Gabriel C. Mel

The paper develops a predictive theory for experimental design in neural population geometry inference, showing that more neurons allow accurate inference with fewer trials.

The paper shows that random connectivity in neural networks preserves representational similarity despite activity drift, applicable to biological circuits.

The paper develops a mathematical model to explain how pretreatment with PMA enhances detection of rare microbial species through thermodynamic principles.

The paper reassesses claims about grid cell emergence in path-integrator circuits, finding consistency with prior theory.

This paper shows that hexagonal grid cell patterns emerge in neural networks trained for path integration and develops methods to analyze these networks for mechanistic insights.

The paper investigates how visual cortex cells detect object boundaries by analyzing image statistics and showing that 'incitatory' cell-cell interactions can produce boundary detection from simple cells.

This study uses task-driven neural networks to show that heterogeneous MEC cells are functionally significant and that reward-modulated models better capture MEC response profiles than traditional models.