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Title: Real-time position reconstruction with hippocampal place cells
Authors: Guger, Christoph
Gener, Thomas
Pennartz, Cyriel M A
Brotons-Mas, Jorge R.
Edlinger, Günter
Bermúdez i Badia, S. 
Verschure, Paul
Schaffelhofer, Stefan
Sanchez-Vives, Maria V.
UNESCO Clasification: 32 Ciencias médicas
Keywords: Real-time position reconstruction
Place cells
Firing fields
Spatial navigation
Hippocampus, et al
Issue Date: 2011
Journal: Frontiers in Neuroscience 
Abstract: Brain–computer interfaces (BCI) are using the electroencephalogram, the 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 (PCs) 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 80 cm × 80 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 three 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 PCs 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.
ISSN: 1662-4548
DOI: 10.3389/fnins.2011.00085
Source: Frontiers in Neuroscience[ISSN 1662-4548] (85) (Junio 2011)
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