Spiking neural networks and hippocampal function: A web-accessible survey of simulations, modeling methods, and underlying theories

Sutton, Nate; Ascoli, Giorgio A.

doi:10.1016/j.cogsys.2021.07.008

Cited by 11 publications

(11 citation statements)

References 142 publications

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“…A recent review of publications on simulations of the hippocampal formation identified CAN grid cell models as a notable case relevant to spatial navigation (Sutton & Ascoli, 2021). Here, we used Hippocampome.org parameters to build a new SNN of grid cells adopting multiple methods from previous CAN models (Burak and Fiete, 2009; Solanka et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

Sutton,

Gutiérrez-Guzmán,

Dannenberg

et al. 2024

Preprint

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Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal was to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software is released as open source to enable broad community reuse and encourage novel applications.

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Section: Methodsmentioning

confidence: 99%

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

Sutton,

Gutiérrez-Guzmán,

Dannenberg

et al. 2024

Preprint

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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%

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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“…OpenWorm, for example, is a successful, decade-long community project to create and simulate a realistic, data-driven reference model of the roundworm Caenorhabditis elegans ( C. elegans ) including its neural circuitry of ∼ 302 neurons to study the behavior of this relatively simple organism in silico (Szigeti et al, 2014; Gerkin et al, 2018). By contrast, for the hippocampus, with a circuit many orders of magnitude larger than C. elegans , models have typically been constructed with a minimal circuit structure on a relatively small scale and often their model parameters have been tuned with the goal of reproducing a single empirical phenomenon (see Sutton and Ascoli, 2021). Comparing the results from a variety of circuit models is problematic because they vary in their degree of realism and frequently rely on one or a few single neuron models making generalization of their findings difficult (see Sutton and Ascoli, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, for the hippocampus, with a circuit many orders of magnitude larger than C. elegans , models have typically been constructed with a minimal circuit structure on a relatively small scale and often their model parameters have been tuned with the goal of reproducing a single empirical phenomenon (see Sutton and Ascoli, 2021). Comparing the results from a variety of circuit models is problematic because they vary in their degree of realism and frequently rely on one or a few single neuron models making generalization of their findings difficult (see Sutton and Ascoli, 2021). While these focused models have led to valuable insights (see M. E. Hasselmo et al, 2020), this piecemeal approach fails to demonstrate whether these separate phenomena can be reproduced in a full circuit model without the need to adjust parameters.…”

Section: Introductionmentioning

confidence: 99%

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Romani,

Antonietti,

Bella

et al. 2023

Preprint

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The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it can be challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scalein silicomodel of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We have tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extendin vitroandin vivoexperiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

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Spiking neural networks and hippocampal function: A web-accessible survey of simulations, modeling methods, and underlying theories

Cited by 11 publications

References 142 publications

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

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

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

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