Figure 1: Theory-driven approach for neural circuit characterisation, followed by experimental validation. Neuroscience research faces a need to link big data on brain anatomy and physiology, as high-throughput measurements of these become increasingly feasible. Neural circuit models (A; e.g. of the retina) can provide predictions of such links (B; predicted bipolar cell receptive (top) and projective (bottom) fields from measured ganglion cell responses), which can be subsequently tested by experiments for validation (C; measured bipolar cell receptive and projective fields). Adapted from Real et al., Curr Biol 2017.
The Asari group combines experimental and computational approaches to study the principles and the function of neuronal circuits, specifically in the early visual system of mice.
Previous and current research
How do neurons interact with each other to perform specific neurological functions? To address this question, we have been taking a theory-driven approach to better understand the circuit mechanisms underlying diverse computations in the retina (see figure). We developed a biologically faithful but computationally tractable model of the retina, from which predictions were derived on the circuit structure and function, and subsequently tested for experimental validation (Real et al., Curr Biol 2017; Vlasiuk and Asari, bioRxiv 2020). We will further pursue this theory-driven approach to reveal how signal flows within retinal circuits process specific aspects of visual information.
Future projects and goals
As we have learned more about local circuit functions of individual brain areas, it becomes increasingly important to understand: (a) how these areas interact with each other to modulate their local circuit functions, and (b) how such interactions help process sensory and motor signals to organise an animal’s behaviour. An excellent model system to address these questions is the mouse retina. First, the retina is one of the best-understood circuits in the brain, and the physiological functions are known in detail from the molecular to the cellular circuit level. Second, various tools are available in mice to label, monitor, and manipulate specific cell types and circuits. Third, although the retina is often thought to make only feed-forward connections to the brain, there is an anatomical substrate of efferent inputs from multiple brain areas to the retina across species.
We will thus focus on the bidirectional interactions between the retina and the brain under different behavioural and internal states of an animal, and analyse the functional role of the retinal efferents and their impact on the retinal afferents from the viewpoint of visual computation (see figure). The results will clarify how visual processing in the retina is dynamically modulated by efferent inputs under different behavioural conditions, and how a diverse set of retinal outputs serves as a basis for visual computation along the afferent visual pathways. This will help explain what each stage of visual processing is for, and also help refine an input–output model to better describe visual responses at each stage of the visual system. Ultimately, the outcome of our research will support the future development of visual prosthetic devices by faithfully emulating the function of the early visual system; in particular, the retina.