First-author publication in computational neuroscience

I’m excited to announce the publication of a first-author paper under mentorship of Dean Buonomano at UCLA and in collaboration with Ash Tanwar. This paper was the culmination of years of work using deep learning models trained on tasks designed to tease out explanations for how the brain represents timing and memory through the intrinsic dynamics of neural circuits.

The paper can be read in full here. Here are the highlights:

• A recurrent neural network (RNN) can implement timing, working memory (WM), and the comparison of intervals.
• RNN units (neurons) exhibit mixed selectivity (i.e. not tuned only to timing or WM, but tuned to both)
• Units contain more information about the timing of the interval than the WM of the interval
• Our work predicts how a human brain could represent timing and WM information