neuroscience brain-computer-interfaces cognitive-science ai-research

The circuit that lets your brain think and see

engineering.columbia.edu

July 3, 2026

6 min read

🔥🔥🔥🔥🔥

49/100

Summary

Research at Columbia Engineering reveals that the brain's circuitry operates more efficiently than previously understood, challenging traditional views of how visual information is processed. Nuttida Rungratsameetaweemana proposes that the brain integrates thinking and seeing more directly than the conventional model suggests.

Key Takeaways

Researchers at Columbia Engineering found that early visual areas of the cortex actively process information differently based on the task being performed, rather than merely relaying visual data.
A neural network model developed by the team demonstrated that inhibitory neurons, which suppress other inhibitory neurons, are crucial for the brain's ability to switch between tasks.
Silencing the specific inhibitory neurons in the visual cortex of mice impaired their ability to track task context, aligning with predictions made by the neural network model.
The study emphasizes the importance of building simple neural models that reflect biological realities to understand the mechanisms of brain function.

Read original article

View story →

Coverage & discussions

Hacker News · engineering.columbia.edu

The circuit that lets your brain think and see ↗

Community discussions

Hacker News discussion ↗

Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Mixed

Positives

Reverse engineering how algorithms in the brain work is a really promising path towards making genuine AI systems which would make the current crop of LLMs obsolete.

Concerns

Why are they using neural nets to model observed behavior when biological neurons work completely differently? This feels like a fundamental mismatch.
The actual experiment is just training a large RNN to do a simple task — it seems hard to claim we've learned anything useful about brain function from this.