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