Romain D. Cazé

The paper demonstrates that sublinear dendritic summation enables neurons to perform linearly non-separable computations by showing increased spiking probability with scattered synaptic activation.

This paper predicts granule cells can perform linearly non-separable computations using Boolean algebra and biophysical modeling.

This paper shows dendrites allow neurons to perform complex computations with less precise synaptic weights, improving efficiency in both biological and artificial systems.

The paper demonstrates how dendritic non-linear processing enables stimulus selectivity in neurons, even when preferred stimulus input is not dominant, and shows increased robustness to synaptic and dendritic failures.

This paper investigates how synaptic clusters form and function in neurons, showing that specific rules govern their distribution and that changes in input statistics can reshape these clusters, with implications for both biological learning and artificial neural networks.