Topic: receptive fields

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. While these phenomena have been studied extensively with experiments and theory, the underlying principles for combining E and I inputs are still unclear. Here, the rules for probabilistically combining E and

Natural visual environments are full of complex and diverse patterns. Recent neurophysiological experiments based on artificial patterns have suggested that macaque V1 neurons’ receptive fields are more complex and diverse in feature selectivity (Tang et al 2017) rather than just oriented Gabor filters. However, that finding was based on extensive parametric artificial stimuli used and might be biased. Hence, the nature of the neural codes of macaque V1 neurons remains controversial. To resolve this question, we performed 2-photon calcium imaging on three macaque monkeys to obtain 1689 V1 neur

Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses

The upper visual layers of the mouse superior colliculus (SC) exhibit an orientation preference map (OPM) with a retinotopy-based concentric pattern (Ahmadlou and Heimel, 2015; Feinberg and Meister, 2015; but see Chen et al., 2020). The underlying developmental mechanisms that give rise to this concentric OPM in the mouse SC are still unclear. During visual system development, the stage III (S3) retinal waves are known to play important roles in circuit formation. Specifically, the S3 waves that tend to propagate towards the caudal direction (Gribizis et al., 2019) have impacts on the directio

The efficient coding hypothesis posits that sensory systems are adapted to the statistics of their inputs, capturing essential structure while minimizing the use of resources (neurons, spikes, etc). A variety of formulations have been developed and, differing primarily in their definition of efficiency. For example, Independent Components Analysis [Bell and Sejnowski, 1997] seeks a complete set of axes along which the data distribution is heavy-tailed. Sparse Coding [Olshausen and Field, 1996] learns a set of basis functions that can sparsely reconstruct natural image patches (

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. By deploying an efficient coding argument, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. To test this experimentally, developed a simple, novel imaging system that is indispensable for studies at this scale. In agreement with our predictions, we c

Everything we see has a location. We know where things are before we know what they are. But how do we know where things are? Receptive fields in the visual system specify location but neural delays lead to serious errors whenever targets or eyes are moving. Motion may be the problem here but motion can also be the solution, correcting for the effects of delays and eye movements. To demonstrate this, I will present results from three motion illusions where perceived location differs radically from physical location. These help understand how and where position is coded. We first look at the ef

The Stabilized Supralinear Network is a model of recurrently connected excitatory (E) and inhibitory (I) neurons with a supralinear input-output relation. It can explain cortical computations such as response normalization and inhibitory stabilization. However, the network's connectivity is designed by hand, based on experimental measurements. How the recurrent synaptic weights can be learned from the sensory input statistics in a biologically plausible way is unknown. Earlier theoretical work on plasticity focused on single neurons and the balance of excitation and inhibition but did not cons

Human vision relies on rapid eye movements (saccades) 2-3 times every second to bring peripheral targets to central foveal vision for high resolution inspection. This rapid sampling of the world defines the perception-action cycle of natural vision and profoundly impacts our perception. Marmosets have similar visual processing and eye movements as humans, including a fovea that supports high-acuity central vision. Here, I present a novel approach developed in my laboratory for investigating the neural mechanisms of visual processing using naturalistic free viewing and simple target foraging pa

Animals are able to perceive self-motion and navigate in their environment using optic flow information. They often perform visually guided stabilization behaviors like the optokinetic (OKR) or optomotor response (OMR) in order to maintain their eye and body position relative to the moving surround. But how does the animal manage to perform appropriate behavioral response and how are processing tasks divided between the various non-cortical visual brain areas? Experiments have shown that the zebrafish pretectum, which is homologous to the mammalian accessory optic system, is involved in the OK

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 disc

Recent research in awake, behaving organisms revealed the strong modulatory effects of movement on sensory coding. Surprisingly, these effects are not consistent across species. For example, in rodents and insects locomotion increases the magnitude of visual responses [1-3], while in primates, locomotion has a weak suppressive influence [4]. These differences raise intriguing questions about the computational purpose of such modulations and the generality of the underlying principles of sensory processing. Here, we address these questions from a theoretical perspective. Our approach is groun

Spontaneous activity in young ferrets (p22) is organised into modular, low-dimensional patterns across multiple cortical areas [1]. In the following two weeks (p22-43), these activity patterns undergo a transition similar in all areas, and show a gradual increase in dimensionality in most areas, suggesting an expansion of representational capacity [2]. However, it is unclear what the functional role of having low-dimensional activity patterns at the initial stage of development is, since one would expect the limited dimensionality to restrict representational learning. In this work, we investi

Perception requires integrating noisy dynamic visual information across the visual field to identify relevant stimuli and guide decisions. While temporal integration has been studied extensively in experiments with highly controlled visual stimuli and reverse-correlation techniques, the nonlinear mechanisms underlying spatial integration are often neglected. More concretely, neurons in the Middle Temporal area (MT) respond heterogeneously and nonlinearly to stimuli inside their receptive fields (RFs) [1-3], and these neurophysiological properties could mediate spatial suppression of motion obs

The role of sensory systems is to interpret real-world events to facilitate behavior. For instance, the visual system enacts compensatory eye movements based on a global motion representation, generated by combining local motion signals from the retina. However, due to the aperture problem, local motion signals are inherently ambiguous, and how the visual system solves the aperture problem is unclear. We studied how the mouse Nucleus of the Optic Tract (NOT) integrates retinal visual information and how this supports behavioral responses. NOT mediates the horizontal optokinetic reflex, the com