Comprehensive characterization of human color discrimination thresholds

Leveraging the smooth variation of internal noise makes comprehensive characterization of color discrimination practical, offering an efficient framework for studying perceptual limits across various ...

elifesciences.org

Sparse component analysis: A method that uncovers separable computations within neural population activity

Zimnik et al. provide a new, interpretable, unsupervised dimensionality approach for neural population activity. Their approach, sparse component analysis (SCA), reveals the latent factors corresponding to distinct computations without users needing to provide behavioral labels or known timing of computations.

dlvr.it

The Simons Collaboration on Ecological Neuroscience: Studying how the brain interacts with the world

The Simons Collaboration on Ecological Neuroscience (SCENE) seeks to uncover general principles of brain function through an ecological perspective: studying perception, cognition, and action in the context of the affordances available to different agents. Here, Angelaki et al. introduce SCENE’s goals, hypotheses, and approaches outlining a collaborative vision for the next decade.

dlvr.it

Reward-learning algorithm hardwired into dopamine circuit

The finding bolsters the canonical model of reward prediction error, which has come under scrutiny in recent years.

thetransmitter.org

A critical initialization for biological neural networks - Nature

Basic properties of symmetric random matrices can explain the emergence of macroscopic patterns in neural networks, suggesting that initialization conditions of connected neural populations may confer computational advantages across the mouse brain.

go.nature.com

Neural representation of action symbols in primate frontal cortex - Nature

A drawing-like task designed to study compositional generalization identifies a specific neural population in the ventral premotor cortex in primates that encodes action symbols.

go.nature.com

Do deep learning models recognize 3D shapes in the same way humans do?

A chair can still look like a chair even when its surface is reduced to a sparse cloud of points. Humans are remarkably good at recognizing objects from this kind of minimal 3D information. A new stud...

santafe.edu

Neural population geometry and optimal coding of tasks with shared latent structure - Nature Neuroscience

The authors analytically determine how neuronal correlations and geometry collectively determine readout generalization across tasks and show how these geometric features follow distinct trajectories ...

nature.com

Why neural foundation models work, and what they might—and might not—teach us about the brain

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

thetransmitter.org

Visual information is broadcast among cortical areas in discrete channels

Activity fluctuations, or noise correlations, are robust measures of neuronal connectivity and they reveal that neural circuity controls mixing of information at a granular level across millimeter-length...

buff.ly

Ecological visual processing in the mouse | Bionic Vision Lab

We review computations that are engaged in ecological contexts, including active sensing, motion processing, scene analysis, distance estimation, and spatial perception.

bionicvisionlab.org

Statistics of natural scenes shape contextual modulation in the visual cortex

How does visual context shape neuronal responses to local features? Fu et al. demonstrate that surround modulation in primary visual cortex neurons reflects how well context matches the optimal local feature under natural scene statistics—probable continuations facilitate, improbable ones suppress—consistent with Bayesian inference leveraging prior knowledge of the visual world.

dlvr.it

Linking neural manifolds to circuit structure in recurrent networks

Neural population activity can be described either by low-dimensional dynamics on neural manifolds or by single-neuron selectivities. Using a theoretical approach, Pezon et al. relate these two statistical descriptions to circuit structure in recurrent networks. Their results reveal both degeneracies and specific constraints in how circuit structure shapes neural activity.

dlvr.it

Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes

YouTube video by Cosyne Talks

youtube.com