Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.

• MeSH
• Action Potentials physiology   MeSH
• Models, Neurological MeSH
• Nerve Net * physiology   MeSH
• Synaptic Transmission * physiology   MeSH
• Neural Inhibition physiology   MeSH
• Neural Networks, Computer MeSH
• Computer Simulation MeSH
• Reproducibility of Results MeSH
• Publication type
• Journal Article MeSH

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

The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system.

• MeSH
• Olfactory Receptor Neurons physiology   MeSH
• Olfactory Pathways physiology   MeSH
• Electrophysiological Phenomena MeSH
• Pheromones physiology   MeSH
• Moths physiology   MeSH
• Neurons, Afferent physiology   MeSH
• Probability MeSH
• Reproducibility of Results MeSH
• Models, Statistical MeSH
• Arthropod Antennae physiology   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
• Pheromones MeSH

Sensory neurons are often reported to adjust their coding accuracy to the stimulus statistics. The observed match is not always perfect and the maximal accuracy does not align with the most frequent stimuli. As an alternative to a physiological explanation we show that the match critically depends on the chosen stimulus measurement scale. More generally, we argue that if we measure the stimulus intensity on the scale which is proportional to the perception intensity, an improved adjustment in the coding accuracy is revealed. The unique feature of stimulus units based on the psychophysical scale is that the coding accuracy can be meaningfully compared for different stimuli intensities, unlike in the standard case of a metric scale.

• MeSH
• Acoustic Stimulation MeSH
• Algorithms * MeSH
• Evoked Potentials physiology   MeSH
• Physical Stimulation MeSH
• Adaptation, Physiological physiology   MeSH
• Humans MeSH
• Models, Neurological * MeSH
• Nerve Net cytology   physiology   MeSH
• Neurons physiology   MeSH
• Psychophysics MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH