Lai-Sang Young

According to our database, Lai-Sang Young authored at least 13 papers between 2009 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

A data-driven biology-based network model reproduces C. elegans premotor neural dynamics.

Megan Morrison, Lai-Sang Young

PLoS Comput. Biol., 2025

2022

The Use of Reduced Models to Generate Irregular, Broad-Band Signals That Resemble Brain Rhythms.

Benjamin Ambrosio, Lai-Sang Young

Frontiers Comput. Neurosci., 2022

2021

A data-informed mean-field approach to mapping of cortical parameter landscapes.

Zhuo-Cheng Xiao, Kevin K. Lin, Lai-Sang Young

PLoS Comput. Biol., 2021

Unraveling the mechanisms of surround suppression in early visual processing.

Yao Li, Lai-Sang Young

PLoS Comput. Biol., 2021

Malleability of gamma rhythms enhances population-level correlations.

Sonica Saraf, Lai-Sang Young

J. Comput. Neurosci., 2021

2019

A case study in the functional consequences of scaling the sizes of realistic cortical models.

Madhura R. Joglekar, Logan Chariker, Robert Shapley, Lai-Sang Young

PLoS Comput. Biol., 2019

2016

Dynamic Signal Tracking in a Simple V1 Spiking Model.

Guillaume Lajoie, Lai-Sang Young

Neural Comput., 2016

2015

Emergent spike patterns in neuronal populations.

Logan Chariker, Lai-Sang Young

J. Comput. Neurosci., 2015

2013

Emergent dynamics in a model of visual cortex.

Aaditya V. Rangan, Lai-Sang Young

J. Comput. Neurosci., 2013

Dynamics of spiking neurons: between homogeneity and synchrony.

Aaditya V. Rangan, Lai-Sang Young

J. Comput. Neurosci., 2013

Modeling temporal networks using random itineraries

Alain Barrat, Bastien Fernandez, Kevin K. Lin, Lai-Sang Young

CoRR, 2013

2009

Reliability of Coupled Oscillators.

Kevin K. Lin, Eric Shea-Brown, Lai-Sang Young

J. Nonlinear Sci., 2009

Spike-time reliability of layered neural oscillator networks.

Kevin K. Lin, Eric Shea-Brown, Lai-Sang Young

J. Comput. Neurosci., 2009