Ralf Wessel

Ralf Wessel

Professor of Physics
PHD, UNIVERSITY OF CAMBRIDGE
MS, TECHNICAL UNIVERSITY MUNICH
research interests:
  • Neurophysics
  • NeuroAI
  • Computational Aesthetics

contact info:

mailing address:

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

What are the principles of neural computation in artificial and biological intelligence? To address this question, Prof. Wessel pursues three interrelated research pillars.

Structure and Principles in Neural Population Activity: Enabled by recent breakthroughs in neurotechnology, Wessel’s group obtains recordings from vast neural populations of animals within the context of a sensory environment or task. Given the large number of interconnected noisy neurons in the brain, neural activity is high-dimensional and variable. They employ mathematical tools to seek out structure and underlying principles from these complex data sets. The resulting structure, such as manifolds, establishes links between neural population activity and information coding (Xia et al 2021; Ye & Wessel 2024). The resulting principles, such as criticality, informs about the underlying self-organized dynamics of the neural circuits (Xu et al 2024).

Synergy between AI and Brains: The convergence of Machine Learning and Artificial Intelligence and Brain Sciences is creating a unique opportunity for transformational research into the understanding of how intelligence works. Self-organized Deep Neural Networks now reach human-level performance in certain tasks. Importantly, these networks display characteristics of brain function that have not been captured with traditional models. Wessel’s group employ Deep Neural Networks as a novel framework that enables them to investigate emergent coding principles in brains (Ye et al 2024; see podcast). 

Computational Aesthetics of Mosaics: Wessel investigates the aesthetics of contemporary mosaic art. The project utilizes computational neuroscience and AI to explore the brain's processing of mosaics, examining how features like color, texture, and pattern affect aesthetic appreciation. The research proposes to develop AI models that predict cognitive effects of artistic features in mosaics and to create personalized mosaic experiences tailored to individual preferences. The overall goal is to advance our understanding of the subjective aesthetic experience of mosaic art, while simultaneously enhancing the creative process of making mosaics through AI-driven tools (see podcast).
 

Awards

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

            Xu Y, Schneider A, Wessel R, Hengen KB (2024) Sleep restores an optimal computational regime in cortical networks. Nature Neuroscience, 27: 328–338.

            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.