Title:
Sensory input encoding and readout methods for in vitro living neuronal networks
Sensory input encoding and readout methods for in vitro living neuronal networks
| dc.contributor.advisor | Rozell, Christopher J. | |
| dc.contributor.advisor | Potter, Steve M. | |
| dc.contributor.author | Ortman, Robert L. | en_US |
| dc.contributor.committeeMember | Butera, Robert | |
| dc.contributor.committeeMember | Venayagamoorthy, Kumar | |
| dc.contributor.department | Electrical and Computer Engineering | en_US |
| dc.date.accessioned | 2012-09-20T18:22:17Z | |
| dc.date.available | 2012-09-20T18:22:17Z | |
| dc.date.issued | 2012-07-06 | en_US |
| dc.description.abstract | Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm. | en_US |
| dc.description.degree | MS | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/44856 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Cognitive optimization and prediction | en_US |
| dc.subject | Support vector machine | en_US |
| dc.subject | Microelectrode arrays | en_US |
| dc.subject | Cultured cortical networks | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Separation property | en_US |
| dc.subject | Information theory | en_US |
| dc.subject | Neural coding | en_US |
| dc.subject | Communication | en_US |
| dc.subject | Power system control | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Living neuronal networks | en_US |
| dc.subject | Sensory coding | en_US |
| dc.subject | Liquid state machine | en_US |
| dc.subject | Hybrid neural microsystem | en_US |
| dc.subject | Biologically inspired artificial neural network | en_US |
| dc.subject.lcsh | Neural networks (Neurobiology) | |
| dc.subject.lcsh | Neurons | |
| dc.subject.lcsh | Algorithms | |
| dc.subject.lcsh | Sensor networks | |
| dc.title | Sensory input encoding and readout methods for in vitro living neuronal networks | en_US |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Rozell, Christopher J. | |
| local.contributor.advisor | Potter, Steve M. | |
| local.contributor.corporatename | School of Electrical and Computer Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAdvisorOfPublication | 301419ce-552b-4510-b374-2eecfed3f42a | |
| relation.isAdvisorOfPublication | 419ab98e-ec40-4523-8d84-ffd9f63bd432 | |
| relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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