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Fly photoreceptors demonstrate energy-information trade-offs in neural coding.

Niven JE, Anderson JC, Laughlin SB - PLoS Biol. (2007)

Bottom Line: Comparing species, the fixed cost, the total cost of signalling, and the unit cost (cost per bit) all increase with a photoreceptor's highest information rate to make information more expensive in higher performance cells.This law of diminishing returns promotes the evolution of economical structures by severely penalising overcapacity.Similar relationships could influence the function and design of many neurons because they are subject to similar biophysical constraints on information throughput.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Cambridge, Cambridge, United Kingdom.

ABSTRACT
Trade-offs between energy consumption and neuronal performance must shape the design and evolution of nervous systems, but we lack empirical data showing how neuronal energy costs vary according to performance. Using intracellular recordings from the intact retinas of four flies, Drosophila melanogaster, D. virilis, Calliphora vicina, and Sarcophaga carnaria, we measured the rates at which homologous R1-6 photoreceptors of these species transmit information from the same stimuli and estimated the energy they consumed. In all species, both information rate and energy consumption increase with light intensity. Energy consumption rises from a baseline, the energy required to maintain the dark resting potential. This substantial fixed cost, approximately 20% of a photoreceptor's maximum consumption, causes the unit cost of information (ATP molecules hydrolysed per bit) to fall as information rate increases. The highest information rates, achieved at bright daylight levels, differed according to species, from approximately 200 bits s(-1) in D. melanogaster to approximately 1,000 bits s(-1) in S. carnaria. Comparing species, the fixed cost, the total cost of signalling, and the unit cost (cost per bit) all increase with a photoreceptor's highest information rate to make information more expensive in higher performance cells. This law of diminishing returns promotes the evolution of economical structures by severely penalising overcapacity. Similar relationships could influence the function and design of many neurons because they are subject to similar biophysical constraints on information throughput.

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Measurements of Photoreceptor Membrane Properties Allow the Calculation of Metabolic Cost(A) The membrane potential (mean Ā± standard error of the mean) of R1ā€“6 photoreceptors in the dark and at different effective photon rates, measured in four species S. carnaria (blue), C. vicina (red), D. virilis (green), and D. melanogaster (black).(B) The corresponding resistances (mean Ā± standard error of the mean) of R1ā€“6 photoreceptor in the dark and at different effective photon rates.(C) The electrical model circuit of the photoreceptors. The model calculates from the measurements of membrane potential and resistance the rate at which the Na+/K+ pump, P, hydrolyses ATP molecules: gL = light-gated conductance; EL = reversal potential for light-gated current; iL = light-gated current; gK = potassium conductance; EK = potassium reversal potential; iK = potassium current.(D) The rate of hydrolysis of ATP molecules calculated at each effective photon rate for R1ā€“6 photoreceptor of the four species (mean).
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pbio-0050116-g003: Measurements of Photoreceptor Membrane Properties Allow the Calculation of Metabolic Cost(A) The membrane potential (mean Ā± standard error of the mean) of R1ā€“6 photoreceptors in the dark and at different effective photon rates, measured in four species S. carnaria (blue), C. vicina (red), D. virilis (green), and D. melanogaster (black).(B) The corresponding resistances (mean Ā± standard error of the mean) of R1ā€“6 photoreceptor in the dark and at different effective photon rates.(C) The electrical model circuit of the photoreceptors. The model calculates from the measurements of membrane potential and resistance the rate at which the Na+/K+ pump, P, hydrolyses ATP molecules: gL = light-gated conductance; EL = reversal potential for light-gated current; iL = light-gated current; gK = potassium conductance; EK = potassium reversal potential; iK = potassium current.(D) The rate of hydrolysis of ATP molecules calculated at each effective photon rate for R1ā€“6 photoreceptor of the four species (mean).

Mentions: We used an established electrical model of the photoreceptor membrane to estimate the rate at which photoreceptors consume metabolic energy (see Materials and Methods). The model [14,34] incorporates the two major conductances, light-gated and potassium, as well as the electrogenic Na+/K+ pump, and calculates the flux of ions through these components from measurements of total conductance and membrane potential (Figure 3). The flux of ions through the Na+/K+ pump gives the rate at which ATP is hydrolysed in order to maintain ionic concentration gradients, and ATP hydrolysis rate in molecules sāˆ’1 is our measure of metabolic energy cost.


Fly photoreceptors demonstrate energy-information trade-offs in neural coding.

Niven JE, Anderson JC, Laughlin SB - PLoS Biol. (2007)

Measurements of Photoreceptor Membrane Properties Allow the Calculation of Metabolic Cost(A) The membrane potential (mean Ā± standard error of the mean) of R1ā€“6 photoreceptors in the dark and at different effective photon rates, measured in four species S. carnaria (blue), C. vicina (red), D. virilis (green), and D. melanogaster (black).(B) The corresponding resistances (mean Ā± standard error of the mean) of R1ā€“6 photoreceptor in the dark and at different effective photon rates.(C) The electrical model circuit of the photoreceptors. The model calculates from the measurements of membrane potential and resistance the rate at which the Na+/K+ pump, P, hydrolyses ATP molecules: gL = light-gated conductance; EL = reversal potential for light-gated current; iL = light-gated current; gK = potassium conductance; EK = potassium reversal potential; iK = potassium current.(D) The rate of hydrolysis of ATP molecules calculated at each effective photon rate for R1ā€“6 photoreceptor of the four species (mean).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1828148&req=5

pbio-0050116-g003: Measurements of Photoreceptor Membrane Properties Allow the Calculation of Metabolic Cost(A) The membrane potential (mean Ā± standard error of the mean) of R1ā€“6 photoreceptors in the dark and at different effective photon rates, measured in four species S. carnaria (blue), C. vicina (red), D. virilis (green), and D. melanogaster (black).(B) The corresponding resistances (mean Ā± standard error of the mean) of R1ā€“6 photoreceptor in the dark and at different effective photon rates.(C) The electrical model circuit of the photoreceptors. The model calculates from the measurements of membrane potential and resistance the rate at which the Na+/K+ pump, P, hydrolyses ATP molecules: gL = light-gated conductance; EL = reversal potential for light-gated current; iL = light-gated current; gK = potassium conductance; EK = potassium reversal potential; iK = potassium current.(D) The rate of hydrolysis of ATP molecules calculated at each effective photon rate for R1ā€“6 photoreceptor of the four species (mean).
Mentions: We used an established electrical model of the photoreceptor membrane to estimate the rate at which photoreceptors consume metabolic energy (see Materials and Methods). The model [14,34] incorporates the two major conductances, light-gated and potassium, as well as the electrogenic Na+/K+ pump, and calculates the flux of ions through these components from measurements of total conductance and membrane potential (Figure 3). The flux of ions through the Na+/K+ pump gives the rate at which ATP is hydrolysed in order to maintain ionic concentration gradients, and ATP hydrolysis rate in molecules sāˆ’1 is our measure of metabolic energy cost.

Bottom Line: Comparing species, the fixed cost, the total cost of signalling, and the unit cost (cost per bit) all increase with a photoreceptor's highest information rate to make information more expensive in higher performance cells.This law of diminishing returns promotes the evolution of economical structures by severely penalising overcapacity.Similar relationships could influence the function and design of many neurons because they are subject to similar biophysical constraints on information throughput.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Cambridge, Cambridge, United Kingdom.

ABSTRACT
Trade-offs between energy consumption and neuronal performance must shape the design and evolution of nervous systems, but we lack empirical data showing how neuronal energy costs vary according to performance. Using intracellular recordings from the intact retinas of four flies, Drosophila melanogaster, D. virilis, Calliphora vicina, and Sarcophaga carnaria, we measured the rates at which homologous R1-6 photoreceptors of these species transmit information from the same stimuli and estimated the energy they consumed. In all species, both information rate and energy consumption increase with light intensity. Energy consumption rises from a baseline, the energy required to maintain the dark resting potential. This substantial fixed cost, approximately 20% of a photoreceptor's maximum consumption, causes the unit cost of information (ATP molecules hydrolysed per bit) to fall as information rate increases. The highest information rates, achieved at bright daylight levels, differed according to species, from approximately 200 bits s(-1) in D. melanogaster to approximately 1,000 bits s(-1) in S. carnaria. Comparing species, the fixed cost, the total cost of signalling, and the unit cost (cost per bit) all increase with a photoreceptor's highest information rate to make information more expensive in higher performance cells. This law of diminishing returns promotes the evolution of economical structures by severely penalising overcapacity. Similar relationships could influence the function and design of many neurons because they are subject to similar biophysical constraints on information throughput.

Show MeSH