Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning

Vaxenburg, Roman; Siwanowicz, Igor; Merel, Josh; Robie, Alice A.; Morrow, Carmen; Novati, Guido; Stefanidi, Zinovia; Both, Gert-Jan; Card, Gwyneth M.; Reiser, Michael B.; Botvinick, Matthew M.; Branson, Kristin M.; Tassa, Yuval; Turaga, Srinivas C.

doi:10.1101/2024.03.11.584515

Cited by 10 publications

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Connectome-constrained networks predict neural activity across the fly visual system

Lappalainen,

Tschopp,

Prakhya

et al. 2024

Nature

View full text Add to dashboard Cite

We can now measure the connectivity of every neuron in a neural circuit1–9, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question10. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe1–5 but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning11, to allow the model network to detect visual motion12. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.

show abstract

Connectome-constrained networks predict neural activity across the fly visual system

Lappalainen,

Tschopp,

Prakhya

et al. 2024

Nature

View full text Add to dashboard Cite

show abstract

The fly connectome reveals a path to the effectome

Pospisil,

Aragon,

Dorkenwald

et al. 2024

Nature

View full text Add to dashboard Cite

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1–3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

show abstract