Anders Lansner

This study evaluates synergistic interactions between three short-term plasticity mechanisms in spiking neural networks to enhance working memory performance across diverse tasks.

The paper proposes a computational model to explain odor naming omissions by linking Bayesian-Hebbian learning to linguistic ambiguity in odor-label associations.

The paper builds a spiking network model of rat episodic memory to study how long- and short-term synaptic plasticity interact in memory tasks.

This paper proposes a cortical spiking neural network model with Bayesian-Hebbian learning to explain semantization in episodic memory, showing it outperforms STDP in capturing decontextualization.

The paper proposes a WM indexing theory using a spiking neural network model to explain how PFC associates, maintains, and updates multi-modal LTM representations through fast Hebbian plasticity.

The paper introduces a biophysically plausible double bouquet cell model into a cortical microcircuit to address issues with learning rules that simultaneously learn excitation and inhibition.

The paper investigates how a probabilistic associative learning model can encode and reproduce temporal structures of multiple sequences, showing its robustness to noise and ability to handle overlapping sequences through modularity.