In this paper we investigate the rate coding capabilities of neurons whose input signal are alterations of the base state of balanced inhibitory and excitatory synaptic currents. We consider different regimes of excitation-inhibition relationship and an established conductance-based leaky integrator model with adaptive threshold and parameter sets recreating biologically relevant spiking regimes. We find that given mean post-synaptic firing rate, counter-intuitively, increased ratio of inhibition to excitation generally leads to higher signal to noise ratio (SNR). On the other hand, the inhibitory input significantly reduces the dynamic coding range of the neuron. We quantify the joint effect of SNR and dynamic coding range by computing the metabolic efficiency-the maximal amount of information per one ATP molecule expended (in bits/ATP). Moreover, by calculating the metabolic efficiency we are able to predict the shapes of the post-synaptic firing rate histograms that may be tested on experimental data. Likewise, optimal stimulus input distributions are predicted, however, we show that the optimum can essentially be reached with a broad range of input distributions. Finally, we examine which parameters of the used neuronal model are the most important for the metabolically efficient information transfer.

• MeSH
• Adenosine Triphosphate metabolism   MeSH
• Action Potentials physiology   MeSH
• Excitatory Postsynaptic Potentials physiology   MeSH
• Membrane Potentials physiology   MeSH
• Models, Neurological * MeSH
• Neural Conduction physiology   MeSH
• Synaptic Transmission physiology   MeSH
• Neural Inhibition physiology   MeSH
• Neurons physiology   MeSH
• Computer Simulation MeSH
• Signal-To-Noise Ratio MeSH
• Computational Biology MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Adenosine Triphosphate MeSH

In order to understand how olfactory stimuli are encoded and processed in the brain, it is important to build a computational model for olfactory receptor neurons (ORNs). Here, we present a simple and reliable mathematical model of a moth ORN generating spikes. The model incorporates a simplified description of the chemical kinetics leading to olfactory receptor activation and action potential generation. We show that an adaptive spike threshold regulated by prior spike history is an effective mechanism for reproducing the typical phasic-tonic time course of ORN responses. Our model reproduces the response dynamics of individual neurons to a fluctuating stimulus that approximates odorant fluctuations in nature. The parameters of the spike threshold are essential for reproducing the response heterogeneity in ORNs. The model provides a valuable tool for efficient simulations of olfactory circuits.

• Keywords
• adaptive threshold, integrate-and-fire model, olfactory receptor neuron,
• MeSH
• Action Potentials physiology   MeSH
• Models, Biological MeSH
• Olfactory Receptor Neurons drug effects   physiology   MeSH
• Electrophysiological Phenomena MeSH
• Adaptation, Physiological * MeSH
• Moths physiology   MeSH
• Sex Attractants pharmacology   MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Sex Attractants MeSH