Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations

AbstractWe compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) ahomogeneous population whose units receive independent noise and ii) a deterministicheterogeneous population, where each unit exhibits a different baseline firing rate ( ’disorder’). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disord er depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show tha...
Source: Journal of Computational Neuroscience - Category: Neuroscience Source Type: research