Stanford University · NeuroscienceThe Adaptive Circuits Lab
Jennifer L Raymond · Stanford, California
Research Focus
Dr. Jennifer L. Raymond's lab at Stanford University focuses on understanding how learning influences brain function, particularly in the context of sensory processing and motor control. The lab investigates how the brain's neural circuits adapt their signal processing capabilities through experience, aiming to understand fundamental processes like synaptic plasticity that can lead to improved recovery from neural disorders. By combining experimental techniques with computational models, the lab seeks to reveal insights that could innovate clinical strategies for balancing and eye movement disorders.
synaptic plasticitymotor learningneural circuitsvestibular systemeye movement controlmeta-learningsignal processingbehavioral neuroscienceadaptive learningneurotechnology
Research Aims
5AIM 01Investigate how timing rules of synaptic plasticity can be learned and adapted by neural circuits.
AIM 02Explore the neural mechanisms by which the brain integrates vestibular and visual signals for eye movement control.
AIM 03Analyze how learning alters neural circuit computations and influences motor control behavior.
AIM 04Develop methods to characterize signal transformations in neural circuitry related to eye movements.
AIM 05Examine the implications of adaptable synaptic mechanisms for clinical interventions in neurological disorders.
Skills the Lab Values
experimental designelectrophysiologybehavioral assaysmolecular biology techniquesdata analysis with statistical modelscomputer modelingin vivo experimentationimaging techniquesRNA sequencingneurophysiology
Output against the department
Publications95bottom 31% of dept.460th of 671
this lab
most publishedleast published
Citations4.6kbottom 34% of dept.446th of 671
this lab
most citedleast cited
Career totals from OpenAlex, compared against labs that share a department. A department spans everything from one paper to tens of thousands, so bar heights are on a log scale — otherwise the largest lab would flatten every other bar to a line.