KongFatt Wong-Lin

Cognitive Neuroscience · 2006

A researcher in the Science corpus: 1 paper; trusts 20 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

Whom KongFatt Wong-Lin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Nicolas J.-B. Brunel
  2. Michael N. Shadlen
  3. J. Roitman
  4. Xiao-Jing Wang
  5. Marius Usher
  6. Philip L. Smith
  7. Nicolas Fourcaud-Trocmé
  8. John Palmer
  9. Christian K. Machens
  10. Albert Compte

Who trusts KongFatt Wong-Lin overall

  1. Xiao-Jing Wang
  2. Michael Breakspear
  3. Stanislas Dehaene
  4. Roger Ratcliff
  5. Birte U. Forstmann
  6. Gustavo Deco
  7. Arbora Resulaj
  8. H. Francis Song
  9. Enrique C. A. Hansen
  10. Terrence R. Stanford

Papers from KongFatt Wong-Lin's perspective

Ordered by how strongly the researchers KongFatt Wong-Lin trusts endorse each paper. Same corpus, a different order for every researcher.

  1. The time course of perceptual choice: the leaky, competing accumulator model.
  2. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. (Journal of Neurophysiology)
  3. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. (Cerebral Cortex)
  4. Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition (Journal of Computational Neuroscience)
  5. Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task (Journal of Neuroscience)
  6. Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons
  7. Modeling brain function: the world of attractor neural networks, 1st Edition
  8. A role for neural integrators in perceptual decision making. (Cerebral Cortex)
  9. Dynamics of a recurrent network of spiking neurons before and following learning
  10. Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons (Neural Computation)

Their papers