Synthetic Neurobiology

Neural stimulation hardware has traditionally been either electrical or magnetic in nature. Our lab has recently developed optogenetic molecular methods for making neurons able to be activated or silenced by multiple colors of light. We are engineering optical hardware systems for targetedly stimulating and inactivating neurons precisely, from one to many at a time, with complex spatiotemporal patterns, even in dense tissue in the living brain. Our goal is to find ways to cure intractable psychiatric and neurological disorders.

Funk2 is a novel process-description language that keeps track of everything that it does. Remembering these causal execution traces allows parallel threads to reflect, recognize, and react to the history and status of other threads. Novel forms of complex, adaptive, nonlinear control algorithms can be written in the Funk2 programming language. Currently, Funk2 is implemented to take advantage of distributed grid processors consisting of a heterogeneous network of computers, so that hundreds of thousands of parallel threads can be run concurrently, each using many gigabytes of memory. Funk2 is inspired by Marvin Minsky's Critic-Selector theory of human cognitive reflection.

Devices to facilitate gene therapy will be of increasing importance in years to come. We are developing fluidic systems to facilitate viral delivery in complex tissues.

How do high-level cognitive functions emerge from primitive neural computations to mediate complex human behavior? We are developing precise, focal ways of investigating phenomena such as trust and risk-taking, in order to understand how they play roles in purchasing, decision-making, social interaction, and other real-world scenarios.

Mental health therapies are complex and, to be computer-deliverable, must be customizable and adaptive. We are applying software engineering principles to automate, and make customizable and adapatable, such therapies via a Web-based application. The technology also provides a new platform for studying the cognitive process and neural circuitry of therapy to further non-pharmacological methods of health interventions and management.

We have engineered molecular sensitizers that make genetically specified neurons that can be activated by pulses of blue light, and silenced by pulses of yellow light. This revolutionary technology enables us to reprogram neural networks at the millisecond timescale, opening up the systematic analysis and engineering of the brain, as well as completely novel methods of therapy. We are now developing new and improved molecules and pursuing pre-clinical translational testing.

Moral Compass is a model of how children learn in a problem-solving environment where the child is learning to accomplish goals in the context of parents, strangers, and cultural knowledge. The child learns in multiple ways: playing alone, being told stories, and being rewarded or punished. Our model aims to provide an explanation for relatively complex reflective states of mind, such as desire, avoidance, focus, Ignorance, and personality traits. Our model also emphasizes different types of failure in its reflective approach to learning, including: surprise, disappointment, and guilt. Possible applications include better understanding of the mental health of cognition in social domains.

Despite use in treating depression, and promise in treating stroke, Parkinson's, tinnitus, and other disorders, noninvasive brain stimulation technology is bulky, power-hungry, non-focal, and requires precision alignment with neural structures. We are applying modern engineering techniques to create a portable, focal, noninvasive brain stimulator that will enable a new platform for therapeutic neuromodulation.

This top-secret project is aimed at improving human cognition and happiness, by empowering people to control their lives.

Neurological and psychiatric disorders afflict over one billion people worldwide, presenting annual costs exceeding $1 trillion. What are the principles of controlling neural circuits, in order to improve their functions and overcome intractable neurological and psychiatric disorders? We have invented cell-type-specific optical neural control technologies, and with them we are seeking to parse out the methods with which to fix activity in aberrant neural circuits, correcting the computational dynamics within, in order to discover new principles of treating neural disease.

Complex data—such as neurophysiological recordings, or measures of human behavior, Internet, and general network data—are extremely difficult to analyze because of the dynamic nature of the high-dimensional set of interacting processes that generates the data. Accordingly, traditional statistical and data analysis methods—clustering, correlation, and so forth—can rarely create models sophisticated enough to explain the data, without fitting noise, demanding astronomically sized datasets, or requiring enormous amounts of hand-tuning by insightful labor. We propose to design and develop a system that continuously generates novel data-modeling hypotheses and evaluates them in real time, testing models of ever-increasing complexity on data as it comes in.