Yashar Ahmadian

This study shows that reciprocal excitatory connections between cortical areas create line attractor dynamics that promote consensus on stimulus features across areas.

The paper shows that a retinotopically organized stabilized supralinear network with specific horizontal connections explains both contrast-dependent gamma oscillations and surround suppression in macaque V1.

The paper explains neural response homeostasis and stimulus-specific adaptation through an efficient coding framework, showing how adaptation optimizes population coding fidelity with minimal metabolic cost.

This study shows freely-moving mice use sniff-synchronized nose movements to navigate odor gradients, identifying two distinct movement motifs during olfactory search.

The paper shows that place-cell activity in mice is modulated by heading direction relative to a reference point.

The paper shows that odor concentration discrimination is independent of sniff frequency, and a fluid dynamics model explains variability in MT cell responses, enabling sniff-invariant coding.

This paper uses GANs to fit mechanistic neural network models to experimental tuning curves, capturing their diversity without requiring explicit likelihood computation.

The paper shows that deviations from the matching law in foraging behavior reflect an optimal strategy using multi-timescale learning to balance bias and variance under uncertainty.

The paper shows that a stochastic Stabilized Supralinear Network model explains stimulus-induced changes in cortical variability and correlations.