Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
- University of Oslo, Norway
- Allen Institute for Brain Science, United States
- Allen Institute, United States
- Norwegian University of Life Sciences, Norway
Abstract
Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: Firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.
Data availability
The files necessary to run simulations of the different model versions presented in the paper as well as data resulting from simulations of those model versions are publicly available in Dryad: https://doi.org/10.5061/dryad.k3j9kd5b8The experimental data set utilized is publicly available at: https://portal.brain-map.org/explore/circuits/visual-coding-neuropixelsThe code generated for data analysis and producing the figures in this manuscript is publicly available at: https://github.com/atleer/CINPLA_Allen_V1_analysis.git.
-
Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortexDryad Digital Repository, doi:10.5061/dryad.k3j9kd5b8.
-
20191003_AIBS_mouse_ecephys_brain_observatory_1_1DANDI Archive 000021.
Article and author information
Author details
Alexander J Stasik
Funding
Simula School of Research
- Atle E Rimehaug
European Union Horizon 2020 Research and Innovation program (785907)
- Espen Hagen
European Union Horizon 2020 Research and Innovation program (945539)
- Espen Hagen
Research Council of Norway (COBRA - project number 250128)
- Alexander J Stasik
IKTPLUSS-IKT and Digital Innovation (300504)
- Alexander J Stasik
National Institute of Neurological Disorders and Stroke (R01NS122742)
- Yazan N Billeh
- Josh H Siegle
- Kael Dai
- Shawn R Olsen
- Christof Koch
- Anton Arkhipov
National Institute of Biomedical Imaging and Bioengineering (R01EB029813)
- Yazan N Billeh
- Josh H Siegle
- Kael Dai
- Shawn R Olsen
- Christof Koch
- Anton Arkhipov
Allen Institute
- Yazan N Billeh
- Josh H Siegle
- Kael Dai
- Shawn R Olsen
- Christof Koch
- Anton Arkhipov
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Rimehaug et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,433
- views
-
- 307
- downloads
-
- 25
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
eLife 12:e87169.
https://doi.org/10.7554/eLife.87169
Further reading
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.
-
- Computational and Systems Biology
- Neuroscience
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.
