Topic: Fruit fly visual system

Modern experimental techniques now reveal the structure and function of neural circuits at unprecedented scale and resolution. How can we use this wealth of data to understand how cells and circuits implement computations underlying behaviour? Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to bu

Experimental techniques now make it possible to measure the structure and function of neural circuits at an unprecedented scale and resolution. How can we leverage this wealth of data to understand how neural circuits perform computations underlying behaviour? A mechanistic understanding will require models that align with experimental measurements and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Building such models has remained a central challenge. I will present our work on addressing this challenge. We have developed machine learning me

Research efforts in mapping the connectivity of neural circuits has led to a wealth of connectomic data, including the full nervous system of the fruit fly. The dynamical characteristics of the neurons in these circuits, however, are still largely unknown. Recent work has demonstrated that connectome-constrained models with ethological task constraints generate accurate predictions of single-neuron response properties. Specifically, models with connectivity derived from the fruit fly visual system were trained using deep learning to perform a motion vision task. Resulting models accurately pre