Biology subjects

Sainsbury, T. T. J.

Publications and source records attributed to Sainsbury, T. T. J..

2 recordsLinked to original sources

bioRxiv2021

Topographically localised modulation of tectal cell spatial tuning by natural scenes

Visual neurons can have their tuning properties contextually modulated by the presence of visual stimuli in the area surrounding their receptive field, especially when that stimuli contains natural features. However, stimuli presented in specific egocentric locations may have greater behavioural relevance, raising the possibility that the extent of contextual modulation may vary with position in visual space. To explore this possibility we utilised the small size and optical transparency of the larval zebrafish to describe the form and spatial arrangement of contextually modulated cells throughout an entire tectal hemisphere. We found that the spatial tuning of tectal neurons to a prey-like stimulus sharpens when the stimulus is presented in the context of a naturalistic visual scene. These neurons are confined to a spatially restricted region of the tectum and have receptive fields centred within a region of visual space in which the presence of prey preferentially triggers hunting behaviour. Our results demonstrate that circuits that support behaviourally relevant modulation of tectal neurons are not uniformly distributed. These findings add to the growing body of evidence that the tectum shows regional adaptations for behaviour.

neuroscience↗

bioRxiv2018

Bayesian inference of neuronal ensembles

In many areas of the brain, both spontaneous and stimulus-evoked activity can manifest as synchronous activation of neuronal ensembles. The characterization of ensemble structure and dynamics provides important insights into how brain computations are distributed across neural networks. The proliferation of experimental techniques for recording the activity of neuronal ensembles calls for a comprehensive statistical method to describe, analyze and characterize these high dimensional datasets. Here we introduce a generative model of synchronous activity to describe spontaneously active neural ensembles. Unlike existing methods, our analysis provides a simultaneous estimation of ensemble composition, dynamics and statistical features of these neural populations, including ensemble noise and activity rate. We also introduce ensemble \"coherence\" as a measure of within-ensemble synchrony. We have used our method to characterize population activity throughout the tectum of larval zebrafish, allowing us to make statistical inference on the spatiotemporal organization of tectal ensembles, their composition and the logic of their interactions. We have also applied our method to functional imaging and neuropixels recordings from the mouse, allowing us to relate the activity of identified ensembles to specific behaviours such as running or changes in pupil diameter.

neuroscience↗