Taehoon Kim

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

This paper introduces an ensemble artificial neural network (eANN) to improve neuronal connectivity inference from spike trains, showing it outperforms existing methods across different dynamical regimes.

Scalable covariance-based connectivity inference for synchronous neuronal networks

This paper introduces FDDC, a method for inferring connectivity in neuronal networks, showing it outperforms existing methods and reveals a negative correlation between small-worldness and network synchrony.

Parallel reconstruction of the excitatory and inhibitory inputs received by single neurons reveals the synaptic basis of recurrent spiking

The paper develops an experimental platform to analyze excitatory and inhibitory synaptic inputs in neurons and finds that spiking coincides with rapid changes in the E/I ratio.

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

The paper introduces DeePhys, a tool for functional phenotyping of neurons using electrophysiological data, and shows it can predict genotype and age in iPSC-derived DA neurons with the A53T SNCA mutation.