Learning to control the brain through adaptive closed-loop patterned stimulation
| Autor: | Katherine C Letai, Zongda Che, Camden J. MacDowell, Cynthia R. Steinhardt, Sina Tafazoli, Timothy J. Buschman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Visual perception
Computer science 0206 medical engineering Population Biomedical Engineering Stimulation 02 engineering and technology Stimulus (physiology) 03 medical and health sciences Cellular and Molecular Neuroscience Mice 0302 clinical medicine medicine Animals Learning Computer Simulation education Neurons education.field_of_study Brain 020601 biomedical engineering Electric Stimulation Electrophysiology Visual cortex medicine.anatomical_structure Brain stimulation Neuroscience Closed loop 030217 neurology & neurosurgery |
| Zdroj: | Journal of neural engineering. 17(5) |
| ISSN: | 1741-2552 |
| Popis: | Objective Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. Approach The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5-15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4-16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. Main results In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Significance Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity. |
| Databáze: | OpenAIRE |
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