Realtime Neural Engineering.

Our lab designs new ways of understanding and interacting with complex neural circuits in vivo. Our approaches enable new ways to explore how information is processed, stored, and retrieved in both healthy brains and in models of human neurological diseases and disorders. The experimental neurobiological topics we focus most on are understanding memory - how knowledge is integrated and stored, including during periods of offline processing. Our forte is the use of closed-loop signal processing to demonstrate the causal signficance of specific patterns of ensemble activity. We also develop new algorithms to make sense of large amounts of data, and new neural interface technologies, systems and software that are shared as open source.

Realtime neural signal processing icon

Jadhav_et_al Science 2012

Interacting with Memory

Forming memories involves converting the patterns of activity which occur during experience into stable representations in distributed circuits in the brain. Brief bursts of activity during quiescence and sleep within a brain region called the hippocampus coordinate the process of storing and recalling memories. To understand these critical phenomena, we are developing systems that translate ongoing neural activity into information and use this to manipulate related circuits in real-time. By demonstrating a causal understanding of memory, we hope to enable therapies that might, for example, allow us to selectively inhibit the recall or long-term storage of traumatic episodes. More information.

Understanding the brain with machine learning

Cutting edge systems neuroscience involves simultaneous observation of ensembles of hundreds of individual neurons. The promise of these observations is that they enable us to understand the internal representatiosn encoded within neural circuits. This is particularly critical during periods like sleep in which animals are not behaving, and there are not external variables to correlate signals with. We are developing new approaches for observing and understanding the latent variables that underly learning, memory, decision making, and other critical cognitive functions. Recent papers using Statistical Decoding, Gaussian Processes Latent Variable Models and Hidden Markov Models

Gaussian Process Manifolds

Open Source Tools

Open Source Tools.

We are developing a number of new technologies, open source software and embedded systems tools for neuroscience experiments including spectral data analysis, imaging, virtual reality, and deep brain stimulation. We are eager to share! More information about tools for neuroscience and neural engineering.

We are grateful for the organizations that support our work!