Metabolic cost as a unifying principle governing neuronal biophysics

Abstract

The brain contains an astonishing diversity of neurons, each expressing only one set of ion channels out of the billions of potential channel combinations. Simple organizing principles are required for us to make sense of this abundance of possibilities and wealth of related data. We suggest that energy minimization subject to functional constraints may be one such unifying principle. We compared the energy needed to produce action potentials singly and in trains for a wide range of channel densities and kinetic parameters and examined which combinations of parameters maximized spiking function while minimizing energetic cost. We confirmed these results for sodium channels using a dynamic current clamp in neocortical fast spiking interneurons. We find further evidence supporting this hypothesis in a wide range of other neurons from several species and conclude that the ion channels in these neurons minimize energy expenditure in their normal range of spiking.he mammalian genome contains genes encoding hundreds of types of ion channels, most of which can be produced in multiple splice variants and regulated at multiple phosphorylation sites by numerous intrinsic and extrinsic factors (1-4). Which combination of these ion channels will be expressed by any particular neuron, with any particular computational or behavioral role? Because any given electrical phenotype can be produced by many combinations of ion channels (5-8), on functional grounds alone, this question is severely underconstrained. What other constraints might govern ion channel expression? And can exploring these constraints help us understand how cells or circuits are constructed?Energy consumption may be one such constraint. The brain is one of the most energetically demanding organs in the body (9, 10); the human brain uses more than twice as much glucose per day as the heart (10). Neuronal activity-action potential generation, input integration, and synaptic transmission-accounts for 50-80% of this energy use (11-14). Potential energy is stored in transmembrane ion gradients, which creates a cellular battery whose maintenance accounts for most of the brain's ATP consumption (11,13,15,16). Action potential generation taps into these gradients and expends some of this potential energy, which needs to be actively restored. How can this energy be most efficiently used to generate activity or carry out computation?This question has inspired a body of research on how to encode a signal, or perform a computation, using as few action potentials-and thus as little energy-as possible (17)(18)(19)(20)(21)(22)(23)(24). Which combinations of ion channels will minimize energy cost depends both on the neuron's function, such as its typical firing rate, as well as on the detailed kinetics of the channels (25). Here, we combine biophysical modeling with dynamic-clamp electrophysiology to understand the constraints on the kinetics and density of the ion channels underlying action potential generation. We find that ion channel expression in various species, neural...

Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics

“…Another possibility is that improved neural efficiency, such as using less brain activity and/or optimizing motor unit recruitment, could reduce metabolic cost. Efficient neuronal signaling in the brain has been shown to correspond with energy minimization (Attwell and Laughlin, 2001;Hasenstaub et al, 2010). Information processing and neuronal signaling patterns also consume a large portion of the total energy used by the brain (Attwell and Laughlin, 2001;Magistretti, 2009).…”

Highly Energized Inhibitory Interneurons are a Central Element for Information Processing in Cortical Networks

“…However, energy-saving mechanisms that may partially compensate for the high ATP demand have been discussed for fast-spiking interneurons, i.e., fewer excitatory input sites, partially higher input resistance as well as expression of voltage-gated K þ -channels of the Kv3 subtype and rapidly inactivating Na þ -channels. 18,134,138,[140][141][142][143] Examples from the basal ganglia also suggest that high spiking rates of GABAergic neurons do not necessarily correlate with their energetic needs. 18 As long as we are lacking direct empirical evidence for the activity-dependent energy utilization of these interneurons, it will be difficult to decide how much energy is required to permit their fast-spiking behavior.…”

Neuronal energy consumption: biophysics, efficiency and evolution

“…When combined with details of molecular processes that occur within neurons, such as the structure of second messenger cascades, this approach can yield detailed energy budgets for neurons that quantify the consumption of specific processes. Moreover, when coupled with dynamic computational models [26,29,41], this approach can allow the energy consumption of neurons to be estimated on a fine temporal scale equivalent to that of the electrical signals within neurons themselves.…”

“…It was assumed that the energy consumption of this action was broadly representative of other action potentials [9]; however, this was dispelled by combining experimental measurements and computational modelling of a range of action potentials primarily from mammalian neurons [26][27][28][29]. This demonstrated that the squid giant axon action potential was profligate in its energy consumption compared to most other action potentials, and revealed a hitherto unappreciated heterogeneity in the biophysics of the currents generating action potentials and their consequences for the energy consumption of the action potential [25][26][27][28][29][30][31][32][33][34][35][36]. The major cause of differences in energy consumption was identified as the overlap between the inward and outward currents during the action potential [26,28,34,35]: A large overlap inflates energy consumption whereas complete separation of the currents reduces energy consumption close to the minimum possible.…”

“…This is not, however, their sole function; action potentials are also important for preventing noise accumulation in successive layers of information processing in neural circuits. The energy consumption of a single action potential within a single neuron would be challenging to measure directly, so typically it is estimated by converting the electrical signals into the total amount of work the 3Na + /2K + pump must do to restore ion gradients (Box 1) [9,[25][26][27][28][29][30][31][32][33][34][35][36]. Estimates of action potential energy consumption are, consequently, dependent upon accurate measurement of biophysical parameters including channel kinetics, conductance magnitudes and membrane capacitances.…”