Principal Investigator
Bradley Voytek
(scholar | github | linkedin | cv)

I’m a Professor of the Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program here at UC San Diego. I'm interested in what "codes" the 86 billion or so neurons in our brains can possibly use to "talk" to one another. Our brains are very noisy and messy and are embedded in very noisy, messy biological bodies, so this is a very difficult problem! To address this my lab combines many different approaches, including neural simulation, invasive human brain recordings, autonomic nervous system analyses, large-scale data science, and machine learning. We do all of this to understand how brains give rise to complex human cognition, and how cognitive processes can be disrupted with aging and in disease. I am both an Alfred P. Sloan Neuroscience Research Fellow and a Kavli Fellow of the National Academies of Sciences, as well as a founding faculty member of the UC San Diego Halıcıoğlu Data Science Institute and the Undergraduate Data Science program. In 2010, I got my PhD in neuroscience from UC Berkeley and then worked as a postdoctoral fellow at UC San Francisco. In between, I was the first Data Scientist at Uber where I helped build their data science strategy and team. I’m an open science and honest science advocate. I’m also kind of known as the “zombie brain” guy, along with friend and fellow neuroscientist Timothy Verstynen. We even published a book on this: Do Zombies Dream of Undead Sheep?.

PhD Student
Sydney Smith
(email | twitter | scholar | github)

Back in 2018 when I first joined the Voytek Lab as a lab manager, I was curious about what computational neuroscience could tell me about the brain. Now, as a PhD student in the Neurosciences Graduate Program, I’ve become obsessed with discovering how best to study electrophysiological signals and what computational methods can reveal about human cognition. Specifically, I’m interested in slow frequency oscillations. What neurophysiological processes produce them? When and where do they occur in the brain? How do we decide what qualifies as a “slow oscillation” compared to non-rhythmic activity? What are the implications for neuroscience research when they are measured improperly? During my PhD, I’m attempting to answer some of these questions by recording data from patients with depression and epilepsy to try to better understand the nature of slow oscillations in the brain and behavior.

PhD Student
Quirine van Engen
(email | cv | github)

A brain trying to understand itself is the funniest idea about neuro/cognitive science. After studying the brain during my Bachelors Psychobiology and Masters Brain and Cognitive Sciences (both at the University of Amsterdam), I totally got hooked on the most interesting organ we possess that controls all our automatic responses, as well as our thoughts. Although I will miss my home town, I am excited to continue my passion for research here in the lab. During my latest research project (here at the lab), I investigated both oscillatory theta and alpha activity, and the aperiodic spectrum changes during a working memory task. Broadly speaking, my main research interest is how the brain processes information. More specifically, I don't know yet, because everything is super interesting.

PhD Student
Eena Kosik
(email | website | twitter | github)

To truly understand the brain and cognition, we must remember that the brain is intricately connected to the rest of the body. I am fascinated by these peripheral and central nervous system interactions that exist between neural activity and other physiological signals (such as breathing, and cardiac rhythms), as well as how they relate to cognitive function. How can we better measure and parameterize these rhythms? How might these parameters relate to one another? Can we discover any causal drivers of these signals? How does manipulation of these signals influence behavior? I am using electrophysiological measures to analyze these rhythms in humans to answer some of these questions.

PhD Student
Andrew Bender

I am broadly interested in the computational mechanisms and information processing underlying cognitive processes such as decision making and working memory. I pursue this interest through use of computational modeling and machine learning techniques in tandem with analysis of electrophysiological recordings.

PhD Student
Michael (MJ) Preston
(email | github | linkedin | scholar)

I am a PhD student in the Neurosciences Graduate Program. I am interested in neural mechanisms of information processing and how these processes are reflected in the electrical signals we record from the brain. My previous research experiences have focussed on the role of neural oscillations in brain circuit function, promoting open-science through The OpenBehavior Project, and modelling decision-making.

PhD Student
Blanca Martin-Burgos

I am a Neurosciences PhD student co-mentored by both Dr. Brad Voytek and Dr. Alysson Muotri. I am working on recording activity in cortical organoids and am interested in how these signals change with development, aging, and disease. A broad range of computational techniques excite me, including time series analysis, graph theory and machine learning. Some of these techniques could be tailored to leverage the unique advantages of organoids. To this end, I am working on developing analysis tools to study network dynamics in organoids. In my previous research, I studied circadian rhythms with Dr. Mary Harrington at Smith College. More specifically, we developed an in vivo non-invasive technique to record circadian gene expression in freely moving mice and computational methods for time series analysis of the data obtained with our new technique. Thinking about rhythms and fluctuations at the circadian level got me excited about neural dynamics (periodic and aperiodic!) and Voytek Lab’s unique approach to exploring them.

PhD Student
Trevor McPherson

Sensory processing in the brain can be framed as a hierarchical process, where information is integrated across scales to support increasingly abstract representations of the external world. Hierarchy can be spatial (multiple brain regions interacting), temporal (computation unfolds across multiple timescales), or reflect the structure neural code itself (co-activation of spiking activity, relationships between periodic or aperiodic dynamcis). I am interested in understanding how the brain might leverage hierarchy to efficiently represent sensory information and support behavior. As a Neurosciences PhD student co-mentored by both Dr. Brad Voytek and Dr. Tim Gentner, I use the European starling as an animal model to study the auditory sensory hierarchy. These songbirds naturally produce and rely on the perception of complex vocal sequences, making them ideal for the study of how acoustic information is neurally represented and distributed throughout the brain. Through a combination of behavioral, acute, and chronic experiments, I am examining how extracellularly recorded neuronal population dynamics across multiple regions and timescales capture the statistics of natural birdsong sequences. Quantifying various notions of hierarchy requires a diverse computational toolkit, and I am interested in analytical approaches drawn from Fourier analysis, information theory, dynamical systems, algebraic topology, and machine learning. Previously I studied the effect of brain stimulation on neural activity in the context of cognition and disease with Dr. Flavio Fröhlich at the University of North Carolina at Chapel Hill. My time in the Frohlich lab stressed the importance of integrating analyses across scales and peaked my interest in rhythmic neuronal processing. I’m excited to continue exploring periodic and aperiodic neuronal dynamics and their implications for hierarchical sensory processing with the Voytek Lab!

PhD Student
Ryan Hammonds
(email | github)

I am currently a Data Science PhD student (previously a software developer) in the lab. My work has focused on developing software related to models and analyses of neural time series and power spectra. Recent projects include quantifying neural timescales and the development of a package for managing analysis workflows related to neural signal processing and modeling. I plan to integrate predictive machine learning models/algorithms with neural data.

PhD Student
Dillan Cellier
(email | github)

I am a current 3rd year Cognitive Science PhD student. My primary interest in the Voytek lab is to understand how the timescales of neurons (for example, as measured via the knee of the aperiodic signal) relate to the timescales of higher-order cognition, such as that involved in abstract representations, stimulus processing, or tasks that involve many rules. My previous work entailed using EEG, fMRI and TMS to probe the causal role of oscillations in cognitive control with Dr. Justin Riddle when we were both members of Mark D'Esposito's lab at UC Berkeley. From there I expanded upon this work with Prof. Kai Hwang at UIowa, where we studied attention and task hierarchies using EEG, as well as characterizing the aperiodic signal as it changes in development. At UCSD I have also worked with Prof. Anastasia Kiyonaga on questions related to working memory and distraction.

PhD Student
Morgan Fitzgerald
(email | github)

As a neuroscience PhD student who previously worked with Dr. Emily Jacobs at UC Santa Barbara, I am excited to join the Voytek lab and look at neural oscillations and cognition. Building upon my previous work, I am particularly interested in incorporating sex differences into the lab's exploration of brain function and behavior. This integration of sex differences research will enhance our understanding of how neural oscillations contribute to the complex and often sex-specific workings of the brain, ultimately providing insights into disorders that may have sex differences in prevalence and presentation.

PhD Student
Liz Kaplan
(email | github)

I am a first year Cognitive Science PhD student. Previously, I worked with Dr. Stephanie Jones at Brown University investigating the role of alpha rhythms in modulating visual evoked responses through computational modeling in The Human Neocortical Neurosolver (HNN). I also used HNN to model the effects of TMS on the somatosensory cortex and tactile perception. I am excited to begin my work in the Voytek Lab, continuing to explore how neural oscillations influence various behaviors.

Postdoc
Pamela (Pam) Riviere
(email | github | scholar)

I am interested in exploring how neural circuits adapt their electrical activity patterns in response to changing environmental conditions, behavioral demands, and changes to organisms' internal states (e.g., nutrient availability). Environmental and interoceptive inputs can provoke changes to the neuromodulatory milieu that neural circuits are embedded in. My projects will investigate neuronal responses to neuromodulatory inputs, with a special focus on how neuromodulatory action affects the shape of neuronal action potentials.

Postdoc
Christian Cazares
(email | twitter | github | scholar)

My postdoctoral research aims to identify new electrophysiological and genetic markers for abnormal cortical function and disease severity in a genetic model of neurodevelopmental disorders that can be recapitulated in patient-derived tissue cultures. My long-term research interests are primarily driven by two outstanding questions: 1) What is the functional organization of neuronal communication processes? and 2) How are the underlying neural circuits disrupted in neurodevelopmental disorders? Developing a better understanding of the convergent mechanisms governing abnormal cortical function in vivo and in vitro will provide great value to clinicians and translational researchers wanting to treat cognitive and behavioral impairments found in these patient populations.