Real-time Position Reconstruction with Hippocampal Place Cells
Brain-computer interfaces (BCI) are using the EEG (Electroencephalogram), the ECoG (Electrocorticogram) and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be develope...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-06-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00085/full |
| _version_ | 1830405815069048832 |
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| author | Christoph Guger Thomas eGener Cyriel ePennartz Jorge eBrotons-Mas Jorge eBrotons-Mas Guenter Edlinger Sergi eBermúdez I Badia Stefan eSchaffelhofer Paul eVerschure Maria V Sanchez-Vives |
| author_facet | Christoph Guger Thomas eGener Cyriel ePennartz Jorge eBrotons-Mas Jorge eBrotons-Mas Guenter Edlinger Sergi eBermúdez I Badia Stefan eSchaffelhofer Paul eVerschure Maria V Sanchez-Vives |
| author_sort | Christoph Guger |
| collection | DOAJ |
| description | Brain-computer interfaces (BCI) are using the EEG (Electroencephalogram), the ECoG (Electrocorticogram) and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80x80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat’s trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat’s position in real-time.The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4 % using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9 % for 3 rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that place cells were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control. |
| first_indexed | 2024-12-20T17:53:40Z |
| format | Article |
| id | doaj.art-f41ef3b32e744b6e9f8c8f03e43445be |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-20T17:53:40Z |
| publishDate | 2011-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-f41ef3b32e744b6e9f8c8f03e43445be2022-12-21T19:30:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2011-06-01510.3389/fnins.2011.000851446Real-time Position Reconstruction with Hippocampal Place CellsChristoph Guger0Thomas eGener1Cyriel ePennartz2Jorge eBrotons-Mas3Jorge eBrotons-Mas4Guenter Edlinger5Sergi eBermúdez I Badia6Stefan eSchaffelhofer7Paul eVerschure8Maria V Sanchez-Vives9g.tec medical engineering GmbH(IDIBAPS) Institut d‘Investigacions Biomèdiques August Pi i Sunyer University of Amsterdam(IDIBAPS) Institut d‘Investigacions Biomèdiques August Pi i Sunyer CSISg.tec medical engineering GmbHUPFg.tec medical engineering GmbHUPF(IDIBAPS) Institut d‘Investigacions Biomèdiques August Pi i Sunyer Brain-computer interfaces (BCI) are using the EEG (Electroencephalogram), the ECoG (Electrocorticogram) and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80x80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat’s trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat’s position in real-time.The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4 % using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9 % for 3 rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that place cells were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00085/fullHippocampusBCIBrain-computer interfacespatial navigationPlace Cellsspikes |
| spellingShingle | Christoph Guger Thomas eGener Cyriel ePennartz Jorge eBrotons-Mas Jorge eBrotons-Mas Guenter Edlinger Sergi eBermúdez I Badia Stefan eSchaffelhofer Paul eVerschure Maria V Sanchez-Vives Real-time Position Reconstruction with Hippocampal Place Cells Frontiers in Neuroscience Hippocampus BCI Brain-computer interface spatial navigation Place Cells spikes |
| title | Real-time Position Reconstruction with Hippocampal Place Cells |
| title_full | Real-time Position Reconstruction with Hippocampal Place Cells |
| title_fullStr | Real-time Position Reconstruction with Hippocampal Place Cells |
| title_full_unstemmed | Real-time Position Reconstruction with Hippocampal Place Cells |
| title_short | Real-time Position Reconstruction with Hippocampal Place Cells |
| title_sort | real time position reconstruction with hippocampal place cells |
| topic | Hippocampus BCI Brain-computer interface spatial navigation Place Cells spikes |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00085/full |
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