URoL:EN: Learning the Rules of Neuronal Learning

This project will explore the rules of life of neuronal networks in an engineered microenvironment. The networks of neurons in biological brains routinely solve challenging problems in pattern recognition and classification more efficiently and with better performance than man-made computers. This has inspired algorithms and engineering models of computing behind the sophisticated voice recognition and computer vision available today, yet there has been limited progress towards understanding the physical mechanisms within a living neuron and its neighbors underlying its remarkable capabilities.

This project brings together recent technological advances in patterning, recording, stimulation, and genetic manipulation of neurons to study how to nurture a healthy culture of neurons while continuously observing and stimulating them at a scale fine enough to uncover how the individual parts of a single neuron contribute to the overall learning and computation of the network. The goal is a proof-of-concept demonstration of a programmable computation by neurons in a dish.

If successful, this will have significant implications for both the scientific understanding of natural neuronal computation and would introduce a completely new set of engineering tools for interacting with living neurons and exploring what is computationally possible. New understanding from the neuronal rules of life may impact artificial intelligence, robotics, and neural prosthesis.

This project will seek to engage the public with new insights and results while providing the software and datasets as a scientific resource to allow other investigators to verify and extend the results.

Neuronal computation is distributed and dynamic in nature. This project will investigate the rules of life of neuronal networks at an unprecedented spatial and temporal scale within an engineered microenvironment by extending existing technologies to demonstrate trainable spatiotemporal pattern recognition by small networks of cultured neurons. To accomplish this, a multidisciplinary team of engineers and biologists will adapt emerging techniques for culturing neurons in patterned microenvironments and for recording/stimulating neurons using high density microelectrode arrays and optogenetics.

The primary scientific hypothesis is that a single neuron, when presented with a spatiotemporal pattern of input spikes distributed on its dendritic tree, can be trained to respond highly selectively to one pattern out of many distractor stimuli. This research will be organized along five distinct thrusts: 1) development of a stable, non-bursting, neuron/glia co-culture that receives persistent background stimulation to avoid sensory deprivation, 2) identification and localization of network synaptic connectivity between cells and the modification of synaptic strength based on spike-timing-dependent plasticity, 3) characterization of computational primitives observed in the dendritic tree, 4) development of training protocols for single-neuron, selective learning of spatiotemporal patterns in simulation, and 5) training of a live, cultured neuron to selectively respond to a target spatiotemporal pattern.

The experiments will make use of high-density microelectrode arrays and a high-resolution fluorescence microscope with the capability to project patterned light for optogenetic stimulation of cells. This research will establish fundamental and practical understanding of how the neurons individually and collectively respond to their dynamic environment, develop predictive models of the response, and develop control laws to tune the network for specific spatiotemporal pattern recognition tasks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.