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.

We hardly notice when there is a speck on our glasses, the obstructed visual information seems to be magically filled in. The mechanistic basis for this fundamental perceptual phenomenon has, however, remained obscure. What enables neurons in the visual system to respond to context when the stimulus is not available? While feedforward information drives the activity in cortex, feedback information is thought to provide contextual signals that are merely modulatory. We have made the discovery that mouse primary visual cortical neurons are strongly driven by feedback projections from higher visual areas when their feedforward sensory input from the retina is missing. This drive is so strong that it makes visual cortical neurons fire as much as if they were receiving a direct sensory input. These signals are likely used to predict input from the feedforward pathway. Preliminary results show that these feedback projections are strongly influenced by experience and learning.

Research in our lab focuses on the circuit mechanisms underlying sensory computation. We use the mouse retina as a model system because it allows us to stimulate the circuit precisely with its natural input, patterns of light, and record its natural output, the spike trains of retinal ganglion cells. We harness the power of genetic manipulations and detailed information about cell types to uncover new circuits and discover their role in visual processing. Our methods include electrophysiology, computational modeling, and circuit tracing using a variety of imaging techniques.

A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.

Vision begins in the eye, and what the “retina tells the brain” is a major interest in visual neuroscience. To deduce what the retina encodes (“tells”), computational models are essential. The most important models in the retina currently aim to understand the responses of the retinal output neurons – the ganglion cells. Typically, these models make simplifying assumptions about the neurons in the retinal network upstream of ganglion cells. One important assumption is linear spatial integration. In this talk, I first define what it means for a neuron to be spatially linear or nonlinear and how we can experimentally measure these phenomena. Next, I introduce the neurons upstream to retinal ganglion cells, with focus on bipolar cells, which are the connecting elements between the photoreceptors (input to the retinal network) and the ganglion cells (output). This pivotal position makes bipolar cells an interesting target to study the assumption of linear spatial integration, yet due to their location buried in the middle of the retina it is challenging to measure their neural activity. Here, I present bipolar cell data where I ask whether the spatial linearity holds under artificial and natural visual stimuli. Through diverse analyses and computational models, I show that bipolar cells are more complex than previously thought and that they can already act as nonlinear processing elements at the level of their somatic membrane potential. Furthermore, through pharmacology and current measurements, I illustrate that the observed spatial nonlinearity arises at the excitatory inputs to bipolar cells. In the final part of my talk, I address the functional relevance of the nonlinearities in bipolar cells through combined recordings of bipolar and ganglion cells and I show that the nonlinearities in bipolar cells provide high spatial sensitivity to downstream ganglion cells. Overall, I demonstrate that simple linear assumptions do not always apply and more complex models are needed to describe what the retina “tells” the brain.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviourally in mice. The discrepancy between neural and behavioural discrimination could not be explained by the types of stimuli we used, by behavioural states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.

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.