Sensory systems

The second three-year funding period studies adaptive sensory processing through mammalian cortical–subcortical circuits. Priorities are plasticity across timescales, naturalistic behaviour and comparisons between sensory systems. Projects must examine or model microcircuit activity in awake mammals, involve at least two interacting structures including a sensory structure, manipulate neural activity or behaviour, and share data, software and models. Purely anatomical studies, isolated single-region recordings, disease-focused projects, non-mammalian systems and theory without experimental lin

Ant colonies depend heavily on chemical communication, creating demanding problems for sensory gene regulation. This lecture examines the expansion of olfactory-receptor gene families and the mechanisms that select a receptor for each neuron. Comparisons with vertebrates illuminate how stochastic gene choices support organised sensory systems.

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

A major challenge in sensory systems is to understand how neurons extract information from the natural environment. Models derived from their responses to artificial stimuli often have a hard time to generalize and predict responses to natural scenes. However, models directly learned on the responses to natural scenes can be hard to interpret. To address this issue, we have recently developed an approach where we add small perturbations to natural scenes and measure how these perturbations change neuronal responses, to better understand the features extracted by sensory neurons. I will show se

Real world information received by sensory systems is highly dynamic. If and how the recent temporal dynamics, as in the preceding intervals, shape cortical processing is unclear. Here we recorded the cortical signals (EEG) in response to a dynamic train of tactile stimulations applied at the fingertips ( n = 64). We separated the cortical signals according to the next interval dynamics by using a joint-interval distribution (JID) spanning 100ms to 10s. This yielded a two-dimensional event-related potential (JID-ERP) at all the examined time points of the EEG signal (-200 to 300 ms latency fro

Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inp

Sensory systems transform inputs from the environment into internal representations that capture aspects of the information useful in downstream computation and behavior. These representations are not rigid, static reflections of the external world but are dynamically modulated by fluctuating cortical and behavioral states. Locomotion, for example, is associated with facilitative changes in stimulus representation in the mouse visual cortex (Vinck et al 2015). How does sensory processing in the visual system support downstream behavior? "Straightening" hypotheses posit that an important role o

Efficiently transmitting information from dynamic environments necessitates sensory systems that rapidly and reversibly adapt to changes in input statistics. Single neurons adjust their input-output gains to adaptively normalize their stimulus-driven response variance (Fairhall et al. 2001). In neural populations, statistical whitening and related adaptations have been observed (Dan et al. 1996; Benucci et al. 2013; Wanner and Friedrich 2020), whereby joint statistics of neurons are decorrelated in addition to being re-scaled to have equal variance. Existing models of neural population adapt

Neural activity in many sensory systems is organized on low-dimensional manifolds by means of convex receptive fields. Neural codes in these areas are constrained by this organization, as not every neural code is compatible with convex receptive fields. The same codes are also constrained by the structure of the underlying neural network. In my talk I will attempt to provide answers to the following natural questions: (i) How do recurrent circuits generate codes that are compatible with the convexity of receptive fields? (ii) How can we utilize the constraints imposed by the convex receptive

Cells preferentially responding to visual motion in a particular direction are said to be direction-selective, and these were first identified in the primary visual cortex. Since then, direction-selective responses have been observed in the retina of several species, including mice, indicating motion analysis begins at the earliest stage of the visual hierarchy. Yet little is known about how retinal direction selectivity contributes to motion processing in the visual cortex. In this talk, I will present our experimental efforts to narrow this gap in our knowledge. To this end, we used genetic

A critical function of sensory systems is to reliably extract ethologically relevant features from the complex natural environment. A classic model to study feature detection is the direction-selective circuit of the mammalian retina. In this talk, I will discuss our recent work on how visual contexts dynamically influence the neural processing of motion signals in the direction-selective circuit in the mouse retina.

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 (

Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the

…In early sensory systems, the way neurons respond to stimuli can be summarized in their receptive fields (RFs), and these are structured to detect certain features. Experimental data show that they are also noisy, but this randomness remains unaccounted for in existing models. By taking this unexplained variability into account, we reveal how the interplay between structure and random noise provides important computational benefits for sensory encoding. Furthermore, structured randomness offers a biolog

In order for animals to thrive in their complex environments, their sensory systems must form representations of objects that are invariant to changes in some dimensions of their physical cues. For example, we can recognize a friend’s speech in a forest, a small office, and a cathedral, even though the sound reaching our ears will be very different in these three environments. I will discuss our recent experiments into how neurons in auditory cortex can form stable representations of sounds in this acoustically varied world. We began by using a normative computational model of hearing to exami

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste