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Nathan Gouwens

Assistant Investigator

teams /
Allenite

Nathan Gouwens joined the Allen Institute in 2014 as a scientist in the Modeling, Analysis, and Theory group. Now as an Assistant Investigator, he is currently involved with developing biophysically detailed simulations of individual neurons in the mouse visual cortex. These simulations are based on combined electrophysiological and morphological data with the goal of determining how biophysical mechanisms shape the processing of visual information in cortex. Before joining the Allen Institute, Nathan was a graduate student in the lab of Rachel Wilson at Harvard Medical School. His graduate research involved analyzing how electrical signals propagated in the early olfactory circuit in Drosophila through a combination of electrophysiological experiments and computational models. Nathan received his PhD in Neurobiology from Harvard University, an MPhil in Biological Sciences from the University of Cambridge, and a BA in Integrated Science and Biological Sciences at Northwestern University.

research focus

Nathan Gouwens is studying how biophysical mechanisms influence the processing of visual information by cortical neurons through the development of detailed simulations. These biophysical models are based on electrophysiological measurements of intrinsic properties as well as detailed morphologies, which can also serve as building blocks for larger-scale network simulations. The aims are to integrate diverse data to reproduce experimentally-observed features of neuronal activity and to gain insights into how different biophysical mechanisms interplay to shape the role a neuron plays in visual circuits.

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featured publications

publication / 2025
Integrating multimodal data to understand cortical circuit architecture and function
Nature Neuroscience
publication / 2023
A guide to the BRAIN Initiative Cell Census Network data ecosystem
PLoS biology
publication / 2021
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Cell
publication / 2021
Consistent cross-modal identification of cortical neurons with coupled autoencoders
Nature Computational Science
publication / 2020
Common cell type nomenclature for the mammalian brain
eLife
publication / 2019
Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas
PloS One
publication / 2018
Generalized leaky integrate-and-fire models classify multiple neuron types
Nature Communications
publication
Connectomics of predicted Sst transcriptomic types in mouse visual cortex
Nature
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