# Ghost in the Machine: Melding Brain, Computer and Behavior

> **NIH NIH DP1** · UNIVERSITY OF PENNSYLVANIA · 2021 · $1,137,500

## Abstract

Implantable devices are playing a greater role in neurologic care, but their effectiveness is
limited, because they are blind to human thoughts, feelings, and behavior – factors that most
dramatically affect our health. Coupling peripheral sensors to implants might help, but wouldn’t it be
easier if the devices just asked us? Armed with this knowledge, next generation machines will more
effectively drive neural activity in the brain to healthy states. They will also quickly learn behaviors
that worsen health and guide us to better choices. Though DARPA, the NIH, and Neuralink are
spending millions of dollars on new hardware for brain-computer interfaces, none focus on
reciprocal, natural communication between host and machine. There is a desperate need for
novel, practical methods that enable devices to learn from and guide human behavior.
 In this application I propose to develop a new generation of autonomous brain-machine
interfaces – devices that can question, record, act - and combine learning algorithms applied to
neurosignals with teaching by their human hosts. Life with these implants will entail a subtle human-
machine dialogue in which devices and humans teach and learn from each other. Humans will inform
intelligent algorithms about what we are doing and feeling, while machines will incorporate this
information into therapy and guide us to optimize quality of life in personalized ways. This is a
paradigm shift from today’s simple devices, which are programmed by physicians during occasional
office visits. I propose to demonstrate this paradigm in a practical, scalable way using current
epilepsy implants that is rapidly translatable to many neurological disorders.
 To achieve this goal, I will meld several cutting-edge technologies in novel ways, including:
(1) State-of-the-art, high bandwidth implantables that sample neural activity, link to vast cloud-
based computational power to process it, and intervene to modulate brain, spinal cord or peripheral
neural activity. This work utilizes my experience from the past 20 years; (2) I will deploy powerful
new computer science tools in novel ways. I will use convolutional neural nets (a.k.a. Deep
Learning) to learn patterns from vast streams of continuous high-bandwidth neural data, build a
two way human-machine interface using Natural Language Processing (NLP)., and probe
networks with changes in human behavior and electrical stimulation and guide interventions toward
therapeutic goals using Reinforcement Learning. Combining these computer science, machine
learning techniques and measurements of human behavior is a new area of investigation for me
that will leverage my unique background in clinical neurology and engineering to build a new class
of interactive, human therapeutic devices.

## Key facts

- **NIH application ID:** 10267167
- **Project number:** 5DP1NS122038-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Brian Litt
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,137,500
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267167

## Citation

> US National Institutes of Health, RePORTER application 10267167, Ghost in the Machine: Melding Brain, Computer and Behavior (5DP1NS122038-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10267167. Licensed CC0.

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