François Baccelli is Simons Math+X Chair in Mathematics and ECE at UT Austin.  His research directions are at the interface between Applied Mathematics (probability theory, stochastic geometry, dynamical systems) and Communications (network science, information theory, wireless networks). He is co-author of research monographs on point processes and queues (with P. Brémaud); max plus algebras and network dynamics (with G. Cohen, G. Olsder and J.P. Quadrat); stationary queuing networks (with P. Brémaud); stochastic geometry and wireless networks (with B. Blaszczyszyn).

Liberty Hamilton is an Assistant Professor at UT, jointly appointed by the Department of Neurology and the Department of Communication Sciences and Disorders. The goal of her lab is to investigate how the human brain processes speech and other natural sounds, and how sound representations change during development and as a result of learning and plasticity. Her research incorporates multi-site in vivo electrophysiological recordings in patients with epilepsy with computational modeling analyses to address how low-level features of sounds are transformed into meaningful words and sentences. Modeling and computational techniques include linearized models and neural network models of simultaneously recorded local field potential data, unsupervised learning of neural population response structure, and software development for topics relevant to electrocorticography (e.g. electrode localization from CT and MRI scans). Liberty Hamilton received her PhD from the University of California at Berkeley under Dr. Shaowen Bao, where she combined optogenetics and computational models to describe functional interactions within and across layers of the auditory cortex. As an NRSA-funded postdoctoral fellow at the University of California, San Francisco, she worked with Dr. Edward Chang to study speech perception using intracranial recordings in adults. She is a co-director of the NeuroComm laboratory within the Department of Communication Sciences and Disorders.

The goal of Risto's lab is to understand how cognitive abilities, such as sentence and story processing, lexicon, episodic memory, pattern and object recognition, and sequential decision making, emerge through evolution and learning.  The research involves developing new methods for self-organization and evolution of neural networks, as well as verifying them experimentally on human subjects, often in collaboration with experimentalists and medical professionals.  Examples of current work include understanding and inferring the semantics of words and sentences in fMRI images, impaired story telling in schizophrenia, rehabilitation in bilingual aphasia, and evolution of communication in simulated agents.

Alex Huk's research focuses on visual motion, using it as a model system for investigating how the brain integrates information over space and time. His lab employs a variety of methods, including single-unit and multi-unit electrophysiology, causal perturbations of neural activity, psychophysics, and computational modeling. Recent work has focused on applications of generalized linear models (GLMs) and other single-trial amenable analytic frameworks to dissect the multitude of sensory, cognitive, and motor factors that drive many of the brain areas often studied in primates. Ongoing projects seek to extend applications of these tools to large-scale neurophysiological recordings, as well as more mechanistic studies of individual neurons and small circuits.

Research in my laboratory is primarily directed towards understanding the cellular and molecular mechanisms of synaptic integration and long-term plasticity of neurons in the medial temporal lobe. We have focused our attention on the hippocampus and prefrontal cortex, areas of the brain that play important roles in learning, memory and decision-making. Our research uses quantitative electrophysiological, optical-imaging, and computer-modeling techniques.  Most of our projects involve trying to understand how dendritic ion channels, and in particular dendritic channelopathies, impact neuronal and network computations in normal and diseased brain.

Kristen Harris' laboratory studies structural synaptic plasticity in the developing and mature nervous system. Her group has been among the first to develop computer-assisted approaches to analyze synapses in three dimensions through serial section electron microscopy (3DEM) under a variety of experimental and natural conditions. These techniques have led to new understanding of synaptic structure under normal conditions as well as in response to experimental conditions such as long-term potentiation, a cellular mechanism of learning and memory. The body of work includes novel information about how subcellular components are redistributed specifically to those synapses that are undergoing plasticity during learning and memory, brain development, and pathological conditions including epilepsy. Theoretical and computational methods include computational vision for 3D EM reconstruction, high-dimensional spline methods, and molecular simulations of neurotransmitter signaling across the synaptic cleft.

Laura Colgin is an Associate Professor in the Department of Neuroscience at the University of Texas at Austin. She received her PhD from the Institute for Mathematical Behavioral Sciences at the University of California at Irvine, and she completed her postdoctoral training in the laboratory of Nobel Laureates Edvard and May-Britt Moser. Her research uses state-of-the-art multisite recording and multivariate analysis techniques to address several key questions in systems neuroscience, including how the hippocampus stores and retrieves memories and how neuronal computations in the entorhinal-hippocampal network create the spatial component of these memories.

We study information processing and learning in the cerebellum.  Our main experimental approach involves the use of eyelid conditioning as a way to control cerebellar inputs and monitor cerebellar output in vivo.  Through behavioral analysis, in vivo recordings and other manipulations such as stimulation and inactivation we try to understand what the cerebellum computes and the mechanisms that implement these computations.  We augment these studies with computational approaches that include large-scale computer simulations and mathematical models.  The large-scale simulations have been under development for over 25 years.  They involve building conductance-based spiking representations of each cerebellar cell type, developing algorithms to interconnect these neurons in ways that represent cerebellar synaptic organization, and testing them using inputs derived from our empirical studies.  Current versions involve over one million neurons implemented on GPU-based workstations.  These simulations, along with simpler mathematical models when useful, allow us to generate new, empirically testable predictions, to understand our data better and to determine the computational principles that make up cerebellar function.  Big questions include how inputs are transformed to improve learning and to implement stimulus-temporal coding required for the well-timed learning the cerebellum mediates.  We are also interested in the role of feedback in neural system function and in neural/system adaptations that make learning more efficient and that improve performance in the face of noisy inputs.

Nicholas Priebe received his Ph.D. in Physiology from the University of California, San Francisco in 2001 after studying adaptation in motion-selective neurons with Stephen Lisberger. Dr. Priebe was a postdoctoral fellow with David Ferster at Northwestern University, investigating the mechanisms underlying neronal responses in primary vusual cortex. The massive expansion of cerebral cortex is a hallmark of the human brain. We know that the cortex plays an essential role in our perceptions and actions. Sensory inputs from the periphery are transformed in the cortex, allowing us to generate appropriate motor outputs. Dr. Priebe's lab studies the cortical circuitry and the computations that underlie such transformations, using vision as a model system. In visual cortex, neuronal circuitry performs the computations that extract motion, orientation and depth information about the visual environment from subcortical inputs. For example, primary visual cortex (V1) is the cortical location in which information from the two eyes is first integrated, ultimately allowing us to perceive depth in our visual field. By understanding the circuitry that underlies these kinds of computations, we gain insight into similar computations that occur throughout cortex.