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
• akční potenciály fyziologie   MeSH
• modely neurologické MeSH
• nervová síť * fyziologie   MeSH
• nervový přenos * fyziologie   MeSH
• nervový útlum fyziologie   MeSH
• neuronové sítě MeSH
• počítačová simulace MeSH
• reprodukovatelnost výsledků MeSH
• Publikační typ
• časopisecké články 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.

• Klíčová slova
• adaptive threshold, integrate-and-fire model, olfactory receptor neuron,
• MeSH
• akční potenciály fyziologie   MeSH
• biologické modely MeSH
• čichové buňky účinky léků   fyziologie   MeSH
• elektrofyziologické jevy MeSH
• fyziologická adaptace * MeSH
• můry fyziologie   MeSH
• sexuální lákadla farmakologie   MeSH
• zvířata MeSH
• Check Tag
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• sexuální lákadla 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
• čichové buňky fyziologie   MeSH
• čichové dráhy fyziologie   MeSH
• elektrofyziologické jevy MeSH
• feromony fyziologie   MeSH
• můry fyziologie   MeSH
• neurony aferentní fyziologie   MeSH
• pravděpodobnost MeSH
• reprodukovatelnost výsledků MeSH
• statistické modely MeSH
• tykadla členovců fyziologie   MeSH
• zvířata MeSH
• Check Tag
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• feromony 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
• akustická stimulace MeSH
• algoritmy * MeSH
• evokované potenciály fyziologie   MeSH
• fyzikální stimulace MeSH
• fyziologická adaptace fyziologie   MeSH
• lidé MeSH
• modely neurologické * MeSH
• nervová síť cytologie   fyziologie   MeSH
• neurony fyziologie   MeSH
• psychofyzika MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH