Skip to main content
Article thumbnail
Location of Repository

Discrimination and control in stochastic neuron models

By Ph.D. Jing Kang


Major topics of great interest in neuroscience involve understanding the\ud brain function in stimuli coding, perceptive discrimination, and movement\ud control through neuronal activities. Many researchers are designing biophysical\ud and psychological experiments to study the activities of neurons in the\ud presence of various stimuli. People have also been trying to link the neural responses\ud to human perceptual and behavioral level. In addition, mathematical\ud models and neural networks have been developed to investigate how neurons\ud respond and communicate with each other.\ud In this thesis, my aim is to understand how the central nervous system performs\ud discrimination tasks and achieves precise control of movement, using\ud noisy neural signals. I have studied, both through experimental and modelling\ud approaches, how neurons respond to external stimuli. I worked in three aspects\ud in details. The first is the neuronal coding mechanism of input stimuli\ud with different temporal frequencies. Intracellular recordings of single neurons\ud were performed with patch-clamp techniques to study the neural activities\ud in rats somatosensory cortices in vitro, and the simplest possible neural model—integrate-and-fire model—was used to simulate the observations.\ud The results obtained from the simulation were very consistent with that in the\ud experiments. Another focus of this work is the link between the psychophysical\ud response and its simultaneous neural discharges. I derived that under a\ud widely accepted psychophysical law (Weber’s law), the neural activities were\ud less variable than a Poisson process (which is often used to describe the neuron\ud spiking process). My work shows how psychophysical behaviour reflects\ud intrinsic neural activities quantitatively. Finally, the focus is on the control\ud of movements by neural signals. A generalized approach to solve optimal\ud movement control problems is proposed in my work, where pulses are used\ud as neural signals to achieve a precise control. The simulation results clearly\ud illustrate the advantage of this generalized control.\ud In this thesis, I have raised novel, insightful yet simple approaches to study\ud and explain the underlying mechanism behind the complexity of neural system,\ud from three examples on sensory discrimination and neural movement\ud control

Topics: RC0321, QL
OAI identifier:

Suggested articles


  1. (2007). Computational principles of sensorimotor control that minimize uncertainty and variability. doi
  2. (1996). Long-term depression in hippocampus. Annual Review of Neuroscience 19:437-462. BrittenKH,ShadlenMN,NewsomeWT,MovshonJA(1992)TheAnalysisof Visual-Motion - a Comparison of Neuronal and Psychophysical Performance.
  3. (1932). The mechanism of nervous action; electrical studies of the neurone. doi
  4. (1972). Visual pattern analysis in machines and animals. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.