Neural Computation

Our Research

We are interested in understanding computations in neural circuits of the mammalian brain. To attack this problem, we work at the interface between cellular and systems neuroscience. We aim to understand the cellular toolkit that enables single neurons to perform computations, and in turn how single neurons and their patterns of connections contribute to the computations performed by neural circuits.

Neural Computation Lab

• Prof. Beverley Clark
• Honorary Prof. Michael Häusser
• Dr Arnd Roth

Our lab has a special focus on neuronal dendrites, which actively transform synaptic inputs into specific neuronal output patterns. We use the cerebellum and neocortex as model systems, combining in vitro and in vivo imaging and electrophysiology approaches, and taking advantage of a range of high-tech approaches. These include two-photon microscopy, optogenetics, patch-clamp recordings from dendrites, recordings using Neuropixels probes, and most recently the development of ‘all-optical’ approaches for simultaneous readout and manipulation of neurons by combining two-photon imaging and two-photon optogenetics.

Our experiments are complemented by computational models of single neurons and networks of neurons. At each stage of our work, our aim is to link different levels of brain function in order to reveal how activity in single neurons and neural circuits drives behaviour and, importantly, what kinds of changes take place within these circuits during learning.

Dendritic computation

What can dendrites compute? How do they do it? And how are these computations used for behaviour? We are attacking these questions using a combination of experimental and modelling approaches. Two-photon glutamate uncaging experiments and patch-clamp recordings in vitro are being used to define the biophysical toolkit that enables dendrites to perform elementary computations. Imaging and recording from dendrites in vivo allows us to determine how these computations are harnessed in behaving animals. Finally, theoretical models of dendritic function are being used to provide a quantitative description of dendritic computation, as well as experimentally testable predictions.

Cerebellar computation

The circuitry of the cerebellar cortex is both remarkably simple and highly organized, providing a unique opportunity to understand the relationship between the structure and function of a neural circuit in the mammalian brain.. We are taking advantage of the accessibility, genetic tractability and rigorous architecture of the cerebellar cortex to test longstanding theories of how the elements of cerebellar computation are mapped onto its structure.

Cortical computation

What is the cortical code? Answering this question will allow us to understand not only how the cortex processes and stores information, but also how these processes are altered during development and disease. We now have an unprecedented opportunity to crack the neural code used by the cortex with the advent of new tools for recording and manipulating the activity of the genetically defined population of neurons in the cortex. These tools are being applied to the barrel cortex and visual cortex to identify the principles governing sensory processing in head-fixed mice performing behavioural tasks.

Our people

Publications

1. Carolan, J. et al. (2025) All-optical voltage interrogation for probing synaptic plasticity in vivo. Nature Communications
2. Findling, C., Hubert, F., International Brain Laboratory. et al. (2025) Brain-wide representations of prior information in mouse decision-making. Nature 645, 192–200
3. International Brain Laboratory., Angelaki, D., Benson, B. et al.(2025) A brain-wide map of neural activity during complex behaviour. Nature 645, 177–191
4. Simon A et al (2025). Calcium regulation of muscle spindle mechanosensory afferent function. Experimental Physiology.
5. Triplett MA et al (2025). Fast photostimulus optimization for holographic control of neural ensemble activity in vivo. bioRxiv.
6. Beau M, et al (2025). A deep learning strategy to identify cell types across species from high-density extracellular recordings. Cell. Apr 17;188(8):2218-2234. e22.
7. Lakunina A, et al (2025). Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics. bioRxiv.
8. Crossley M, et al (2025). Functional mapping of the molluscan brain guided by synchrotron X-ray tomography. PNAS.
9. Cornford J, et al (2024). Brain-like learning with exponentiated gradients. bioRxiv.
10. Gauld O, et al (2024). A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception. Neuron.
11. Russell LE et al (2024). All-optical interrogation of neural circuits in awake behaving mice. Nature Communications.
12. International Brain Laboratory et al. (2023). A Brain-Wide Map of Neural Activity during Complex Behaviour. bioRxiv.
13. Findling C, et al (2023). Brain-wide representations of prior information in mouse decision-making. bioRxiv.
14. Fişek M et al (2023). Cortico-cortical feedback engages active dendrites in visual cortex. Nature.
15. Ye Z et al (2023). Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings. bioRxiv.
16. Russell L et al (2022). All-optical interrogation of neural circuits in awake behaving mice. Nature Protocols.
17. Kostadinov D & Häusser M (2022). Reward signals in the cerebellum: origins, targets, and functional implications. Neuron.
18. Bicknell B and Häusser M (2021). A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron.
19. International Brain Laboratory et al. (2021). Standardized and reproducible measurement of decision-making in mice. eLife.
20. Häusser M (2021) Optogenetics: the might of light. New England Journal of Medicine.
21. Simon A et al (2021). Ultrastructural readout of in vivo synaptic activity for functional connectomics. bioRxiv.
22. Russell LE,   (2021). All-optical interrogation of neural circuits in behaving mice. bioRxiv.
23. Goetz L, et al (2021). Active dendrites enable strong but sparse inputs to determine orientation selectivity. PNAS.
24. Steinmetz et al. (2021). Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science.
25. Sezener E et al (2021). A rapid and efficient learning rule for biological neural circuits. bioRxiv.