Ralf Wessel

Ralf Wessel

Professor of Physics
research interests:
  • Neurophysics
  • Biophysics

contact info:

mailing address:

  • Washington University
    MSC 1105-110-02
    One Brookings Drive
    St. Louis, MO 63130-4899

To date, a convincing computational framework for the processing of visual stimuli in neural circuits remains elusive. To fill this gap, Professor Wessel’s NeuroPhysics group seeks to delineate principles of visual information processing at the level of spatiotemporal network dynamics in optic tectum and visual cortex.

Arguably the biggest goal in modern neuroscience is to gain a deeper and more complete understanding of strongly correlated neural systems, known as microcircuits. A striking phenomenon of strongly correlated neural systems is visual perception. In broad strokes, it is intriguing to hypothesize that visual perception emerges from the interaction between incoming spatiotemporal stimuli and the internal dynamic state of neural networks. 

To understand the emergence of vision in animal brains, Dr. Wessel’s NeuroPhysics group and their collaborators design experiments addressing questions of dynamics and computation in recurrent neural circuits of visual cortex. The ongoing revolution in neurotechnology empowers these experiments, permitting the chronic recordings of neural activity from large numbers of neurons in recurrent cortical circuits of behaving animals. Dr. Wessel’s group processes and interprets the resulting huge data sets using sophisticated data analyses. Complementary model investigations link biophysical properties of neurons, synapses, and neural network architecture, thus providing a mechanistic understanding of dynamics and computation in recurrent neural circuits. The discovered emergent phenomena, including oscillations, correlations, and neuronal avalanches, are conceptualized using a framework informed by statistical physics and nonlinear dynamics. This synergy of advanced neurotechnology and physics-inspired data analysis, model investigations, and theory provides a fertile opportunity to test the stated working hypothesis of emergent vision and to advance our understanding of cortical microcircuit function.

Wessel obtained an MS in Physics from the Technical University Munich in Germany and a doctorate in Physics from the University of Cambridge in England. During his postdoctoral training in neuroscience at UCSD in La Jolla, he was successively a Human Frontier Science Program Fellow and a German Research Council Fellow. In 1997, Wessel was appointed to Research Assistant Professor in the Department of Physics at UCSD. In 2000, he joined the faculty of Washington University in St. Louis, where he now holds the rank of Professor of Physics. He also holds a joint appointment in the Department of Neuroscience at the Washington University School of Medicine.


In 2007, the Graduate Student Senate selected Ralf Wessel to receive Recognition for Excellence in Mentoring as part of the Outstanding Faculty Mentor Awards. These awards were created by graduate students in the Senate to honor faculty members whose commitment to graduate students and excellence in graduate training has made a significant contribution to the success of graduate students in Arts and Sciences at Washington University.

recent courses

Mechanics (Physics 411)

Motion of a point particle, rotational motion, oscillation, gravitation and central forces, Lagrangian and Hamiltonian formulation.

    Physics of the Brain (Physics 350/450)

    Concepts and techniques of physics are applied to study the functioning of neurons and neuronal circuits in the brain. Neurons and neural systems are modeled at two levels: (i) at the physical level, in terms of the electrical and chemical signals that are generated and transmitted and (ii) at the information-processing level, in terms of the computational tasks performed. Specific topics include: neuronal electrophysiology, neural codes, neural plasticity, sensory processing, neural network architectures and learning algorithms, and neural networks as dynamical and statistical systems.

      Physics of Vision (Physics 355)

      How do the eyes capture an image and convert it to neural messages that ultimately result in visual experience? This lecture and demonstration course will cover the physics of how we see. The course is addressed to physics, premedical, and life-sciences students with an interest in biophysics. Topics include physical properties of light, evolution of the eyes, image formation in the eye, image sampling with an array of photoreceptors, transducing light into electrical signals, color coding, retinal organization, computing with nerve cells, compressing the 3-D world into optic nerve signals, inferring the 3-D world from optic nerve signals, biomechanics of eye movement, engineered vision in machines. The functional impact of biophysical mechanisms for visual experience will be illustrated with psychophysical demonstrations.

        Biophysics Laboratory (Physics 360)

        This laboratory course consists of "table-top" experiments in biological physics that are designed to introduce the student to concepts, methods, and biological model systems in biophysics. Most experiments combine experimentation with computer simulations. The list of available experiments includes electrophysiology, human bioelectricity, optical tweezers, ultrasonic imaging, mass spectrometer, and viscosity measurements.

          Topics in Theoretical Biophysics (Physics 453 & 563)

          What can Deep Learning teach us about the Brain? What can Artificial Intelligence in Neural Networks teach us about the functioning of Natural Intelligence in Brains? What can Computational Neuroscience teach us about the functioning of Artificial Intelligence in Neural Networks? Do the mechanisms of Artificial Intelligence in deep networks serve as a framework for the investigation of Natural Intelligence in evolved brains? Deep Neural Networks (DNNs) have revolutionized Machine Learning and Artificial Intelligence. These networks have recently found their way back into computational neuroscience. DNNs reach human-level performance in certain tasks. Importantly, DNNs can display certain characteristics of brain function that cannot be captured with shallow handcrafted models. With this, DNNs offer an intriguing novel framework that may enable computational neuroscientists to address fundamental questions concerning complexity and neural computation in brains. In this course we will evaluate the tantalizing hypothesis that DNNs can serve as a tool to understand aspects of brain function, thus moving closer towards an understanding of both, natural and artificial intelligence. The course will be organized around 15 key papers illustrating major ideas of DNNs and computational neuroscience.

            Selected Publications

            Xia J, Marks TD, Goard MJ, Wessel R (2021) Stable representation of a naturalistic movie emerges from episodic activity with gain variability. Nature Communication, 12: 5170.

            Ma Z, Turrigiano GG, Wessel R, Hengen KB (2019) Cortical circuit dynamics are homeostatically tuned to criticality in vivo. Neuron 104: 655-664.

            Shew WL, Clawson WP, Pobst J, Karimipanah Y, Wright NC, Wessel R (2015). Adaptation to sensory input tunes visual cortex to criticality. Nature Physics 11: 659-663.

            Brandt SF, Dellen BK, Wessel R (2006). Synchronization from disordered driving forces in arrays of coupled oscillators. Phys Rev Letters 96: 034104.

            Luksch H, Khanbabaie R, and Wessel R (2004). Synaptic dynamics mediate sensitivity to motion independent of stimulus details. Nature Neuroscience 7: 380-388.

            Gabbiani F, Metzner W, Wessel R, and Koch C (1996). From stimulus encoding to feature extraction in weakly electric fish. Nature 384: 564-567.