We use mathematics and engineering to understand the brain and to create new machine learning
Our lab's research is focused at the intersection of mathematics, physics, and engineering in order to understand how the brain learns, represents, and processes complex information, what happens when there is a breakdown in signaling in neurological disorders, and how we can use what we learn to develop novel neural derived algorithms that capture the properties of neural computation in order to develop new artificial intelligence architectures. We are also involved in developing nanotechnologies to interface with the brain and neural sensory retina.
The individuals in our lab both present and past include research scientists, visiting scholars, postdocs, graduate students, medical students, and undergraduate students with diverse backgrounds, including electrical engineering, computer science and engineering, bioengineering, mechanical engineering, physics, mathematics, materials science, neuroscience, and chemistry.
Everyone brings their unique backgrounds and perspectives towards our common goal of understanding the brain as a dynamic system. We want to attempt to understand how the brain learns and encodes and manipulates information as it understands, reacts, and thrives in the environment in which it finds itself.
computational neuroscience and biophysics
Mathematical and physical modeling and simulations of neural processes at molecular, cellular, and systems scales, for the purpose of understanding how structures in the brain represent and process information. We have had a particular focus on modeling the calcium signaling dynamics of astrocyte neural glial cells.
Our lab engaged in the development and use of nanotechnology to study and interface with the brain. In particular, we have previously worked on the development of self assembling nanotechnologies for neural regeneration, and the optimization of chemically functionalized quantum dots to achieve high resolution imaging of cellular structure and calcium dynamics.
Distinct from computational modeling and numerical simulations of the biophysics of neurobiology, we have a strong focus on the theoretical analysis of dynamic signaling in networks(i.e. models, theorems, and proofs that drive new kowledge and predictions). We are interested arriving at a deep understanding of neurophysiological dynamics.
An extension of our nanotechnology work is development of nanoengineered neural technologies intended to interface with the brain and restore clinical function. Most recently, in collaboration with Nanovision Biosciences, our lab has been one of several labs involved in the development of a surgically implantable optoelectronic retinal neural prosthesis to restore vision.
neural derived algorithms and AI
We are leveraging our theoretical and computational work to develop neural derived and neuromimetic (i.e. neural imitating) algorithms for non-biological engineered systems. In particular, we are developing a new class of geometric dynamic artificial neural network to create algorithms that will enable cognitive computing systems to achieve creative 'machine thinking'.
applications to neurological disorders
Much of our work is of direct clinical relevance, or is translational and intended to restore clinical function. We have published on traumatic spinal cord injury and reactive gliosis, Alzheimer's disease, retinal prosthesis, and currently have a strong interest in the systems neuroscience of autism spectrum disorder (ASD) and realted neurodevelopmental disorders.
Center for Engineered Natural Intelligence
The goal of the Center is to leverage neurobiological and neurophysiological principles to develop mathematical models and algorithms that push the boundaries of machine learning and artificial intelligence. To accomplish this, the Center brings together faculty and students from Bioengineering, Physics, Neurobiology, Neurosciences, Mathematics, Cognitive Sciences, Psychology, and the Salk Institute.
We are building mathematical models, algorithms and so ware that enable new forms of machine computation, learning, and intelligence, by leveraging the current state of knowledge of neurobiological processes and computation. Informed by experiments and data science.