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Coding efficiency of fly motion processing is set by firing rate, not firing precision.

Spavieri DL, Eichner H, Borst A - PLoS Comput. Biol. (2010)

Bottom Line: Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions.We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance.Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

View Article: PubMed Central - PubMed

Affiliation: Department of System and Computational Neurobiology, Max-Planck-Institute of Neurobiology, Martinsried, Germany.

ABSTRACT
To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervous system changes with temperature and luminance. Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions. We recorded responses from a motion-sensitive neuron, the H1-cell, to a time-varying stimulus at many different combinations of temperature and luminance. We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance. Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

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Model structure and simulation results.A Structure of the elementary motion detection circuit of the model. B Schematic of the Integrate-and-Fire neuron used for simulating H1 responses. C A segment of the image velocity as a function of time, same excerpt as shown in Fig. 1A. D Simulated raster plot and E Simulated average firing rates for each out of three stimulus conditions (averaged over 1500 trials) as indicated in the legend. F Segment of a stimulus waveform during a velocity transition from inhibitory to excitatory direction; same excerpt as shown in Fig. 2D. G Simulated raster plots of the responses in 20 trials at three different conditions. H Probability distribution of the arrival time of the first spike for the three different conditions shown above. Legend as in 4E.
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pcbi-1000860-g004: Model structure and simulation results.A Structure of the elementary motion detection circuit of the model. B Schematic of the Integrate-and-Fire neuron used for simulating H1 responses. C A segment of the image velocity as a function of time, same excerpt as shown in Fig. 1A. D Simulated raster plot and E Simulated average firing rates for each out of three stimulus conditions (averaged over 1500 trials) as indicated in the legend. F Segment of a stimulus waveform during a velocity transition from inhibitory to excitatory direction; same excerpt as shown in Fig. 2D. G Simulated raster plots of the responses in 20 trials at three different conditions. H Probability distribution of the arrival time of the first spike for the three different conditions shown above. Legend as in 4E.

Mentions: In order to gain insight how temperature and luminance affect the motion processing pathway from the photoreceptors up to H1, we implemented a model of the system under study (see Materials and Methods, Modeling). This model (Fig. 4A, B) incorporates temperature and luminance dependent photoreceptor impulse responses taken from [6], an array of elementary motion detectors [34], [35], and an Integrate-and-Fire model cell that spatially integrates over the array of motion detector inputs. Our aim was to reproduce the measured firing rates and firing precision for the three stimulus conditions depicted in Fig. 1C and Fig. 2D. In particular, the model should be able to reproduce the temperature dependent effects - change of response strength, conservation of firing precision - and the luminance dependent effects - changes of both firing rate and firing precision. The model modifications necessary for reproducing the measurements depicted in Fig. 1C and Fig. 2D thus should allow us to hypothesize what parameters of the motion processing pathway are influenced by the two different sources of perturbation, temperature and luminance, and in which way. The results of the simulation are shown in Fig. 4D, E, G, H.


Coding efficiency of fly motion processing is set by firing rate, not firing precision.

Spavieri DL, Eichner H, Borst A - PLoS Comput. Biol. (2010)

Model structure and simulation results.A Structure of the elementary motion detection circuit of the model. B Schematic of the Integrate-and-Fire neuron used for simulating H1 responses. C A segment of the image velocity as a function of time, same excerpt as shown in Fig. 1A. D Simulated raster plot and E Simulated average firing rates for each out of three stimulus conditions (averaged over 1500 trials) as indicated in the legend. F Segment of a stimulus waveform during a velocity transition from inhibitory to excitatory direction; same excerpt as shown in Fig. 2D. G Simulated raster plots of the responses in 20 trials at three different conditions. H Probability distribution of the arrival time of the first spike for the three different conditions shown above. Legend as in 4E.
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getmorefigures.php?uid=PMC2908696&req=5

pcbi-1000860-g004: Model structure and simulation results.A Structure of the elementary motion detection circuit of the model. B Schematic of the Integrate-and-Fire neuron used for simulating H1 responses. C A segment of the image velocity as a function of time, same excerpt as shown in Fig. 1A. D Simulated raster plot and E Simulated average firing rates for each out of three stimulus conditions (averaged over 1500 trials) as indicated in the legend. F Segment of a stimulus waveform during a velocity transition from inhibitory to excitatory direction; same excerpt as shown in Fig. 2D. G Simulated raster plots of the responses in 20 trials at three different conditions. H Probability distribution of the arrival time of the first spike for the three different conditions shown above. Legend as in 4E.
Mentions: In order to gain insight how temperature and luminance affect the motion processing pathway from the photoreceptors up to H1, we implemented a model of the system under study (see Materials and Methods, Modeling). This model (Fig. 4A, B) incorporates temperature and luminance dependent photoreceptor impulse responses taken from [6], an array of elementary motion detectors [34], [35], and an Integrate-and-Fire model cell that spatially integrates over the array of motion detector inputs. Our aim was to reproduce the measured firing rates and firing precision for the three stimulus conditions depicted in Fig. 1C and Fig. 2D. In particular, the model should be able to reproduce the temperature dependent effects - change of response strength, conservation of firing precision - and the luminance dependent effects - changes of both firing rate and firing precision. The model modifications necessary for reproducing the measurements depicted in Fig. 1C and Fig. 2D thus should allow us to hypothesize what parameters of the motion processing pathway are influenced by the two different sources of perturbation, temperature and luminance, and in which way. The results of the simulation are shown in Fig. 4D, E, G, H.

Bottom Line: Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions.We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance.Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

View Article: PubMed Central - PubMed

Affiliation: Department of System and Computational Neurobiology, Max-Planck-Institute of Neurobiology, Martinsried, Germany.

ABSTRACT
To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervous system changes with temperature and luminance. Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions. We recorded responses from a motion-sensitive neuron, the H1-cell, to a time-varying stimulus at many different combinations of temperature and luminance. We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance. Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

Show MeSH