Intrinsic cellular properties determine the temporal filtering characteristics of neurons

GERMAN MATO, INES SAMENGO

Washington DC, USA

Conferencia; 38 Society for Neuroscience Meeting; 2008

Society for Neuroscience

Neurons in the nervous system exhibit an outstanding variety of morphological and physiological properties. However, close to threshold, this remarkable richness may be grouped succinctly into two basic types of excitability, often referred to as type I and type II. The dynamical traits of these two neuron types have been extensively characterized for constant input currents. The question still remains, however, whether the two types of excitability are associated to different coding properties also in noisy environments. To address this issue, here we determine the temporal filtering characteristics of each cell type, using random Gaussian input stimuli. By means of reverse correlation methods, we obtain the relevant input histories shaping the firing probability, for both types of excitability. Type I neurons (as Wang-Buszaki hippocampal model interneurons) fire in response to scale-free depolarizing stimuli. Type II neurons (as Hodgkin-Huxley model cells) are instead most efficiently driven by input stimuli containing both depolarizing and hyperpolarizing phases, with significant power in the frequency band corresponding to both the subthreshold and the suprathreshold intrinsic frequencies of the cell. These results show that even in very noisy environments, where the coefficient of variability is close to 1, the intrinsic cellular properties have relevant consequences to the neural code. Such noisy environments are often encountered in strongly interconnected neural networks with balanced excitatory and inhibitory synapses. Our simulations show that the filtering properties of individual neurons are also revealed at the network level, thus determining large-scale properties of the neural code.