Noise can improve signal detection in hippocampal synapses
Dominique Durand (Departments of Biomedical Engineering and Neurosciences, Case Western Reserve University)
(November 12, 2002 4:30 PM - 5:30 PM)
Stochastic Resonance (SR) is a phenomenon observed in nonlinear systems whereby the introduction of noise enhances the detection of a subthreshold signal for a certain range of noise intensity. SR has been observed in many physical and mathematical systems. The nonlinear threshold detection mechanism that neurons employ and the noisy environment in which they reside make it likely that SR plays a role in neural signal detection. While the role of SR in sensory neural systems has been studied, its function in central neurons is unknown. In many central neurons, such as the hippocampal CA1 cell, very large dendritic trees are responsible for detecting neural input in a noisy environment. Attenuation due to the electrotonic length of these trees is significant, suggesting that a method other than passive summation is necessary if signals at the distal ends of the tree are to be detected. The hypothesis that SR is involved in the detection of distal synaptic inputs was first tested in a computer simulation of a CA1 cell and then verified with in vitro rat hippocampal slices. The results strongly showed that SR can enhance signal detection in CA1 hippocampal cells. High levels of noise were found to equalize detection of synaptic signals received at varying positions on the dendritic tree. The amount of noise needed to evoke the effect is comparable with physiological noise in slices and in vivo. Computer simulations show that the phenomenon is enhanced in neuronal networks. Therefore both computer simulations and experiments suggest an important role for stochastic resonance in neuronal signal processing.