Milad Lankarany

The paper uses computational simulations to show that spike-timing-dependent plasticity (STDP) regulates synchronization and desynchronization dynamics, resolving the synchronization/desynchronization conundrum.

The paper investigates whether quasi-evoked inhibition in Vim neurons correlates with clinical tremor reduction and its dependence on stimulation location.

This study investigates how electrical stimulation affects neuronal network dynamics, showing state-dependent suppression of beta oscillations and frequency-specific responses.

This paper develops a neural network model to explain frequency-selective deep brain stimulation effects, showing that a boundary equilibrium bifurcation explains both excitatory suppression and GABAergic output changes.

This paper introduces a computational model to understand how deep brain stimulation affects neuronal circuits by considering synaptic properties, connectivity architecture, and circuit motifs.

The paper develops a computational framework to explain how FES therapy induces LTP-dominant plasticity through voltage-dependent synaptic rules and spike interactions.

This study investigates how high-frequency DBS of the thalamic Vim suppresses neuronal firing by analyzing synaptic and network-level mechanisms in patients with essential tremor.

DIME improves SOZ localization using iEEG data, achieving high recall and accuracy in predicting surgical outcomes.

The paper semi-automatically annotates ictal chirps in iEEG data from epilepsy patients and finds their spectro-temporal features correlate with seizure onset zones and surgical outcomes.

The paper develops MATLAB-based software to automatically identify and analyze theta rhythms in hippocampal EEG/LFP data, showing it can robustly detect feature changes during seizures and post-seizure periods.

This study identifies chirp patterns in ictal discharges as a potential biomarker for tracking seizure progression in a mouse epilepsy model.

This paper develops a 1-D CNN for decoding DBS-induced neural activity to predict EMG signals, achieving high accuracy compared to polynomial and LSTM methods.

This paper proposes a model-based closed-loop DBS controller for essential tremor using EMG signals, showing it finds DBS frequencies that align with clinical studies.

The paper investigates the network mechanisms underlying thalamic deep brain stimulation and identifies key features of spiking activity in response to high-frequency stimulation.

The paper introduces a model to infer cognitive states during conflict processing in a Stroop task using neural and behavioral data from Parkinson's patients.

This paper introduces an SVD-based adaptive filtering method to remove both fast and slow artifacts from local field potentials during DBS, outperforming existing techniques on synthetic and real data.

The paper proposes a data processing pipeline that generates graph representations of iEEG data and evaluates their effectiveness for seizure detection using graph neural networks.

This paper introduces an adaptive unscented Kalman filter for neuronal state and parameter estimation, showing improved accuracy and robustness compared to standard variants.

The paper develops a reduced OLM cell model to identify biophysical current balances enabling theta frequency spiking resonance in vivo.

This paper implements a synaptic plasticity model on SpiNNaker neuromorphic hardware to study its role in deep brain stimulation effects.

The paper develops a mathematical model to quantify neuronal firing rate dynamics in basal ganglia and thalamus during DBS across different frequencies, showing consistent parameter fitting with both synthetic and experimental data.

The paper develops a dual optimization algorithm to infer short-term synaptic plasticity dynamics in response to deep brain stimulation using a model-based approach and empirical data.

The paper develops a computational framework to predict site-specific and frequency-dependent effects of deep brain stimulation on neuronal activity using experimental and theoretical insights.

This paper investigates how asynchronous spikes can be reliably represented in a single-layered feedforward network by analyzing the effects of network size and background noise on synaptic weight optimization.

This paper estimates excitatory and inhibitory synaptic conductances from spike trains using a recursive Bayesian approach with Kalman filtering and EM, validated on simulated data.