Visual information processing and the natural input
We are interested how individual and small groups of neurons arranged into microcircuits break sensory patterns into parallel, highly specific representations of the outside world. We use a combination of 2-photon imaging of genetically encoded biosensors, electrophysiology, patterned light stimulation and computational modelling to probe the visual processing of individual and networks of neurons in the retina and brain of zebrafish and mice. We also study the distribution of visual features in the natural visual world to better understand how different visual systems have evolved to optimally process their own natural input.
The retina breaks light patterns into increasingly specific internal representations of the visual world. Spike trains of retinal gamglion cells (RGCs), the retina’s only output neurons, retain only a tiny fraction of original visual input to form a sparse but information rich retinal code. Within the retina, interactions between five principal neuron classes, each comprised of several types, fundamentally shape the response properties of individual pathway elements. We study the gradual evolution of feature extraction as visual information trickles through the retinal network.
Vertebrate colour vision
With the rise of the dinosaurs, our own ancient ancestral proto-mammals escaped to the forests and adopted a nocturnal lifestyle. In this process, they lost all but two of their cone-photoreceptor types, the basis of colour vision. As a result, most of today’s mammals are dichromats, or “atypical trichromats” such as our own species. Not so the zebrafish, or indeed almost any other vertebrate: Most birds, fish, reptiles, amphibians and even jawless fish still “see in 4 or even 5 colours”, based on the 4-5 cone types that were around long before the dinosaurs took over the planet.
The computational power of retinal networks is deeply rooted in their synaptic architecture. The highly specialised ribbon-type synapses of photoreceptors and bipolar cells receive a myriad of feedback and feed-forward signals through the network, making them probably the most important computational locus of the circuit. Using 2-photon imaging of voltage, calcium and synaptic release signals in vivo we aim to probe the visual response properties of retinal synapses towards a better understanding of locally visual signal transformations.
The natural visual world of animals
All sensory systems are specialised to best serve an animal’s sensory-ecological niche, and vision is no exception. Here, specialisations range from basic anatomy all the way down to the tuning individual synapses and microcircuits. To understand what natural sensory demands and pressures shape requires measurements of natural visual worlds, including systematic variations in space, time and wavelength composition. We use custom camera and hyperspectral scan-systems to survey visual information available to animals in nature.
Evolution of vision
The retinas of all vertebrates are based on a common blueprint but different species evolved a vast range of retinal particularities that help them navigate their visuo-ecological niche. How are these differences reflected in retinal networks? The answer towards this, and many other questions relating to animal lifestyle and evolutionary history must come from comparisons across species, including non-model species. We therefore complement our core work on zebrafish vision with frequent excursions into the sensory complement of other animals.
To probe neuronal function in the living nervous system requires state of the art imaging equipment such as our heavily customised 2-photon microscopes and visual stimulators. Here, we aim to drive current possibilities in optical imaging technology, for example by light-path optimisations or the implementation of custom scan strategies that acknowledge the 3D curvature of biological samples. In parallel, we invest heavily in open hardware approaches to optimise research-grade lab equipment for a fraction of commerical costs.
How are networks consisting of anatomically and functionally distinct neuronal elements adapted to sample, filter and forward information contained in a sensory stimulus or in central neuronal ensemble activity towards their specific computational goals? Faced with the staggering complexity of even the simplest neuronal circuits, addressing such questions continues to present a daunting task. One very promising approach will lie in promoting our understanding of computations performed by specific, very well described neuronal ensembles with a clear function, input-output organisation and experimental accessibility, such as the vertebrate retina.
The retina is a beautiful neural circuit, and presents a powerful model system to study many general aspects of neuronal network computation, including sampling optimization, filtering as well as compression of the incoming stream of visual information into highly selective parallel streams towards transmission to the brain. First, the retina operates almost exclusively as a feed-forward network, receiving little or no fast feedback from the brain. Accordingly, great control can be maintained over network input (the visual stimulus) while retinal output is readily recorded in single unit or population measurements of retinal ganglion cells (RGCs), the retina’s output neurons. Second, the anatomical basis of the retina of different species ranks amongst the most well-described ‘complete’ networks, with ready availability of specific transgenic lines and molecular markers. Third, individual retinal microcircuits tile the retinal surface, such that the same computations are effectively performed at every retinal position – this specific organizational motif greatly reduces the parameter space that needs to be explored when aiming to complement our understanding of retinal networks using computational models. Indeed, it is often possible to derive comparatively simple models of computations performed by individual microcircuits that capture the vast majority of the variance observed even when presenting complex visual stimuli. Taken together, the vertebrate retina may be one of the very few model systems in neuroscience today that promises to hold a thorough conceptual functional understanding within arm’s reach. Detailed comparison between processing strategies employed between the retinas of different species, in the context of each system’s natural input, then may yield more general insights into how neuronal networks can evolve to best serve an animal’s needs.