Efficient coding

…framework based on the principle of efficient coding (Barlow,

…redundancy reduction, and efficient coding, focuses on the decision-related information represented by neural responses and how it is extracted from sensory populations. In another framework, based on the Bayesian brain hypothesis, sensory responses represent posterior beliefs about the outside world, which then guide decision-making. Interes

Human perception is significantly influenced by sensory context. A classic demonstration is the tilt illusion, in which the perceived orientation of a center stimulus is altered by the orientation of a surround (Gibson \& Radner 1937). Previous research on the tilt illusion has primarily focused on how surround context alters the response characteristics of individual neurons (e.g., changes in gain and tuning curves), but connecting these observations at the level of single neurons to perceptual behavior remains challenging. More broadly, we still lack a functional and teleological account of

Sensory systems continuously adapt their responses based on the frequency of stimuli they encounter. Our goal is to develop a clear understanding of how populations of cortical neurons adapt to varying sensory environments. Recent experiments in the mouse primary visual cortex (V1) have shown a remarkable finding: the magnitude of population responses follows a power law with respect to stimulus frequency. This power law is universal, with the same exponent observed across different statistical environments, enabling predictions of population responses to new environments and allowing the brai

…ranging from sensory adaptation over efficient coding to various flavors of probabilistic estimation, variably called Bayesian or predictive or optimal. Here we want to demonstrate with several examples that some reasons for attraction vs. repulsion have been overlooked, neglected, or not acknowledged for specific domains. One example for st

…cells indicate that they arise under efficient coding schemes, with metabolic, sparsity, and nonnegativity constraints supporting the formation of regular lattices that approximate what has been observed in animal experiments. Here we import the notion of local a

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers.

…in networks optimized to perform efficient encoding. We consider a network of leaky integrate-and-fire neuro

…into general principles of perception (efficient coding, Bayesian inference) that are obscured when studied in

Sensory neural representations are modulated by a variety of contextual factors, such as multi-modal cues, stimulus history, novelty, behavioral utility, and internal states. Despite decades of attention in systems neuroscience, many questions persist regarding how sensory codes adapt to these different variables. Here, we study this problem in the olfactory system. We focus on a neural circuit (the bulb) where incoming odor inputs are integrated with extensive contextual feedback from areas of the brain involved in multi-sensory association, attention, memory, and decision- making. While most

Biological visual systems have evolved around the efficient coding of natural image statistics in order to support recognition of complex visual patterns. Recent work has shown that deep neural networks are able to learn similar representations to those measured in visual areas in animals, suggesting they may serve as models for the brain. Varying network architectures and loss functions has been shown to modulate the biological similarity learned representations, however the extent to which this results from exposure to natural image statistics during training has not been fully characterized

…we extend an existing framework for efficient coding to decouple the density of neurons from their slopes. We compare the optimally efficient population to measurements of reward prediction error neurons. We find three aspects of the optimized population confirmed: Fi

…by a model based on the principle of coding efficiency. The model assumes th

Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population resp

Sensory neural circuits rely on a common set of motifs to process inputs, including convergence of multiple inputs to a single neuron, divergence of inputs into parallel pathways, and nonlinearities that are selective for some inputs over others. Past work has detailed how optimized response nonlinearities and synaptic weights can maximize encoded information, but these solutions depend on tightly tuned response functions and connectivities. Our study found that incorporating generic, non-invertible, selectivity-inducing nonlinearities into a circuit with divergent and convergent structure can

…noise mitigating effects based on efficient coding principles. Network models with adaptive neurons have revealed advantages including modulation of total activity, supporting Bayesian inference, and allowing computations over distributed timescales. Such efforts are bottom-up, modeling adaptive mechanisms from physiology and analyzing their effects. How top-down environmental and functional pressures influence the specificity of adaptation

…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

…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

…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

…first principles in the context of efficient coding, metabolic and transport networks, evolution, reinforcement learning, and decision making, by postulating that a system has evolved to optimize some utility function under biophysical constraints. Yet as normative theories become increasingly high-dimensional and optimal solutions stop being unique, it gets progressively hard to judge whether theoretical predictions are consis