Neural Circuit Computation and Behavior
How do animals integrate information and make decisions?
When animals move through the environment, they constantly need to make decisions about what to do next. They integrate information from their senses, retain a working memory of such cues, and evaluate signals according to their internal state. Somehow, the nervous system effortlessly implements these computations and eventually controls behavior.
We use the larval zebrafish as a model organism to investigate the neural circuit mechanisms underlying sensory integration and decision-making. Larval zebrafish are tiny and almost perfectly transparent vertebrates. This allows us to use two-photon microscopy to observe brain activity at cellular resolution in behaving animals and to directly link circuit dynamics to individual swimming decisions.
Individual animal behavior: We think that describing animal behavior on a detailed algorithmic level is an essential step towards understanding brain function. To this end, we perform open-loop as well as closed-loop behavioral experiments in freely swimming and head-fixed larvae.
In our stimulus design, we draw inspiration from classical psychophysics work developed for primates and humans. For example, by translating a popular visual stimulus, the random dot motion stimulus, to fish we discovered that larvae can integrate motion in the environment over multiple seconds and use such cues to decide whether they should swim to the right or to the left.
With such simple behavioral experiments, we ask, for example, how long fish remember, whether swimming decisions reset their motion memory, or whether they can also integrate other sensory modalities.
Collective behavior: When in a group, the decision-making of the individual guides what the collective is doing.
Through precise tracking experiments of groups of fish, we explore to what extend such complex social behaviors can readily emerge from the simple sensory-motor primitives that we have explored in individual animals.
Functional imaging: We use two-photon functional imaging in head-fixed behaving animals to characterize the activity of thousands of cells across large parts of the brain. Such experiments help us to identify the key processing centers involved and characterize cell-specific trial-to-trial response dynamics. Having such a perspective on the level of an entire brain helps to deduce how information flows from the animal's senses to its motor output.
Circuit dissections: The observed circuit dynamics arise from an intricate interplay between thousands of cells with each type having its own biophysical properties. To better understand this, we perform cell-specific ablations and optogenetic interventions. We also use a wide array of molecular genetic tools to study anatomy, neurotransmitter identity, synaptic connectivity as well as the expression profile of functionally identified cells.
Modeling: Computational modeling is an important component in our projects: First, it helps us to interpret and design behavioral experiments. Second, it allows us to better understand how complex group dynamics may arise from the decision-making of the individual. Third, it provides us with a powerful tool to make precise and testable predictions about possible cell anatomies and connectivities, neuronal dynamics, as well as behavior in our circuit dissection experiments.
We are always seeking enthusiastic students on all levels to join our group. If you like interdisciplinary work and if you like to tackle challenging projects in Systems Neuroscience, shoot us an email and come visit!
1. Vohra SK., Harth P., Fotowat H., Bahl A., Isoe Y., Engert F., Baum D, and Hege HC. (2022). A visual interface for exploring hypotheses about neural circuits. EuroVis 2022 (submitted).
2. Harpaz R., Nguyen M.N., Bahl A., Engert F. (2021). Precise visuomotor transformations underlying collective behavior in larval zebrafish. Nat. Commun. 12 (1), 1–14.
3. Harpaz R., Aspiras A.C., Chambule S., Tseng S., Engert F., Fishman M.C.*, Bahl A.* (2021). Collective behavior emerges from genetically controlled simple behavioral motifs in zebrafish. Sci. Adv. 7 (41), 1–13.
4. Zhu M.L., Herrera K.J., Vogt K., Bahl A.* (2021). Navigational strategies underlying temporal phototaxis in Drosophila larvae. J. Exp. Biol. 224 (11), 1–7.
5. Chen A.B., Deb D., Bahl A., Engert F. (2021). Algorithms underlying flexible phototaxis in larval zebrafish. J. Exp. Biol. 224 (10), 1–11.
6. Bahl A.*, Engert F. (2020). Neural circuits for evidence accumulation and decision-making in larval zebrafish. Nat. Neurosci. 23 (1), 94–102.
7. Wee C., Song E., Johnson R., Ailani D., Randlett O., Kim J., Nikitchenko M., Bahl A., Yang C., Ahrens M., Kawakami K., Engert F., Kunes S. (2019). A bidirectional network for appetite control in larval zebrafish. eLife 8 (e43775), 1–37.
8. Ribeiro I., Drews M., Bahl A., Machacek C. Borst A., Dickson B.J. (2018). Visual projection neurons mediating directed courtship in Drosophila. Cell 174 (3), 607-621.
9. Leonhardt A., Ammer G., Meier M., Serbe E., Bahl A., Borst A. (2016). Asymmetry of Drosophila ON and OFF motion detectors enhances real-world velocity estimation. Nat. Neurosci. 19 (5), 706–715.
10. Bahl A.*, Serbe E., Meier M., Ammer G., Borst A. (2015). Neural mechanisms for Drosophila contrast vision. Neuron 88 (6), 1240–1252.
11. Ammer G., Leonhardt A., Bahl A., Dickson B.J., Borst A. (2015). Functional specialization of neural input elements to the Drosophila ON motion detector. Curr. Biol. 25 (17), 2247–2253.
12. Maisak M.S., Haag J., Ammer G., Serbe E., Meier M., Leonhardt A., Schilling T., Bahl A., Rubin G.M., Nern A., Dickson B., Reiff D.F., Hopp E., Borst A. (2013). A directional tuning map of Drosophila elementary motion detectors. Nature 500 (7461), 212–216.
13. Bahl A.*, Ammer G., Schilling T., Borst A.* (2013). Object tracking in motion-blind flies. Nat. Neurosci. 16 (6), 730–738.
14. Bahl A.*, Stemmler M.B., Herz A.V.M., Roth A. (2012). Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. J. Neurosci. Methods 210 (1), 22–34.
15. Plett J., Bahl A., Buss M., Kühnlenz K., Borst A. (2012). Bio-inspired visual ego-rotation sensor for MAVs. Biol. Cybern. 106 (1), 51–63.
16. Roth A.*, Bahl A.* (2009). Divide et impera: optimizing compartmental models of neurons step by step. J. Physiol. 587 (7), 1369–1370.