Neuronal gain modulability is determined by dendritic morphology: A computational optogenetic study
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Abstract
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron's input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron's gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells-confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain modulation.
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Morphological features of dendrites regulate the extent to which gain control of individual neurons is possible, with moderate branching in pyramidal neurons potentially promoting the greatest possible range of gain modulation.
“In addition, morphological features of the dendrites regulate the extent to which gain control of individual neurons is possible, with moderate branching in pyramidal neurons potentially promoting the greatest possible range of gain modulation96.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [96]
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Cites
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Invariant neural subspaces maintained by feedback modulation
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Tang, Y., An, L., Yuan, Y., Pei, Q., Wang, Q., & Liu, J. K. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008670 · Cited by 16 · see citing works
Islah, N., Etter, G., Tugsbayar, M., Gurbuz, B. T., Richards, B. A., & Muller, E. B. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf134 · Cited by 12 · see citing works
“This idea is supported by the unique morphological and physiological properties of pyramidal neurons, as activity in apical dendrites in vivo is generally not sufficient to drive somatic output responses alone ( Stuart and Spruston, 1998 ; Larkum et al., 1999 ; Larkum, 2013 ; Jarvis et al., 2018 ).”
Biological substrates of structure-function coupling in brain networks
Fotiadis, P., Arnsten, A. F. T., Parkes, L., Satterthwaite, T. D., Shinohara, R. T., & Bassett, D. S. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106581 · Cited by 5 · see citing works
Optogenetic Stimulation Recruits Cortical Neurons in a Morphology-Dependent Manner
Berling, D., Baroni, L., Chaffiol, A., Gauvain, G., Picaud, S. A., & Antolík, J. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1215-24.2024 · Cited by 4 · see citing works
Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience
Busch, S. E., & Khakhalin, A. S. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00099.2019 · Cited by 4 · see citing works
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
Liu, P., Bo, K., Ding, M., & Fang, R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011943 · Cited by 4 · see citing works
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Applying Graph Theory and Mathematical-Computational Modelling to Study a Neurophysiological Circuit
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“When it comes to the nervous system, mathematical and computational modelling enables data integration from observations and experiments into a theoretical framework, facilitating the exploitation of the functional role of nervous structures, and neuronal circuits [6] [7] [8] [9].”
BackgroundZhang, S., Xiang, P., Suo, M., Yi, Z., Wei, Z., Li, J., Zeng, Y., & Chen, Y. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111710 · Cited by 1 · see citing works
The Retina as a Dynamical System
Cessac, B. (2021) · World scientific series on nonlinear science, series B · doi:10.1142/9789811221903_0014 · Cited by 1 · see citing works
“…by a non linear response to its voltage variation, given by : where : Gain control plays an important rule in regulating neurons activity, as it adjusts the relationship between dramatically changing inputs and the limited range of amplitudes in the neuron’s response of neurons [65][66, 67].”
BackgroundEstudo computacional da integração de sinais de cones e bastonetes em células bipolares retinianas
Leopoldo, K. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.47.2021.tde-23062021-185040 · Cited by 1 · see citing works
Emergence Of Emotion Selectivity in A Deep Neural Network Trained to Recognize Visual ObjectsPreprint
Liu, P., Ding, M., & Fang, R. (2023) · doi:10.31219/osf.io/j5fnm · Cited by 1 · see citing works
Computational neuroscience meets optogenetics: Unlocking the brain’s secrets
Schultz, S. R., & Nikolić, K. (2019) · Research Features · doi:10.26904/rf-125-8891
Self-citationInvariant neural subspaces maintained by feedback modulationPreprint
Naumann, L., Keijser, J., & Sprekeler, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.29.466453
“Dendritic input can change the gain of the neural transfer function on fast timescales (Larkum et al., 2004; Jarvis et al., 2018).”
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“It was shown in silico that neuronal gain modulation can be influenced by dendritic morphology (87), and that neuronal firing properties can be influenced by dendrite geometry (88).”
Morphometric Interpretation of Postganglionic Sympathetic Neurons that Innervate Myocardium
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- Authors
- Jarvis, S., Nikolic, K., & Schultz, S. R.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Jarvis, Sarah
- Schultz, Simon R.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-03-09
- Volume
- 14
- Issue
- 3
- Pages
- e1006027
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5862493
- OpenAlex
- W2950200460
- Cited by
- 28
- References
- 55
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 1 · 2021: 6 · 2020: 5 · 2019: 5 · 2018: 1
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-03-21)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UWL Repository (University of West London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- H2020 Marie Skłodowska-Curie Actions (628086)
- Biotechnology and Biological Sciences Research Council (BB/L018268/1, BB/K001817/1)
- Engineering and Physical Sciences Research Council (EP/N002474/1)
- European Research Council (319818)
- Wellcome Trust (ISSF)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Photoreceptor and optogenetics research
- Keywords
- dendritic morphology, dendritic branching, input-output function, optogenetics, excitatory and inhibitory inputs, layer 5 pyramidal neurons, stellate cells, dendritic targeting, computational neuron model
- MeSH terms
- Computer Simulation, Dendrites, Kinetics, Models, Neurological, Neurons, Synapses, Pyramidal Cells, Computational Biology, Opsins, Optogenetics
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