Seminars

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

The science of the retina has reached an interesting stage of completion. There exists now a consensus standard model of this neural system - at least in the minds of many researchers - that serves as a baseline against which to evaluate new claims. The standard model links phenomena from molecular biophysics, cell biology, neuroanatomy, synaptic physiology, circuit function, and visual psychophysics. It is further supported by a normative theory explaining what the purpose is of processing visual information this way. Most new reports of retinal phenomena fit squarely within the standard model, and major revisions seem increasingly unlikely. Given that our understanding of other brain circuits with comparable complexity is much more rudimentary, it is worth considering an example of what success looks like. In this talk I will summarize what I think are the ingredients that led to this mature understanding of the retina. Equally important, a number of practices and concepts that are currently en vogue in neuroscience were not needed or indeed counterproductive. I look forward to debating how these lessons might extend to other areas of brain research.

The research in my lab focuses on sensory signal processing, particularly in cases where sensory systems perform at or near the limits imposed by physics. Photon counting in the visual system is a beautiful example. At its peak sensitivity, the performance of the visual system is limited largely by the division of light into discrete photons. This observation has several implications for phototransduction and signal processing in the retina: rod photoreceptors must transduce single photon absorptions with high fidelity, single photon signals in photoreceptors, which are only 0.03 – 0.1 mV, must be reliably transmitted to second-order cells in the retina, and absorption of a single photon by a single rod must produce a noticeable change in the pattern of action potentials sent from the eye to the brain. My approach is to combine quantitative physiological experiments and theory to understand photon counting in terms of basic biophysical mechanisms. Fortunately there is more to visual perception than counting photons. The visual system is very adept at operating over a wide range of light intensities (about 12 orders of magnitude). Over most of this range, vision is mediated by cone photoreceptors. Thus adaptation is paramount to cone vision. Again one would like to understand quantitatively how the biophysical mechanisms involved in phototransduction, synaptic transmission, and neural coding contribute to adaptation.

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy usage are regulated during food scarcity. I addressed this in the visual cortex of awake mice using whole-cell recordings and two-photon imaging to monitor layer 2/3 neuronal activity and ATP usage. I found that food restriction reduced synaptic ATP usage by 29% through a decrease in AMPA receptor conductance. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting membrane potential. Consequently, neurons spiked at similar rates as controls, but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost since it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening in orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. These findings reveal novel mechanisms that dynamically regulate energy usage and coding precision in neocortex.

Axons in the optic nerve are arranged in bundles and conducting action potential with resistance related to their membrane. Optic nerve axons do not form absolutely independent conductive channels. They are directly coupled by gap junctions formed in majority by neuronal Cx45. Coupling of axons, except known transpassing functions, allows to reduce axonal membrane resistance of optic nerve and accelerates transduction of visual signal. This novel finding have substantial implications for understanding of the pathogenesis of various optic neuropathies and identifies a new potential target for a therapeutic approach.

Our laboratory seeks to understand how neural circuits receive, compute, encode and transmit information. More specifically, we’d like to learn what biophysical and morphological features equip synapses, neurons and networks to perform these tasks. The retina is a model system for the study of neuronal information processing: We can deliver precisely defined physiological stimuli and record responses from many different cell types at various points within the network; in addition, retinal circuitry is particularly well understood, enabling us to interpret more directly the impact of synaptic and cellular mechanisms on circuit function. I will present recent experiments in the lab that exploit these advantages to examine how synapses and neurons within retinal amacrine cell circuits perform specific visual computations.