Abigail Morrison

The paper introduces a framework for simulating multicompartment neurons with customizable subcellular dynamics using NEST and NESTML, enabling efficient and scalable network simulations.

This paper shows dendritic modulation enables multitask representation learning in hierarchical sensory processing by allowing neurons to adaptively extract features across different contexts.

The paper shows that topographic modularity in neural circuits improves signal-to-noise ratio and task performance through recurrent inhibition.

The paper replicates and analyzes a modular spiking network with reward-based learning, highlighting its robustness and limitations under different conditions.

This paper introduces a framework for predicting extracellular brain signals from neuronal network models, showing that linearized biophysical models can accurately capture spatiotemporal dynamics of these signals.

The paper introduces a design template for multiscale cosimulation in neuroscience, enabling integration of cellular and population-level models to enhance computational modeling capabilities.

The paper benchmarks two neuromorphological simulators, NEURON and Arbor, on CPUs and GPUs, showing Arbor can be 60-400x faster and 100x more energy efficient.

This paper introduces a spiking neural network architecture combining unsupervised learning and clustered connectivity, showing improved performance on reinforcement learning tasks compared to unclustered or static input networks.

This study investigates how structured connectivity in modular spiking networks enables efficient and accurate information transfer between modules during stimulus processing.

The paper investigates how heterogeneity in neuronal and synaptic properties enhances computational power in cortical microcircuits, showing that heterogeneous models outperform homogeneous ones.

The paper derives likelihoods for health conditions from fMRI-graph data to improve clinical decision-making by providing probabilistic outputs rather than binary classifications.