Jeremy E. Niven

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

SUMMARY Evolution 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 discuss the role of these efficient schemes in reducing the costs of information processing. Assessing evidence from a wide range of vertebrate and invertebrate examples, we show that reducing energy expenditure can account for many of the morphological features of sensory systems and has played a key role in their evolution.

Are Bigger Brains Better

Attempts to relate brain size to behaviour and cognition have rarely integrated information from insects with that from vertebrates. Many insects, however, demonstrate that highly differentiated motor repertoires, extensive social structures and cognition are possible with very small brains, emphasising that we need to understand the neural circuits, not just the size of brain regions, which underlie these feats. Neural network analyses show that cognitive features found in insects, such as numerosity, attention and categorisation-like processes, may require only very limited neuron numbers. Thus, brain size may have less of a relationship with behavioural repertoire and cognitive capacity than generally assumed, prompting the question of what large brains are for. Larger brains are, at least partly, a consequence of larger neurons that are necessary in large animals due to basic biophysical constraints. They also contain greater replication of neuronal circuits, adding precision to sensory processes, detail to perception, more parallel processing and enlarged storage capacity. Yet, these advantages are unlikely to produce the qualitative shifts in behaviour that are often assumed to accompany increased brain size. Instead, modularity and interconnectivity may be more important.

Fly Photoreceptors Demonstrate Energy-Information Trade-Offs in Neural Coding

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, ∼20% of a photoreceptors 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 ∼200 bits s−1 in D. melanogaster to ∼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 photoreceptors 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.

Action potential energy efficiency varies among neuron types in vertebrates and invertebrates

The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na+ and K+ currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin–Huxley model of the squid axon, optimizing the kinetics or number of Na+ and K+ channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost.

Why Do Axons Differ in Caliber

CNS axons differ in diameter (d) by nearly 100-fold (∼0.1–10 μm); therefore, they differ in cross-sectional area (d2) and volume by nearly 10,000-fold. If, as found for optic nerve, mitochondrial volume fraction is constant with axon diameter, energy capacity would rise with axon volume, also as d2. We asked, given constraints on space and energy, what functional requirements set an axons diameter? Surveying 16 fiber groups spanning nearly the full range of diameters in five species (guinea pig, rat, monkey, locust, octopus), we found the following: (1) thin axons are most numerous; (2) mean firing frequencies, estimated for nine of the identified axon classes, are low for thin fibers and high for thick ones, ranging from ∼1 to >100 Hz; (3) a tracts distribution of fiber diameters, whether narrow or broad, and whether symmetric or skewed, reflects heterogeneity of information rates conveyed by its individual fibers; and (4) mitochondrial volume/axon length rises ≥d2. To explain the pressure toward thin diameters, we note an established law of diminishing returns: an axon, to double its information rate, must more than double its firing rate. Since diameter is apparently linear with firing rate, doubling information rate would more than quadruple an axons volume and energy use. Thicker axons may be needed to encode features that cannot be efficiently decoded if their information is spread over several low-rate channels. Thus, information rate may be the main variable that sets axon caliber, with axons constrained to deliver information at the lowest acceptable rate.

Do insect metabolic rates at rest and during flight scale with body mass

Energetically costly behaviours, such as flight, push physiological systems to their limits requiring metabolic rates (MR) that are highly elevated above the resting MR (RMR). Both RMR and MR during exercise (e.g. flight or running) in birds and mammals scale allometrically, although there is little consensus about the underlying mechanisms or the scaling relationships themselves. Even less is known about the allometric scaling of RMR and MR during exercise in insects. We analysed data on the resting and flight MR (FMR) of over 50 insect species that fly to determine whether RMR and FMR scale allometrically. RMR scaled with body mass to the power of 0.66 (M0.66), whereas FMR scaled with M1.10. Further analysis suggested that FMR scaled with two separate relationships; insects weighing less than 10 mg had fourfold lower FMR than predicted from the scaling of FMR in insects weighing more than 10 mg, although both groups scaled with M0.86. The scaling exponents of RMR and FMR in insects were not significantly different from those of birds and mammals, suggesting that they might be determined by similar factors. We argue that low FMR in small insects suggests these insects may be making considerable energy savings during flight, which could be extremely important for the physiology and evolution of insect flight.

Robustness of Neural Coding in Drosophila Photoreceptors in the Absence of Slow Delayed Rectifier K+ Channels

Determining the contribution of a single type of ion channel to information processing within a neuron requires not only knowledge of the properties of the channel but also understanding of its function within a complex system. We studied the contribution of slow delayed rectifier K+ channels to neural coding in Drosophila photoreceptors by combining genetic and electrophysiological approaches with biophysical modeling. We show that the Shab gene encodes the slow delayed rectifier K+ channel and identify a novel voltage-gated K+ conductance. Analysis of the in vivo recorded voltage responses together with their computer-simulated counterparts demonstrates that Shab channels in Drosophila photoreceptors attenuate the light-induced depolarization and prevent response saturation in bright light. We also show that reduction of the Shab conductance in mutant photoreceptors is accompanied by a proportional drop in their input resistance. This reduction in input resistance partially restores the signaling range, sensitivity, and dynamic coding of light intensities of Shab photoreceptors to those of the wild-type counterparts. However, loss of the Shab channels may affect both the energy efficiency of coding and the processing of natural stimuli. Our results highlight the role of different types of voltage-gated K+ channels in the performance of the photoreceptors and provide insight into functional robustness against the perturbation of specific ion channel composition.

Miniaturization of Nervous Systems and Neurons

Miniaturized species have evolved in many animal lineages, including insects and vertebrates. Consequently, their nervous systems are constrained to fit within tiny volumes. These miniaturized nervous systems face two major challenges for information processing: noise and energy consumption. Fewer or smaller neurons with fewer molecular components will increase noise, affecting information processing and transmission. Smaller, more densely-packed neural processes will increase the density of energy consumption whilst reducing the space available for mitochondria, which supply energy. Although miniaturized nervous systems benefit from smaller distances between neurons, thus saving time, space and energy, they have also increased the space available for neural processing by expanding and contorting their nervous systems to fill any available space, sometimes at the expense of other structures. Other adaptations, such as multifunctional neurons or matched filters, may further alleviate the pressures on space within miniaturized nervous systems. Despite these adaptations, we argue that limitations on information processing are likely to affect the behaviour generated by miniaturized nervous systems.

Shaker K(+)-channels are predicted to reduce the metabolic cost of neural information in Drosophila photoreceptors.

Shaker K+-channels are one of several voltage-activated K+-channels expressed in Drosophila photoreceptors. We have shown recently that Shaker channels act as selective amplifiers, attenuating some signals while boosting others. Loss of these channels reduces the photoreceptor information capacity (bits s-1) and induces compensatory changes in photoreceptors enabling them to minimize the impact of this loss upon coding natural-like stimuli. Energy as well as coding is also an important consideration in understanding the role of ion channels in neural processing. Here, we use a simple circuit model that incorporates the major ion channels, pumps and exchangers of the photoreceptors to derive experimentally based estimates of the metabolic cost of neural information in wild-type (WT) and Shaker mutant photoreceptors. We show that in WT photoreceptors, which contain Shaker K+-channels, each bit of information costs approximately half the number of ATP molecules than each bit in Shaker photoreceptors, in which lack of the Shaker K+-channels is compensated by increased leak conductance. Additionally, using a Hodgkin-Huxley-type model coupled to the circuit model we show that the amount of leak present in both WT and Shaker photoreceptors is optimized to both maximize the available voltage range and minimize the metabolic cost.

Balanced Excitatory and Inhibitory Synaptic Currents Promote Efficient Coding and Metabolic Efficiency

A balance between excitatory and inhibitory synaptic currents is thought to be important for several aspects of information processing in cortical neurons in vivo, including gain control, bandwidth and receptive field structure. These factors will affect the firing rate of cortical neurons and their reliability, with consequences for their information coding and energy consumption. Yet how balanced synaptic currents contribute to the coding efficiency and energy efficiency of cortical neurons remains unclear. We used single compartment computational models with stochastic voltage-gated ion channels to determine whether synaptic regimes that produce balanced excitatory and inhibitory currents have specific advantages over other input regimes. Specifically, we compared models with only excitatory synaptic inputs to those with equal excitatory and inhibitory conductances, and stronger inhibitory than excitatory conductances (i.e. approximately balanced synaptic currents). Using these models, we show that balanced synaptic currents evoke fewer spikes per second than excitatory inputs alone or equal excitatory and inhibitory conductances. However, spikes evoked by balanced synaptic inputs are more informative (bits/spike), so that spike trains evoked by all three regimes have similar information rates (bits/s). Consequently, because spikes dominate the energy consumption of our computational models, approximately balanced synaptic currents are also more energy efficient than other synaptic regimes. Thus, by producing fewer, more informative spikes approximately balanced synaptic currents in cortical neurons can promote both coding efficiency and energy efficiency.