Dynamic influences on coincidence detection in neocortical pyramidal neurons.
The firing rate of neocortical pyramidal neurons is believed to represent primarily the average arrival rate of synaptic inputs; however, it has also been found to vary somewhat depending on the degree of synchrony among synaptic inputs. We investigated the ability of pyramidal neurons to perform coincidence detection, that is, to represent input timing in their firing rate, and explored some factors that influence that representation. We injected computer-generated simulated synaptic inputs into pyramidal neurons during whole-cell recordings, systematically altering the phase delay between two groups of periodic simulated input events. We explored how input intensity, the synaptic time course, inhibitory synaptic conductance, and input jitter influenced the firing rate representation of input timing. In agreement with computer modeling studies, we found that input synchronization increases firing rate when intensity is low but reduces firing rate when intensity is high. At high intensity, the effect of synchrony on firing rate could be switched from reducing to increasing firing rate by shortening the simulated excitatory synaptic time course, adding inhibition (using the dynamic clamp technique), or introducing a small input jitter. These opposite effects of synchrony may serve different computational functions: as a means of increasing firing rate it may be useful for efficient recruitment or for computing a continuous parameter, whereas as a means of decreasing firing rate it may provide gain control, which would allow redundant or excessive input to be ignored. Modulation of dynamic input properties may allow neurons to perform different operations depending on the task at hand.
Department of Physiology and Biophysics, Neurology, University of Washington, Seattle, Washington 98105, USA.
Lucinda A
LA
Grande
Gregory A
GA
Kinney
Greta L
GL
Miracle
William J
WJ
Spain
D000818:Animals; D003198:Computer Simulation; D004558:Electric Stimulation; D066298:In Vitro Techniques; D008959:Models, Neurological; D019579:Neocortex / Q000166:cytology / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D018408:Patch-Clamp Techniques; D017966:Pyramidal Cells / Q000502:physiology*; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013003:Somatosensory Cortex / Q000166:cytology / Q000502:physiology