A mechanosensory feedback that uncouples external and self-generated sensory responses in the olfactory cortex.

Sampling behaviors have sensory consequences that can hinder perceptual stability. In olfaction, sniffing affects early odor encoding, mimicking a sudden change in odor concentration. We examined how the inhalation speed affects the representation of odor concentration in the main olfactory cortex.

Unsupervised learning of mid-level visual representations.

Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn to exploit the statistical structure of their inputs in the absence of explicit training targets or rewards. Sophisticated experimental approaches have enabled the investigation of the influence of sensory experience on neural self-organization and its synaptic bases. Meanwhile, novel algorithms for unsupervised and self-supervised learning have become increasingly popular both as inspiration for theories of the brain, particularly for the function of intermediate visual cortical areas, and as building blocks of real-world learning machines.

Dynamics of cortical contrast adaptation predict perception of signals in noise.

Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background.

Time as a supervisor: temporal regularity and auditory object learning.

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects," that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e.

Distributed processing for value-based choice by prelimbic circuits targeting anterior-posterior dorsal striatal subregions in male mice.

Fronto-striatal circuits have been implicated in cognitive control of behavioral output for social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output mediated by connectivity to dorsal striatum. Despite evidence for functional diversity along the anterior-posterior striatal axis, it is unclear how distinct fronto-striatal sub-circuits support value-based choice.

How Occam's razor guides human decision-making.

Occam's razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to play a role in human perception and decision-making, but the nature of our presumed preference for simplicity is not understood. Here we use preregistered behavioral experiments informed by formal theories of statistical model selection to show that, when faced with uncertain evidence, human subjects exhibit preferences for particular, theoretically grounded forms of simplicity of the alternative explanations.

Efficient processing of natural scenes in visual cortex.

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes.

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle.

Temporal stability of stimulus representation increases along rodent visual cortical hierarchies.

Cortical representations of brief, static stimuli become more invariant to identity-preserving transformations along the ventral stream. Likewise, increased invariance along the visual hierarchy should imply greater temporal persistence of temporally structured dynamic stimuli, possibly complemented by temporal broadening of neuronal receptive fields. However, such stimuli could engage adaptive and predictive processes, whose impact on neural coding dynamics is unknown.

Embo: a Python package for empirical data analysis using the Information Bottleneck.

We present , a Python package to analyze empirical data using the Information Bottleneck (IB) method and its variants, such as the Deterministic Information Bottleneck (DIB). Given two random variables and , the IB finds the stochastic mapping of that encodes the most information about , subject to a constraint on the information that is allowed to retain about . Despite the popularity of the IB, an accessible implementation of the reference algorithm oriented towards ease of use on empirical data was missing.

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits.

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them.

Information Theory in Neuroscience.

This is the Editorial article summarizing the scope and contents of the Special Issue, Information Theory in Neuroscience.

Distinct timescales of population coding across cortex.

The cortex represents information across widely varying timescales. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds, whereas in association cortex behavioural choices can require the maintenance of information over seconds. However, it remains poorly understood whether diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity.

Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce.

Network structure within the cerebellar input layer enables lossless sparse encoding.

The synaptic connectivity within neuronal networks is thought to determine the information processing they perform, yet network structure-function relationships remain poorly understood. By combining quantitative anatomy of the cerebellar input layer and information theoretic analysis of network models, we investigated how synaptic connectivity affects information transmission and processing. Simplified binary models revealed that the synaptic connectivity within feedforward networks determines the trade-off between information transmission and sparse encoding.