Spike-rate coding and spike-time coding are affected oppositely by different adaptation mechanisms.
Spike-frequency adaptation causes reduced spiking during prolonged stimulation, but the full impact of adaptation on neural coding is far more complex, especially if one takes into account the diversity of biophysical mechanisms mediating adaptation and the different ways in which neural information can be encoded. Here, we show that adaptation has opposite effects depending on the neural coding strategy and the biophysical mechanism responsible for adaptation. Under noisy conditions, calcium-activated K(+) current (I(AHP)) improved efficient spike-rate coding at the expense of spike-time coding by regularizing the spike train elicited by slow or constant inputs; noise power was increased at high frequencies but reduced at low frequencies, consistent with noise shaping that improves coding of low- frequency signals. In contrast, voltage-activated M-type K(+) current (I(M)) improved spike-time coding at the expense of spike-rate coding by stopping the neuron from spiking repetitively to slow inputs so that it could generate isolated, well timed spikes in response to fast inputs. Using dynamical systems analysis, we demonstrate how I(AHP) minimizes perturbation of the interspike interval caused by high- frequency noise, whereas I(M) minimizes disruption of spike-timing accuracy caused by repetitive spiking. The dichotomous outcomes are related directly to the distinct activation requirements for I(AHP) and I(M), which in turn dictate whether those currents mediate negative feedback onto spiking or membrane potential. Thus, based on their distinct activation properties, I(AHP) implements noise shaping that improves spike-rate coding of low-frequency signals, whereas I(M) implements high-pass filtering that improves spike-time coding of high- frequency signals.
Prescott
Steven A
SA
Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute, La Jolla, California 92037, USA. prescott@neurobio.pitt.edu
Sejnowski
Terrence J
TJ
D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological; D000465:Algorithms; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*