An imaging system for monitoring receptive field dynamics
Field:	Motor systems and sensorimotor integration
Authors:	Per Petersson Mattias Holmer Thomas Breslin Marcus Granmo Jens Schouenborg
Address of presenting author:	Neurofysiologi Sölvegatan 19 223 62 Lund
E-mail:	Per.Petersson@mphy.lu.se
Phone:	046-222 46 51
Fax:	046-222 45 46
Text of abstract:	In the present study, a computerized system, termed Receptive Field Imaging (RFI), for rapid mapping of multiple receptive fields and their respective sensitivity distribution, is described. RFI was tested on the receptive fields in the nociceptive withdrawal reflex (NWR) system in the rat. The NWR has a modular organisation, where each module performs a functionally determined sensorimotor transformation. To obtain a detailed map of the sensitivity distribution within a receptive field, the strengths of a large number of afferent inputs need to be determined. Automated multielectrode stimulation and recording, spike classification and counting is performed on-line by the RFI program. While direct user interpretation is made possible by a user-friendly graphical presentation. RFI uses random stimulation of multiple sites in combination with an averaging procedure time locked to stimulation to extract the contribution from each of the stimulated sites. To evaluate system function, a series of experiments have been carried out. RFI replicates results obtained with non-automated methods and have been shown to follow receptive field dynamics induced by topical spinal cord application of Morphine and Naloxon on a minute time scale. The large number of stimulation and registration units monitored in parallel allows a detailed network analysis of single synapse sets corresponding to 'connection weights' between individual neurones. On the network level of analysis, 'population coding' can be revealed by e.g. cross-correlation measurements of activity resulting from stimulation of sites connecting to multiple modules, and consequently the degree of coupling between entire functional modules. Thus, an in vivo correlate of artificial neuronal network (ANN) models is at hand, and a future prospect will be to closely match
Keywords:	Receptive field, multi-electrode, somatosensory

Created 2000-03-17