Metabolically Efficient Information Processing

Balasubramanian, Vijay; Kimber, Don; Berry, Michael

doi:10.1162/089976601300014358

Cited by 129 publications

(149 citation statements)

References 13 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…Strategies to improve the energy efficiency of neural coding are not restricted to single neurons but can also occur within populations of neurons (Levy and Baxter, 1996;Vinje and Gallant, 2000;Balasubramanian et al, 2001;Willmore and Tolhurst, 2001;De Polavieja, 2002;Perez-Orive et al, 2002;Schreiber et al, 2002;Olshausen and Field, 2004;Hromádka et al, 2008). Energy efficiency within neural populations is still constrained by the properties of individual neurons, such as the relationship between the energetic cost of maintaining a neuron at rest and whilst signalling.…”

Section: Energy Efficiency In Sensory Systemsmentioning

confidence: 99%

“…Noise is a constraint both upon energy efficient coding and the minimization of wiring costs within the nervous system (Balasubramanian et al, 2001;De Polavieja, 2002;Faisal et al, 2005). For example, an optimum energy efficient code that maximizes the information coded by a given number of spikes (the Boltzmann distribution) predicts that neural spike rates should follow an exponential distribution.…”

Section: Energy Efficiency In Sensory Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Energy limitation as a selective pressure on the evolution of sensory systems

Niven

Laughlin

2008

Journal of Experimental Biology

931

779

View full text Add to dashboard Cite

SummaryEvolution of animal morphology, physiology and behaviour is shaped by the selective pressures to which they are subject. Some selective pressures act to increase the benefits accrued whilst others act to reduce the costs incurred, affecting the cost/benefit ratio. Selective pressures therefore produce a trade-off between costs and benefits that ultimately influences the fitness of the whole organism. The nervous system has a unique position as the interface between morphology, physiology and behaviour; the final output of the nervous system is the behaviour of the animal, which is a product of both its morphology and physiology. The nervous system is under selective pressure to generate adaptive behaviour, but at the same time is subject to costs related to the amount of energy that it consumes. Characterising this trade-off between costs and benefits is essential to understanding the evolution of nervous systems, including our own. Within the nervous system, sensory systems are the most amenable to analysing costs and benefits, not only because their function can be more readily defined than that of many central brain regions and their benefits quantified in terms of their performance, but also because recent studies of sensory systems have begun to directly assess their energetic costs. Our review focuses on the visual system in particular, although the principles we discuss are equally applicable throughout the nervous system. Examples are taken from a wide range of sensory modalities in both vertebrates and invertebrates. We aim to place the studies we review into an evolutionary framework. We combine experimentally determined measures of energy consumption from whole retinas of rabbits and flies with intracellular measurements of energy consumption from single fly photoreceptors and recently constructed energy budgets for neural processing in rats to assess the contributions of various components to neuronal energy consumption. Taken together, these studies emphasize the high costs of maintaining neurons at rest and whilst signalling. A substantial proportion of neuronal energy consumption is related to the movements of ions across the neuronal cell membrane through ion channels, though other processes such as vesicle loading and transmitter recycling also consume energy. Many of the energetic costs within neurons are linked to 3Na + /2K + ATPase activity, which consumes energy to pump Na + and K + ions across the cell membrane and is essential for the maintenance of the resting potential and its restoration following signalling. Furthermore, recent studies in fly photoreceptors show that energetic costs can be related, via basic biophysical relationships, to their function. These findings emphasize that neurons are subject to a law of diminishing returns that severely penalizes excess functional capacity with increased energetic costs. The high energetic costs associated with neural tissue favour energy efficient coding and wiring schemes, which have been found in numerous sensory systems. We discus...

show abstract

Section: Energy Efficiency In Sensory Systemsmentioning

confidence: 99%

Section: Energy Efficiency In Sensory Systemsmentioning

confidence: 99%

Energy limitation as a selective pressure on the evolution of sensory systems

Niven

Laughlin

2008

Journal of Experimental Biology

931

779

View full text Add to dashboard Cite

show abstract

“…Noise also constrains coding efficiency potentially preventing neuronal populations from implementing the most efficient codes and, consequently, reducing the energy efficiency of information coding (reviewed in [23]). For example, in populations of neurons encoding natural stimuli low spike rates are used less often than predicted because of their low reliability, causing a deviation from the maximally efficient coding scheme in which spike rates are distributed exponentially [57,58].…”

Section: Synaptic Inputsmentioning

confidence: 99%

Neuronal energy consumption: biophysics, efficiency and evolution

Niven

2016

Current Opinion in Neurobiology

123

View full text Add to dashboard Cite

Electrical and chemical signaling within and between neurons consumes energy. Recent studies have sought to refine our understanding of the processes that consume energy and their relationship to information processing by coupling experiments with computational models and energy budgets. These studies have produced insights into both how neurons and neural circuits function, and why they evolved to function in the way they do. Introduction Neurons consume energy. Appreciating that they do so is essential for understanding and interpreting the function and evolution of neurons, neural circuits and, ultimately, whole brains. Yet we must go beyond mere appreciation by relating specific molecular components and processes to the energy they consume and the work they contribute to processing information and generating behavior. This permits determination of both 'how' and 'why' processes consume energy, and an understanding of the key trade-offs that have influenced neural evolution (reviewed in [1,2]). Although neuronal energy consumption has been studied for over 80 years [3,4], conceptual and methodological breakthroughs [5][6][7][8][9] have prompted renewed interest in the causes and consequences of neuronal energy consumption over the last ~20 years. Here I review this recent progress in our understanding of how the physiology and anatomy of neurons and neural circuits reflect fundamental relationships between energy consumption, biophysics and performance. Addresses

show abstract

“…It is known that energetic costs place important constraints on the design of physical computing devices as well as on neural computing architectures in the brain and retina (9)(10)(11), suggesting that these constraints may also influence the design of cellular computing networks.…”

mentioning

confidence: 99%

Energetic costs of cellular computation

Mehta

Schwab

2012

Proc. Natl. Acad. Sci. U.S.A.

205

256

View full text Add to dashboard Cite

Cells often perform computations in order to respond to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general theoretical grounds, it is expected that such computations require cells to consume energy. In particular, Landauer's principle states that energy must be consumed in order to erase the memory of past observations. Here, we explicitly calculate the energetic cost of steady-state computation of ligand concentration for a simple two-component cellular network that implements a noisy version of the Berg-Purcell strategy. We show that learning about external concentrations necessitates the breaking of detailed balance and consumption of energy, with greater learning requiring more energy. Our calculations suggest that the energetic costs of cellular computation may be an important constraint on networks designed to function in resource poor environments, such as the spore germination networks of bacteria.T he relationship between information and thermodynamics remains an active area of research despite decades of study (1-4). An important implication of the recent experimental confirmation of Landauer's principle, relating the erasure of information to thermodynamic irreversibility, is that any irreversible computing device must necessarily consume energy (2, 3). The generality of Landauer's argument suggests that it is true regardless of how the computation is implemented. A particularly interesting class of examples relevant to systems biology and biophysics is that of intracellular biochemical networks that compute information about the external environment. These biochemical networks are ubiquitous in biology, ranging from the quorumsensing and chemotaxis networks in single-cell organisms to networks that detect hormones and other signaling factors in higher organisms.A fundamental issue is the relationship between the information processing capabilities of these biochemical networks and their energetic costs (5-8). It is known that energetic costs place important constraints on the design of physical computing devices as well as on neural computing architectures in the brain and retina (9-11), suggesting that these constraints may also influence the design of cellular computing networks.The best studied example of a cellular computation is the estimation of the steady-state concentration of a chemical ligand in the surrounding environment (12)(13)(14). This problem was first considered in the seminal paper by Berg and Purcell who showed that the information a cell can acquire about its environment is fundamentally limited by stochastic fluctuations in the occupancy of the membrane-bound receptor proteins that detect the ligand (12). In particular, they considered the case of a cellular receptor that binds ligands with a concentration-dependent rate k off 4 and unbinds particles at a uniform rate k off 4 (see Fig. 1). They argued that cells could estimate the ambient che...

show abstract

Metabolically Efficient Information Processing

Cited by 129 publications

References 13 publications

Energy limitation as a selective pressure on the evolution of sensory systems

Energy limitation as a selective pressure on the evolution of sensory systems

Neuronal energy consumption: biophysics, efficiency and evolution

Energetic costs of cellular computation

Contact Info

Product

Resources

About