Connectomic data

…modeling of cortex based on connectomic data for monkeys and mice, and highlight macroscopic gradients of synaptic excitation and inhibition as a general principle of

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

Recurrent neural networks (RNNs) trained on working memory (WM) tasks have been the subject of extensive study. These networks typically contain one or more aspects of biological-implausibility, such as the adoption of saturating output functions (tanh), violation of Dale's Law or introduction of artificial elements (LSTM). They also have over-simplistic input pathways such as binary cues, which are insufficient to represent the rich dynamics found in sensory brain areas. In particular, the role of inter-areal communication between sensory and association areas for WM has been critically under

Animals are presented with noisy and incomplete information from which we learn how to react in novel situations. For example, a given training set could have many explanations, each implying a different behavioural response; acting appropriately requires inferring the most plausible explanation. The way animals choose to wield Occam's razor, selecting the most parsimonious explanation, is called their inductive bias, and it is implicitly built into the operation of animals' neural circuits. This relationship between an observed sensory circuit and its inductive bias is a useful explanatory wi

…Detailed connectomic data, e.g., the Hemibrain dataset, reveal a massive number of nested feedback loops among these three classes of neurons. Dissecting the role of these feedback circuits is key to the understanding the computation taking place in AL and beyond. How

Many flexible behaviors are thought to rely on internal representations of an animal’s spatial relationship to its environment and of the consequences of its actions in that environment. While such representations—e.g. of head direction and value—have been extensively studied, how they are combined to guide behavior is not well understood. I will discuss how we are exploring these questions using a classical visual learning paradigm for the fly. I’ll begin by describing a simple policy that, when tethered to an internal representation of heading, captures structured behavioral variability in t