Can neurons speak? Semantic narration of vision at single-cell resolution

Identifying what individual neurons encode in higher-order visual cortex is an open problem. Responses resist intuitive parameterization, and the deep-network embeddings used in their place are black ...

arxiv.org

Can neurons speak? Semantic narration of vision at single-cell resolution

Identifying what individual neurons encode in higher-order visual cortex is an open problem. Responses resist intuitive parameterization, and the deep-network embeddings used in their place are black ...

arxiv.org

Can neurons speak? Semantic narration of vision at single-cell resolution

Identifying what individual neurons encode in higher-order visual cortex is an open problem. Responses resist intuitive parameterization, and the deep-network embeddings used in their place are black ...

arxiv.org

GitHub - arnaumarin/neurrator

Contribute to arnaumarin/neurrator development by creating an account on GitHub.

github.com

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationa...

doi.org

Vision-Language Models Suppress Female Representations Under Ambiguous Input

Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far less is known about ambiguous inputs (a worker in f...

arxiv.org

Vision-Language Models Suppress Female Representations Under Ambiguous Input

Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far less is known about ambiguous inputs (a worker in f...

arxiv.org

Benchmarking spike source localization algorithms in high density probes

Author summary Accurately estimating neuron locations from extracellular recordings is critical to building reliable brain–machine interfaces. This spatial information enhances spike sorting and enabl...

dx.plos.org

Benchmarking spike source localization algorithms in high density probes

Author summary Accurately estimating neuron locations from extracellular recordings is critical to building reliable brain–machine interfaces. This spatial information enhances spike sorting and enabl...

dx.plos.org

Should neuroscientists ‘vibe code’?

Researchers are developing software entirely through natural language conversations with advanced large language models. The trend is transforming how research gets done—but it also presents new…

thetransmitter.org

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationa...

advanced.onlinelibrary.wiley.com

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationa...

doi.org

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationa...

advanced.onlinelibrary.wiley.com

Should neuroscientists ‘vibe code’?

Researchers are developing software entirely through natural language conversations with advanced large language models. The trend is transforming how research gets done—but it also presents new…

thetransmitter.org

An autonomous AI agent for universal behavior analysis

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit r...

biorxiv.org

Realigning representational drift in mouse visual cortex by flexible brain-machine interfaces

The ability to stably decode brain activity is crucial for brain-machine interfaces (BMIs), which are often compromised by recording instability due to immune responses and probe drifting. In addition...

biorxiv.org

Neural manifolds: more than the sum of their neurons - Nature Reviews Neuroscience

In this Journal Club, Juan Gallego discusses a 2014 article that provided a first causal hint that neural manifolds may not only be a convenient way to interpret neural population activity.

nature.com

Neural models for detection and classification of brain states and transitions - Communications Biology

A deep learning self-supervised hybrid CNN-autoencoder model is used to detect brain states and transitions, like wakefulness, slow oscillations and microarousals, during the emergence from anesthesia...

doi.org

Neural models for detection and classification of brain states and transitions

Communications Biology - A deep learning self-supervised hybrid CNN-autoencoder model is used to detect brain states and transitions, like wakefulness, slow oscillations and microarousals, during...

rdcu.be

Spike sorting AI agent

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting metho...

biorxiv.org

No More Adam: Learning Rate Scaling at Initialization is All You Need

In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum (SGDM...

arxiv.org

BI 199 Hessam Akhlaghpour: Natural Universal Computation | Brain Inspired

braininspired.co

Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems

Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major chal...

arxiv.org