State and stimulus dependence reconcile motion computation and the Drosophila connectome

Abstract

Sensory systems dynamically optimize their processing properties in order to process a wide range of environmental and behavioral conditions. However, attempts to infer the function of these systems via modeling often treat system components as having static processing properties. This is particularly evident in the Drosophila motion detection circuit, where the core algorithm for motion detection is still debated, and where inputs to motion detecting neurons remain underdescribed. Using whole-cell patch clamp electrophysiology, we measured the state- and stimulus-dependent filtering properties of inputs to the OFF motion-detecting T5 cell in Drosophila. Simply summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that changes in the shape of input temporal filters are sufficient to explain conflicting theories of T5 function. Therefore, with our measurements and our model, we reconcile motion computation with the anatomy of the circuit.

Non-preferred contrast responses in the Drosophila motion pathways reveal a receptive field structure that explains a common visual illusion

“…9 It appears that T4 and T5 responses may mask additional complexity not yet uncovered or their upstream inputs could interact to produce simpler downstream effects. Although assigning model conductances to individual cell types may be too simplistic, it is important to combine our modeling approach with the powerful circuit-constrained models pioneered by other groups 29,[44][45][46][47] and attempt to reconcile the above discrepancies. One such future step would be to replace our generic excitatory and inhibitory inputs with specific conductances based on the known characteristics of the upstream neurons that provide inputs to T4 and T5.…”

Identifying inputs to visual projection neurons in Drosophila lobula by analyzing connectomic data

“…In addition to the relative numerical simplicity of the fly brain (Raji and Potter, 2021) and its sophisticated genetic tools to monitor and manipulate specific cell types (Jenett et al, 2012; Kazama, 2015; Kitamoto, 2001; Pfeiffer et al, 2010; Simpson and Looger, 2018; Tirian and Dickson, 2017), a major contributor to this rapid progress has been electron microscopy (EM) based connectomes (Meinertzhagen, 2018, 2016). For example, dense EM reconstruction of circuitry surrounding the elementary motion detecting neurons in the Drosophila brain, T4 and T5 (Shinomiya et al, 2019, 2014; Takemura et al, 2015, 2017, 2013), have guided functional studies by providing strong constraints on neural computations in T4 and T5 (Agrochao et al, 2020; Behnia et al, 2014; Borst, 2018; Kohn et al, 2021; Ramos-Traslosheros and Silies, 2021; Strother et al, 2017; Zavatone-Veth et al, 2020), as well as by discovering previously undocumented circuit elements (Ammer et al, 2015; Behnia et al, 2014; Meier and Borst, 2019; Serbe et al, 2016; Strother et al, 2017). Similarly, EM reconstruction of early visual neuropils led to the discovery of a novel pathway for color vision (Takemura et al, 2008), which was later functionally confirmed to be contributing to the spectrally-sensitive behaviors (Gao et al, 2008).…”