Two distinct mechanisms shape the reliability of neural responses.
Despite intrinsic noise sources, neurons can generate action potentials with remarkable reliability. This reliability is influenced by the characteristics of sensory or synaptic inputs, such as stimulus frequency. Here we use conductance-based models to study the frequency dependence of reliability in terms of the underlying single-cell properties. We are led to distinguish a mean-driven firing regime, where the stimulus mean is sufficient to elicit continuous firing, and a fluctuation-driven firing regime, where spikes are generated by transient stimulus fluctuations. In the mean-driven regime, the stimulus frequency that induces maximum reliability coincides with the firing rate of the cell, whereas in the fluctuation-driven regime, it is determined by the resonance properties of the subthreshold membrane potential. When the stimulus frequency does not match the optimal frequency, the two firing regimes exhibit different "symptoms" of decreased reliability: reduced spike-time precision and reduced spike probability, respectively. As a signature of stochastic resonance, reliable spike generation in the fluctuation-driven regime can benefit from intermediate amounts of noise that boost spike probability without significantly impairing spike-time precision. Our analysis supports the view that neurons are endowed with selection mechanisms that allow only certain stimulus frequencies to induce reliable spiking. By modulating the intrinsic cell properties, the nervous system can thus tune individual neurons to pick out specific input frequency bands with enhanced spike precision or spike probability.
Schreiber
Susanne
S
Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Invalidenstr. 43, D-10115 Berlin, Germany. s.schreiber@rz.hu-berlin.de
Samengo
Inés
I
Herz
Andreas V M
AV
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001703:Biophysics; D003198:Computer Simulation; D017097:Electric Impedance; D004558:Electric Stimulation; D008959:Models, Neurological; D009431:Neural Conduction / Q000502:physiology; D009474:Neurons / Q000145:classification* / Q000502:physiology*; D011336:Probability; D011930:Reaction Time / Q000502:physiology; D015203:Reproducibility of Results; D013269:Stochastic Processes; D013997:Time Factors