15 publications

References 76 publications

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

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

A spiking neural network model of cortical intraregional metastability

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

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