Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types

Nandi, Anirban; Chartrand, Thomas; Geit, Werner Van; Buchin, Anatoly; Yao, Zizhen; Lee, Soo Yeun; Wei, Yina; Kalmbach, Brian; Lee, Brian; Lein, Ed; Berg, Jim; Sümbül, Uygar; Koch, Christof; Tasic, Bosiljka; Anastassiou, Costas A.

doi:10.1016/j.celrep.2022.111176

Cited by 26 publications

(21 citation statements)

References 73 publications

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“…We hope to fully automate this process and make it "self-service" in future versions of the site. As large-scale modeling efforts increasingly accompany high-throughput experiments [77], such automation will become critical.…”

Section: Discussionmentioning

confidence: 99%

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

et al. 2023

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As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.

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Section: Discussionmentioning

confidence: 99%

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

et al. 2023

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“…Our study highlighted for the first time that it is feasible to use a computational model in order to reproduce the spiking activities observed across a given sensory neural population in vivo. As mentioned above, previous studies have used similar approaches, but instead focused on experimental data obtained in vitro (24, 25). Specifically, a recent modeling effort has shown that computational modeling, parameterized by in vitro data, can allow for the detection of causal relationships between transcriptomics, morphology, intrinsic membrane conductances, and electrophysiology in the mouse visual cortex (24).…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, previous studies have used similar approaches, but instead focused on experimental data obtained in vitro (24, 25). Specifically, a recent modeling effort has shown that computational modeling, parameterized by in vitro data, can allow for the detection of causal relationships between transcriptomics, morphology, intrinsic membrane conductances, and electrophysiology in the mouse visual cortex (24). Another recent modeling effort has successfully shown that calcium-activated potassium channels play a critical role towards mediating burst firing in CA3 pyramidal cells (25).…”

Section: Discussionmentioning

confidence: 99%

“…One potent approach to understanding how different factors contribute to spiking heterogeneities is computational modeling. Specifically, recent approaches have shown that computational models can be successfully used to link electrophysiological properties with different factors, including gene expression, to correctly reproduce spiking heterogeneities seen in vitro (24, 25). However, whether such an approach can successfully explain such relationships in vivo, where neurons are in a high-conductance state due to massive synaptic bombardment (26) remains unknown to date.…”

Section: Introductionmentioning

confidence: 99%

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Decoding the relative contributions of extrinsic and intrinsic mechanisms in mediating heterogeneous spiking activities of sensory neurons in vivo using computational modeling

Akhshi

Haggard

Marquez

et al. 2023

Preprint

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Neurons ubiquitously display heterogeneities in spiking activity even within a given cell type. To date, the relative contributions of extrinsic mechanisms (e.g., synaptic bombardment) and intrinsic mechanisms (e.g., conductances, cell morphology) towards determining spiking activity remain poorly understood. Here we address this important question using a novel approach that combines biophysical techniques, in which extracellular in vivo recordings of electrosensory pyramidal cells within weakly electric fish, are combined with computational modeling. Specifically, by varying parameters, a conductance-based computational model successfully reproduced the highly heterogeneous spiking activities seen experimentally. Model parameters that varied the most were then used to gauge the relative contributions of extrinsic vs. intrinsic mechanisms. Overall, extrinsic synaptic input was predicted to be the main factor accounting for spiking heterogeneities. We tested this prediction experimentally by performing two different manipulations: i) pharmacologically inactivating feedback; ii) applying the neuromodulator serotonin. Our model predicted that feedback inactivation should reduce while serotonin application should increase spiking heterogeneities. Experiments corroborated these predictions. Importantly, for serotonin application, increased heterogeneity occurred despite a strong reduction in intrinsic membrane conductance, further demonstrating that extrinsic synaptic input is the primary determinant of spiking heterogeneities in vivo. Taken together, our results demonstrate that devising a computational model to capture spiking heterogeneities in vivo and assessing which parameters are responsible can successfully determine the relative contributions of extrinsic vs. intrinsic inputs. We expect this approach to be generalizable to other systems and species.

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“…The most successful form of reductive modeling in neuroscience has typically targeted deductive, data-driven prediction and validation of experimental observations of single-neuron behavior. This biophysical approach is undergirded by the Hodgkin-Huxley formalism [61][62][63] which quantifies a sufficient subset of lower level, physical/chemical processes-e.g., ionic gradients, reversal potentials, intrinsic conductances, (in)activation nonlinearities, etc.-that remain analytically coherent at the subneuronal level of excitable membranes and cellular compartments. In contrast, modeling supraneuronal group behavior has relied on large-scale simulations of simple interconnected units, efficiently implemented as 1-or 2-dimensional point-neuron models (cf.…”

Section: Framing An Integrative Neuroscience Of Intelligencementioning

confidence: 99%

Neurodynamical Computing at the Information Boundaries of Intelligent Systems

Monaco

Hwang

2022

Cogn Comput

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Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies—properly conceived as reentrant dynamical flows and not merely as identified groups of neurons—may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.

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Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types

Cited by 26 publications

References 73 publications

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

Decoding the relative contributions of extrinsic and intrinsic mechanisms in mediating heterogeneous spiking activities of sensory neurons in vivo using computational modeling

Neurodynamical Computing at the Information Boundaries of Intelligent Systems

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