Áine Byrne

This paper introduces a network-based method to detect neuronal assemblies from spike train data using the van Rossum distance and Louvain algorithm, validated on synthetic and real datasets.

This paper develops a stochastic neural mass model to simulate cortical beta bursts in Parkinson's disease, showing that reduced background drive and enhanced synaptic coupling can reproduce and counteract altered burst dynamics.

The paper develops mean-field approximations for neural networks with STDP to provide low-dimensional descriptions of their dynamics and improve computational efficiency.

This paper introduces a mean-field model for spiking neuron networks that can simulate synaptic and gap-junction interactions, showing its application in understanding EEG/MEG signals and synaptic waves.

This paper develops predictive coding models to study pain perception by analyzing neural activity in rats, revealing insights into how expectation and chronic pain influence neural dynamics.

This paper introduces a neuronal model for beat generation that learns isochronous rhythms and synchronizes with external stimuli across music-relevant frequencies.