Warm body temperature facilitates energy efficient cortical action potentials

Genomic Introgression and Adaptation of Southern Seabird Species Facilitate Recent Polar Colonization

Genomic adaptation and introgression can occur during the speciation process, enabling species to diverge in their frequencies of adaptive alleles or acquire new alleles that may promote adaptation to environmental changes. There is limited information on introgression in organisms from extreme environments and their responses to climate change. To address these questions, we focused on the 3 southern skua species, selected for their widespread distribution across the Southern Hemisphere and their complex history of speciation and introgression events. Our genomic data reveal that these skuas underwent diversification around the Penultimate Glacial Period, followed by subsequent demographic expansion. We identified a geographic region of introgression among species that followed a directional pattern sourced from the Antarctic continent, South America, and east to west in subantarctic islands, all converging towards the Antarctic Peninsula. The 3 skua species and admixed individuals exhibited a unique pattern of putative genes under selection, allowing adaptation to extreme conditions. Individuals with a higher proportion of Brown Skua ancestry showed signs of selection on genes related to reproductive isolation, while admixed individuals with a higher proportion of South Polar Skua ancestry displayed patterns resembling those of the South Polar Skua. Introgression may be a key mechanism of adaptation for many species that may help buffer against the ongoing climate change.

Cold induces brain region-selective cell activity-dependent lipid metabolism

It has been well documented that cold is an enhancer of lipid metabolism in peripheral tissues, yet its effect on central nervous system lipid dynamics is underexplored. It is well recognized that cold acclimations enhance adipocyte functions, including white adipose tissue lipid lipolysis and beiging, and brown adipose tissue thermogenesis in mammals. However, it remains unclear whether and how lipid metabolism in the brain is also under the control of ambient temperature. Here, we show that cold exposure predominantly increases the expressions of the lipid lipolysis genes and proteins within the paraventricular nucleus of the hypothalamus (PVH) in male mice. Mechanistically, by using innovatively combined brain-region selective pharmacology and in vivo time-lapse photometry monitoring of lipid metabolism, we find that cold activates cells within the PVH and pharmacological inactivation of cells blunts cold-induced effects on lipid peroxidation, accumulation of lipid droplets, and lipid lipolysis in the PVH. Together, these findings suggest that PVH lipid metabolism is cold sensitive and integral to cold-induced broader regulatory responses.

Rapid neurostimulation at the micron scale with an optically controlled thermal-capture technique

Precise control of cellular temperature at the microscale is crucial for developing novel neurostimulation techniques. Here, the effect of local heat on the electrophysiological properties of primary neuronal cultures and HEK293 cells at the subcellular level using a cutting-edge micrometer-scale thermal probe, the diamond heater-thermometer (DHT), is studied. A new mode of local heat action on a living cell, thermal-capture mode (TCM), is discovered using the DHT probe. In TCM, the application of a 50 °C temperature step induces a great increase in cellular response, allowing the cell to be thermally captured and depolarized by up to 20 mV. This thermal effect is attributed to local phase changes in the phospholipid membrane, enabling precise and reproducible modulation of cell activity. The TCM is shown to open up new opportunities for thermal cell stimulation. DHT reliably triggers action potentials (APs) in neurons at rates up to 30 Hz, demonstrating the ability to control cell excitability with millisecond and sub-millisecond resolution. AP shape is modulated by local heat as well. The ability to precisely control the AP shape and rate via thermal-capture mode opens new avenues for non-invasive, localized neurostimulation techniques, particularly in controlling neuron excitability.

A short natural history of mental time travels: a journey still travelled?

Tulving's introduction of episodic memory and the metaphor of mental time travel has immensely enriched our understanding of human cognition. However, his focus on human psychology, with limited consideration of evolutionary perspectives, led to the entrenched notion that mental time travel is uniquely human. We contend that adopting a phylogenetic perspective offers a deeper insight into cognition, revealing it as a continuous evolutionary process. Adherence to the uniqueness of pre-defined psychological concepts obstructs a more complete understanding. We offer a concise natural history to elucidate how events that occurred hundreds of millions of years ago have been pivotal for our ability to mentally time travel. We discuss how the human brain, utilizing parts with ancient origins in a networked manner, enables mental time travel. This underscores that episodic memories and mental time travel are not isolated mental constructs but integral to our perception and representation of the world. We conclude by examining recent evidence of neuroanatomical correlates found only in great apes, which show great variability, indicating the ongoing evolution of mental time travel in humans.This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Neural Processing without O2 and Glucose Delivery: Lessons from the Pond to the Clinic

Neuronal activity requires a large amount of ATP, leading to a rapid collapse of brain function when aerobic respiration fails. Here, we summarize how rhythmic motor circuits in the brain stem of adult frogs, which normally have high metabolic demands, transform to produce proper output during severe hypoxia associated with emergence from hibernation. We suggest that general principles underlying plasticity in brain bioenergetics may be uncovered by studying nonmammalian models that face extreme environments, yielding new insights to combat neurological disorders involving dysfunctional energy metabolism.

Cold induces brain region-selective cell activity-dependent lipid metabolism

It has been well documented that cold is an enhancer of lipid metabolism in peripheral tissues, yet its effect on central nervous system lipid dynamics is underexplored. It is well recognized that cold acclimations enhance adipocyte functions, including white adipose tissue (WAT) lipid lipolysis and beiging, and brown adipose tissue (BAT) thermogenesis in mammals. However, it remains unclear whether and how lipid metabolism in the brain is also under the control of cold acclimations. Here, we show that cold exposure predominantly increases the expressions of the lipid lipolysis genes and proteins within the paraventricular nucleus of the hypothalamus (PVH). Mechanistically, we find that cold activates cells within the PVH and pharmacological inactivation of cells blunts cold-induced effects on lipid peroxidation, accumulation of lipid droplets (LDs), and lipolysis in the PVH. Together, these findings suggest that PVH lipid metabolism is cold sensitive and integral to cold-induced broader regulatory responses.

Thermal effects and ephaptic entrainment in Hodgkin-Huxley model

The brain is understood as an intricate biological system composed of numerous elements. It is susceptible to various physical and chemical influences, including temperature. The literature extensively explores the conditions that influence synapses in the context of cellular communication. However, the understanding of how the brain's global physical conditions can modulate ephaptic communication remains limited due to the poorly understood nature of ephapticity. This study proposes an adaptation of the Hodgkin and Huxley (HH) model to investigate the effects of ephaptic entrainment in response to thermal changes (HH-E). The analysis focuses on two distinct neuronal regimes: subthreshold and suprathreshold. In the subthreshold regime, circular statistics are used to demonstrate the dependence of phase differences with temperature. In the suprathreshold regime, the Inter-Spike Interval are employed to estimate phase preferences and changes in the spiking pattern. Temperature influences the model's ephaptic interactions and can modify its preferences for spiking frequency, with the direction of this change depending on specific model conditions and the temperature range under consideration. Furthermore, temperature enhance the anti-phase differences relationship between spikes and the external ephaptic signal. In the suprathreshold regime, ephaptic entrainment is also influenced by temperature, especially at low frequencies. This study reveals the susceptibility of ephaptic entrainment to temperature variations in both subthreshold and suprathreshold regimes and discusses the importance of ephaptic communication in the contexts where temperature may plays a significant role in neural physiology, such as inflammatory processes, fever, and epileptic seizures.

Infrared neuromodulation-a review

Infrared (IR) neuromodulation (INM) is an emerging light-based neuromodulation approach that can reversibly control neuronal and muscular activities through the transient and localized deposition of pulsed IR light without requiring any chemical or genetic pre-treatment of the target cells. Though the efficacy and short-term safety of INM have been widely demonstrated in both peripheral and central nervous systems, the investigations of the detailed cellular and biological processes and the underlying biophysical mechanisms are still ongoing. In this review, we discuss the current research progress in the INM field with a focus on the more recently discovered IR nerve inhibition. Major biophysical mechanisms associated with IR nerve stimulation are summarized. As the INM effects are primarily attributed to the spatiotemporal thermal transients induced by water and tissue absorption of pulsed IR light, temperature monitoring techniques and simulation models adopted in INM studies are discussed. Potential translational applications, current limitations, and challenges of the field are elucidated to provide guidance for future INM research and advancement.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Interdependence of cellular and network properties in respiratory rhythm generation

How breathing is generated by the preBötzinger complex (preBötC) remains divided between two ideological frameworks, and a persistent sodium current (INaP) lies at the heart of this debate. Although INaP is widely expressed, the pacemaker hypothesis considers it essential because it endows a small subset of neurons with intrinsic bursting or "pacemaker" activity. In contrast, burstlet theory considers INaP dispensable because rhythm emerges from "preinspiratory" spiking activity driven by feed-forward network interactions. Using computational modeling, we find that small changes in spike shape can dissociate INaP from intrinsic bursting. Consistent with many experimental benchmarks, conditional effects on spike shape during simulated changes in oxygenation, development, extracellular potassium, and temperature alter the prevalence of intrinsic bursting and preinspiratory spiking without altering the role of INaP. Our results support a unifying hypothesis where INaP and excitatory network interactions, but not intrinsic bursting or preinspiratory spiking, are critical interdependent features of preBötC rhythmogenesis.

Why birds are smart

Many cognitive neuroscientists believe that both a large brain and an isocortex are crucial for complex cognition. Yet corvids and parrots possess non-cortical brains of just 1-25 g, and these birds exhibit cognitive abilities comparable with those of great apes such as chimpanzees, which have brains of about 400 g. This opinion explores how this cognitive equivalence is possible. We propose four features that may be required for complex cognition: a large number of associative pallial neurons, a prefrontal cortex (PFC)-like area, a dense dopaminergic innervation of association areas, and dynamic neurophysiological fundaments for working memory. These four neural features have convergently evolved and may therefore represent 'hard to replace' mechanisms enabling complex cognition.

Interdependence of cellular and network properties in respiratory rhythmogenesis

How breathing is generated by the preBötzinger Complex (preBötC) remains divided between two ideological frameworks, and the persistent sodium current (INaP) lies at the heart of this debate. Although INaP is widely expressed, the pacemaker hypothesis considers it essential because it endows a small subset of neurons with intrinsic bursting or "pacemaker" activity. In contrast, burstlet theory considers INaP dispensable because rhythm emerges from "pre-inspiratory" spiking activity driven by feed-forward network interactions. Using computational modeling, we discover that changes in spike shape can dissociate INaP from intrinsic bursting. Consistent with many experimental benchmarks, conditional effects on spike shape during simulated changes in oxygenation, development, extracellular potassium, and temperature alter the prevalence of intrinsic bursting and pre-inspiratory spiking without altering the role of INaP. Our results support a unifying hypothesis where INaP and excitatory network interactions, but not intrinsic bursting or pre-inspiratory spiking, are critical interdependent features of preBötC rhythmogenesis.

Thermal Management in Neuromorphic Materials, Devices, and Networks

Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine-learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware-based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy-efficient implementation of neuromorphic devices are considered. The main features of brain-inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching-based neuromorphic devices are discussed, the energy and electrical considerations for spiking-based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy-efficient computing are described.

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

GABABR silencing of nerve terminals

Control of neurotransmission efficacy is central to theories of how the brain computes and stores information. Presynaptic G-protein coupled receptors (GPCRs) are critical in this problem as they locally influence synaptic strength and can operate on a wide range of time scales. Among the mechanisms by which GPCRs impact neurotransmission is by inhibiting voltage-gated calcium (Ca2+) influx in the active zone. Here, using quantitative analysis of both single bouton Ca2+ influx and exocytosis, we uncovered an unexpected non-linear relationship between the magnitude of action potential driven Ca2+ influx and the concentration of external Ca2+ ([Ca2+]e). We find that this unexpected relationship is leveraged by GPCR signaling when operating at the nominal physiological set point for [Ca2+]e, 1.2 mM, to achieve complete silencing of nerve terminals. These data imply that the information throughput in neural circuits can be readily modulated in an all-or-none fashion at the single synapse level when operating at the physiological set point.

Enhanced excitability but mature action potential waveforms at mossy fiber terminals of young, adult-born hippocampal neurons in mice

Adult-born granule neurons pass through immature critical periods where they display enhanced somatic excitability and afferent plasticity, which is believed to endow them with unique roles in hippocampal learning and memory. Using patch clamp recordings in mouse hippocampal slices, here we show that young neuron hyper-excitability is also observed at presynaptic mossy fiber terminals onto CA3 pyramidal neurons. However, action potential waveforms mature faster in the bouton than in the soma, suggesting rapid efferent functionality during immature stages.

Energy-efficiency computing of up and down transitions in a neural network

Spontaneous periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity of slow-wave sleep. Previous theoretical studies have shown that stimulation frequency and the dynamics of intrinsic currents have a major influence on synchronicity and firing rate of spontaneous fluctuation. Energy consumption is driven by internal spontaneous activity. However, its energy consumption and energy efficiency are not clear. Therefore, this article simulates the up and down transitions based on a neural network and discusses the energy consumption and energy efficiency. It is found that the dynamics of intrinsic currents have a great impact on the energy consumption and energy efficiency in the process. The energy consumption is influenced by the size of the period and the average power consumption of the state. The average power consumption by the up state is always greater than the consumption by the down state, and the energy consumption of the transition is more than firing. In addition, the lower average proportion of duration of the up state in the cycle leads to higher energy efficiency. Energy consumption is reduced and energy efficiency is enhanced by adjusting parameters of the network. The study helps us to understand and further explore the metabolic consumption of spontaneous activities.NEW & NOTEWORTHY We use a more biological neural network to explore energy consumption and energy efficiency of up and down transitions. Specifically, we find that average energy consumption is more than that caused by action potentials, which proves that metabolic consumption is acquired substantially in the resting state as well. We also find that energy efficiency is influenced by the proportion of duration of the up state in the cycle. These findings may further improve the economy of the nervous system.

Energy efficiency and coding of neural network

Based on the Hodgkin-Huxley model, this study explored the energy efficiency of BA network, ER network, WS network, and Caenorhabditis elegans neural network, and explained the development of neural network structure in the brain from the perspective of energy efficiency using energy coding theory. The numerical simulation results showed that the BA network had higher energy efficiency, which was closer to that of the C. elegans neural network, indicating that the neural network in the brain had scale-free property because of satisfying high energy efficiency. In addition, the relationship between the energy consumption of neural networks and synchronization was established by applying energy coding. The stronger the neural network synchronization was, the less energy the network consumed.

A review of the bioeffects of low-intensity focused ultrasound and the benefits of a cellular approach

This review article highlights the historical developments and current state of knowledge of an important neuromodulation technology: low-intensity focused ultrasound. Because compelling studies have shown that focused ultrasound can modulate neuronal activity non-invasively, especially in deep brain structures with high spatial specificity, there has been a renewed interest in attempting to understand the specific bioeffects of focused ultrasound at the cellular level. Such information is needed to facilitate the safe and effective use of focused ultrasound to treat a number of brain and nervous system disorders in humans. Unfortunately, to date, there appears to be no singular biological mechanism to account for the actions of focused ultrasound, and it is becoming increasingly clear that different types of nerve cells will respond to focused ultrasound differentially based on the complement of their ion channels, other membrane biophysical properties, and arrangement of synaptic connections. Furthermore, neurons are apparently not equally susceptible to the mechanical, thermal and cavitation-related consequences of focused ultrasound application-to complicate matters further, many studies often use distinctly different focused ultrasound stimulus parameters to achieve a reliable response in neural activity. In this review, we consider the benefits of studying more experimentally tractable invertebrate preparations, with an emphasis on the medicinal leech, where neurons can be studied as unique individual cells and be synaptically isolated from the indirect effects of focused ultrasound stimulation on mechanosensitive afferents. In the leech, we have concluded that heat is the primary effector of focused ultrasound neuromodulation, especially on motoneurons in which we observed a focused ultrasound-mediated blockade of action potentials. We discuss that the mechanical bioeffects of focused ultrasound, which are frequently described in the literature, are less reliably achieved as compared to thermal ones, and that observations ascribed to mechanical responses may be confounded by activation of synaptically-coupled sensory structures or artifacts associated with electrode resonance. Ultimately, both the mechanical and thermal components of focused ultrasound have significant potential to contribute to the sculpting of specific neural outcomes. Because focused ultrasound can generate significant modulation at a temperature <5°C, which is believed to be safe for moderate durations, we support the idea that focused ultrasound should be considered as a thermal neuromodulation technology for clinical use, especially targeting neural pathways in the peripheral nervous system.

Avian neurons consume three times less glucose than mammalian neurons

Brains are among the most energetically costly tissues in the mammalian body.¹ This is predominantly caused by expensive neurons with high glucose demands.² Across mammals, the neuronal energy budget appears to be fixed, possibly posing an evolutionary constraint on brain growth.^3-6 Compared to similarly sized mammals, birds have higher numbers of neurons, and this advantage conceivably contributes to their cognitive prowess.⁷ We set out to determine the neuronal energy budget of birds to elucidate how they can metabolically support such high numbers of neurons. We estimated glucose metabolism using positron emission tomography (PET) and 2-[¹⁸F]fluoro-2-deoxyglucose ([¹⁸F]FDG) as the radiotracer in awake and anesthetized pigeons. Combined with kinetic modeling, this is the gold standard to quantify cerebral metabolic rate of glucose consumption (CMR_glc).⁸ We found that neural tissue in the pigeon consumes 27.29 ± 1.57 μmol glucose per 100 g per min in an awake state, which translates into a surprisingly low neuronal energy budget of 1.86 × 10^-9 ± 0.2 × 10^-9 μmol glucose per neuron per minute. This is approximately 3 times lower than the rate in the average mammalian neuron.³ The remarkably low neuronal energy budget explains how pigeons, and possibly other avian species, can support such high numbers of neurons without associated metabolic costs or compromising neuronal signaling. The advantage in neuronal processing of information at a higher efficiency possibly emerged during the distinct evolution of the avian brain.

Response of a neuronal network computational model to infrared neural stimulation

Infrared neural stimulation (INS), as a novel form of neuromodulation, allows modulating the activity of nerve cells through thermally induced capacitive currents and thermal sensitivity ion channels. However, fundamental questions remain about the exact mechanism of INS and how the photothermal effect influences the neural response. Computational neural modeling can provide a powerful methodology for understanding the law of action of INS. We developed a temperature-dependent model of ion channels and membrane capacitance based on the photothermal effect to quantify the effect of INS on the direct response of individual neurons and neuronal networks. The neurons were connected through excitatory and inhibitory synapses and constituted a complex neuronal network model. Our results showed that a slight increase in temperature promoted the neuronal spikes and enhanced network activity, whereas the ultra-temperature inhibited neuronal activity. This biophysically based simulation illustrated the optical dose-dependent biphasic cell response with capacitive current as the core change condition. The computational model provided a new sight to elucidate mechanisms and inform parameter selection of INS.

Exploring the ovine sperm transcriptome by RNAseq techniques. I Effect of seasonal conditions on transcripts abundance

Understanding the cell molecular changes occurring as a results of climatic circumstances is crucial in the current days in which climate change and global warming are one of the most serious challenges that living organisms have to face. Sperm are one of the mammals’ cells most sensitive to heat, therefore evaluating the impact of seasonal changes in terms of its transcriptional activity can contribute to elucidate how these cells cope with heat stress events. We sequenced the total sperm RNA from 64 ejaculates, 28 collected in summer and 36 collected in autumn, from 40 Manchega rams. A highly rich transcriptome (11,896 different transcripts) with 90 protein coding genes that exceed an average number of 5000 counts were found. Comparing transcriptome in the summer and autumn ejaculates, 236 significant differential abundance genes were assessed, most of them (228) downregulated. The main functions that these genes are related to sexual reproduction and negative regulation of protein metabolic processes and kinase activity. Sperm response to heat stress supposes a drastic decrease of the transcriptional activity, and the upregulation of only a few genes related with the basic functions to maintain the organisms’ homeostasis and surviving. Rams’ spermatozoids carry remnant mRNAs which are retrospectively indicators of events occurring along the spermatogenesis process, including abiotic factors such as environmental temperature.

Resurgent Na+ currents promote ultrafast spiking in projection neurons that drive fine motor control

The underlying mechanisms that promote precise spiking in upper motor neurons controlling fine motor skills are not well understood. Here we report that projection neurons in the adult zebra finch song nucleus RA display robust high-frequency firing, ultra-narrow spike waveforms, superfast Na⁺ current inactivation kinetics, and large resurgent Na⁺ currents (I_NaR). These properties of songbird pallial motor neurons closely resemble those of specialized large pyramidal neurons in mammalian primary motor cortex. They emerge during the early phases of song development in males, but not females, coinciding with a complete switch of Na+ channel subunit expression from Navβ3 to Navβ4. Dynamic clamping and dialysis of Navβ4's C-terminal peptide into juvenile RA neurons provide evidence that Navβ4, and its associated I_NaR, promote neuronal excitability. We thus propose that I_NaR modulates the excitability of upper motor neurons that are required for the execution of fine motor skills.

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.

A dynamics model of neuron-astrocyte network accounting for febrile seizures

Febrile seizure (FS) is a full-body convulsion caused by a high body temperature that affect young kids, however, how these most common of human seizures are generated by fever has not been known. One common observation is that cortical neurons become overexcited with abnormal running of sodium and potassium ions cross membrane in raised body temperature condition, Considering that astrocyte Kir4.1 channel play a critical role in maintaining extracellular homeostasis of ionic concentrations and electrochemical potentials of neurons by fast depletion of extracellular potassium ions, we examined here the potential role of temperature-dependent Kir4.1 channel in astrocytes in causing FS. We first built up a temperature-dependent computational model of the Kir4.1 channel in astrocytes and validated with experiments. We have then built up a neuron-astrocyte network and examine the role of the Kir4.1 channel in modulating neuronal firing dynamics as temperature increase. The numerical experiment demonstrated that the Kir4.1 channel function optimally in the body temperature around 37 °C in cleaning 'excessive' extracellular potassium ions during neuronal firing process, however, higher temperature deteriorates its cleaning function, while lower temperature slows down its cleaning efficiency. With the increase of temperature, neurons go through different stages of spiking dynamics from spontaneous slow oscillations, to tonic spiking, fast bursting oscillations, and eventually epileptic bursting. Thus, our study may provide a potential new mechanism that febrile seizures may be happened due to temperature-dependent functional disorders of Kir4.1 channel in astrocytes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09706-w.

Sparsely Wiring Connectivity in the Upper Beta Band Characterizes the Brains of Top Swimming Athletes

Human brains are extremely energy costly in neural connections and activities. However, it is unknown what is the difference in the brain connectivity between top athletes with long-term professional trainings and age-matched controls. Here we ask whether long-term training can lower brain-wiring cost while have better performance. Since elite swimming requires athletes to move their arms and legs at different tempos in time with high coordination skills, we selected an eye-hand-foot complex reaction (CR) task to examine the relations between the task performance and the brain connections and activities, as well as to explore the energy cost-efficiency of top athletes. Twenty-one master-level professional swimmers and 23 age-matched non-professional swimmers as controls were recruited to perform the CR task with concurrent 8-channel EEG recordings. Reaction time and accuracy of the CR task were recorded. Topological network analysis of various frequency bands was performed using the phase lag index (PLI) technique to avoid volume conduction effects. The wiring number of connections and mean frequency were calculated to reflect the wiring and activity cost, respectively. Results showed that professional athletes demonstrated better eye-hand-foot coordination than controls when performing the CR task, indexing by faster reaction time and higher accuracy. Comparing to controls, athletes' brain demonstrated significantly less connections and weaker correlations in upper beta frequency band between the frontal and parietal regions, while demonstrated stronger connectivity in the low theta frequency band between sites of F3 and Cz/C4. Additionally, athletes showed highly stable and low eye-blinking rates across different reaction performance, while controls had high blinking frequency with high variance. Elite athletes' brain may be characterized with energy efficient sparsely wiring connections in support of superior motor performance and better cognitive performance in the eye-hand-foot complex reaction task.

Frequency-Dependent Energy Demand of Dendritic Responses to Deep Brain Stimulation in Thalamic Neurons: A Model-Based Study

Thalamic deep brain stimulation (DBS) generates excitatory postsynaptic currents and action potentials (APs) by triggering large numbers of synaptic inputs to local cells, which also activates axonal spikes to antidromically invade the soma and dendrites. To maintain signaling, the evoked dendritic responses require metabolic energy to restore ion gradients in each dendrite. The objective of this study is to estimate the energy demand associated with dendritic responses to thalamic DBS. We use a morphologically realistic computational model to simulate dendritic activity in thalamocortical (TC) relay neurons with axonal intracellular stimulation or DBS-like extracellular stimulation. We determine the metabolic cost by calculating the number of adenosine triphosphate (ATP) expended to pump Na⁺ and Ca²⁺ ions out of each dendrite. The ATP demand of dendritic activity exhibits frequency dependence, which is determined by the number of spikes in the dendrites. Each backpropagating AP from the soma activates a spike in the dendrites, and the dendritic firing is dominated by antidromic activation of the soma. High stimulus frequencies decrease dendritic ATP cost by reducing the fidelity of antidromic activation. Synaptic inputs and stimulus-induced polarization govern the ATP cost of dendritic responses by facilitating/suppressing antidromic activation, which also influences the ATP cost by depolarizing/hyperpolarizing each dendrite. These findings are important for understanding the synaptic signaling energy in TC relay neurons and metabolism-dependent functional imaging data of thalamic DBS.

Less is more: Wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing

The brain network is notably cost-efficient, while the fundamental physical and dynamic mechanisms underlying its economical optimization in network structure and activity have not been determined. In this study, we investigate the intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We observe that critical avalanche states from excitation-inhibition balance under modular network topology with less wiring cost can also achieve lower costs in firing but with strongly enhanced response sensitivity to stimuli. We derive mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanches is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights into brain-inspired efficient computational designs.

Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis

OBJECTIVE

Measuring neuronal cell activity using microelectrode arrays reveals a great variety of derived signal shapes within extracellular recordings. However, possible mechanisms responsible for this variety have not yet been entirely determined, which might hamper any subsequent analysis of the recorded neuronal data.

METHODS

To investigate this issue, we propose a computational model based on the finite element method describing the electrical coupling between an electrically active neuron and an extracellular recording electrode in detail. This allows for a systematic study of possible parameters that may play an essential role in defining or altering the shape of the measured electrode potential.

RESULTS

Our results indicate that neuronal geometry, neurite structure, as well as the actual pathways of input potentials that evoke action potential generation, have a significant impact on the shape of the resulting extracellular electrode recording and explain most of the known variations of signal shapes.

CONCLUSION

The presented models offer a comprehensive insight into the effect of geometrical and morphological factors on the resulting electrode signal.

SIGNIFICANCE

Computational modeling complemented with experimental measurements shows much promise to yield meaningful insights into the electrical activity of a neuronal network.

Information processing and energy efficiency of temperature-sensitive Morris-Lecar neuron

In biological organisms, the temperature is an important factor, which affects the motion of micro-particles and biochemical reaction. In current work, we investigate the effect of temperature on the capacity of information processing and the energy efficiency of the Hodgkin's three basic classes of neurons. Increasing the temperature, both of the total entropy and information rate would maintain nearly as constant, and then decrease rapidly and fall to zero. Moreover, energy consumption is reduced as the temperature increases. However, the energy consumption of the class 1 neuron is remarkably greater than that of class 2 and 3 neurons with the same temperature. It is also interesting that the class 3 neuron consumes less energy than that of class 1 and 2 neurons, but generates the same value of total entropy and information rate for the same condition.

Dynamic Recovery from Depression Enables Rate Encoding in Inhibitory Synapses

Parvalbumin-expressing fast-spiking interneurons (PV-INs) control network firing and the gain of cortical response to sensory stimulation. Crucial for these functions, PV-INs can sustain high-frequency firing with no accommodation. However, PV-INs also exhibit short-term depression (STD) during sustained activation, largely due to the depletion of synaptic resources (vesicles). In most synapses the rate of replenishment of depleted vesicles is constant, determining an inverse relationship between depression levels and the activation rate, which theoretically, severely limits rate-coding capabilities. We examined STD of the PV-IN to pyramidal cell synapse in the mouse visual cortex and found that in these synapses the recovery from depression is not constant but increases linearly with the frequency of use. By combining modeling, dynamic clamp, and optogenetics, we demonstrated that this recovery enables PV-INs to reduce pyramidal cell firing in a linear manner, which theoretically is crucial for controlling the gain of cortical visual responses.

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Recovery rate from depression in inhibitory synapses from PV-INs is use dependent

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Dynamic recovery from depression enables rate coding in inhibitory inputs

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PV-IN synapses reduce pyramidal firing in a frequency-dependent manner

Infrared inhibition and waveform modulation of action potentials in the crayfish motor axon

The infrared (IR) inhibition of axonal activities in the crayfish neuromuscular preparation is studied using 2 µm IR light pulses with varying durations. The intracellular neuronal activities are monitored with two-electrode current clamp, while the IR-induced temperature changes are measured by the open patch technique simultaneously. It is demonstrated that the IR pulses can reversibly shape or block locally initiated action potentials. Suppression of the AP amplitude and duration and decrease in axonal excitability by IR pulses are quantitatively analyzed. While the AP amplitude and duration decrease similarly during IR illumination, it is discovered that the recovery of the AP duration after the IR pulses is slower than that of the AP amplitude. An IR-induced decrease in the input resistance (8.8%) is detected and discussed together with the temperature dependent changes in channel kinetics as contributing factors for the inhibition reported here.

Metabolic Cost of Dendritic Ca2+ Action Potentials in Layer 5 Pyramidal Neurons

Pyramidal neurons consume most signaling-related energy to generate action potentials (APs) and perform synaptic integration. Dendritic Ca²⁺ spike is an important integration mechanism for coupling inputs from different cortical layers. Our objective was to quantify the metabolic energy associated with the generation of Ca²⁺ APs in the dendrites. We used morphology-based computational models to simulate the dendritic Ca²⁺ spikes in layer 5 pyramidal neurons. We calculated the energy cost by converting Ca²⁺ influx into the number of ATP required to restore and maintain the homeostasis of intracellular Ca²⁺ concentrations. We quantified the effects of synaptic inputs, dendritic voltage, back-propagating Na⁺ spikes, and Ca²⁺ inactivation on Ca²⁺ spike cost. We showed that much more ATP molecules were required for reversing Ca²⁺ influx in the dendrites than for Na⁺ ion pumping in the soma during a Ca²⁺ AP. Increasing synaptic input increased the rate of dendritic depolarization and underlying Ca²⁺ influx, resulting in higher ATP consumption. Depolarizing dendritic voltage resulted in the inactivation of Ca²⁺ channels and reduced the ATP cost, while dendritic hyperpolarization increased the spike cost by de-inactivating Ca²⁺ channels. A back-propagating Na⁺ AP initiated in the soma increased Ca²⁺ spike cost in the apical dendrite when it coincided with a synaptic input within a time window of several milliseconds. Increasing Ca²⁺ inactivation rate reduced Ca²⁺ spike cost, while slowing Ca²⁺ inactivation increased the spike cost. The results revealed that the energy demand of a Ca²⁺ AP was dynamically dependent on the state of dendritic activity. These findings were important for predicting the energy budget for signaling in pyramidal cells, interpreting functional imaging data, and designing energy-efficient neuromorphic devices.

Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases

The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.

Complementary Tuning of Na+ and K+ Channel Gating Underlies Fast and Energy-Efficient Action Potentials in GABAergic Interneuron Axons

Fast-spiking, parvalbumin-expressing GABAergic interneurons (PV⁺-BCs) express a complex machinery of rapid signaling mechanisms, including specialized voltage-gated ion channels to generate brief action potentials (APs). However, short APs are associated with overlapping Na⁺ and K⁺ fluxes and are therefore energetically expensive. How the potentially vicious combination of high AP frequency and inefficient spike generation can be reconciled with limited energy supply is presently unclear. To address this question, we performed direct recordings from the PV⁺-BC axon, the subcellular structure where active conductances for AP initiation and propagation are located. Surprisingly, the energy required for the AP was, on average, only ∼1.6 times the theoretical minimum. High energy efficiency emerged from the combination of fast inactivation of Na⁺ channels and delayed activation of Kv3-type K⁺ channels, which minimized ion flux overlap during APs. Thus, the complementary tuning of axonal Na⁺ and K⁺ channel gating optimizes both fast signaling properties and metabolic efficiency.

The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals

What type of principle features intrinsic inside of the fluctuated input signals could drive neurons with the maximal excitations is one of the crucial neural coding issues. In this article, we examined both experimentally and theoretically the cortical neuronal responsivity (including firing rate and spike timing reliability) to input signals with different intrinsic correlational statistics (e.g., white-type noise, showed 1/f0 power spectrum, pink noise 1/f, and brown noises 1/f2) and different frequency ranges. Our results revealed that the response sensitivity and reliability of cortical neurons is much higher in response to 1/f noise stimuli with long-term correlations than 1/f0 with short-term correlations for a broad frequency range, and also higher than 1/f2 for all frequency ranges. In addition, we found that neuronal sensitivity diverges to opposite directions for 1/f noise comparing with 1/f0 white noise as a function of cutoff frequency of input signal. As the cutoff frequency is progressively increased from 50 to 1,000 Hz, the neuronal responsiveness increased gradually for 1/f noise, while decreased exponentially for white noise. Computational simulations of a general cortical model revealed that, neuronal sensitivity and reliability to input signal statistics was majorly dominated by fast sodium inactivation, potassium activation, and membrane time constants.

Homeostasis: How Neurons Achieve Temperature Invariance

Temperature influences physiological processes and can corrupt nervous system function. A modelling study shows how regulation of ion channel expression can establish an acute temperature invariance of neuronal responses despite temperature-dependent and variable ionic conductances.

Ion Channels and Thermosensitivity: TRP, TREK, or Both?

Controlling body temperature is a matter of life or death for most animals, and in mammals the complex thermoregulatory system is comprised of thermoreceptors, thermosensors, and effectors. The activity of thermoreceptors and thermoeffectors has been studied for many years, yet only recently have we begun to obtain a clear picture of the thermosensors and the molecular mechanisms involved in thermosensory reception. An important step in this direction was the discovery of the thermosensitive transient receptor potential (TRP) cationic channels, some of which are activated by increases in temperature and others by a drop in temperature, potentially converting the cells in which they are expressed into heat and cold receptors. More recently, the TWIK-related potassium (TREK) channels were seen to be strongly activated by increases in temperature. Hence, in this review we want to assess the hypothesis that both these groups of channels can collaborate, possibly along with other channels, to generate the wide range of thermal sensations that the nervous system is capable of handling.

Average firing rate rather than temporal pattern determines metabolic cost of activity in thalamocortical relay neurons

Thalamocortical (TC) relay cells exhibit different temporal patterns of activity, including tonic mode and burst mode, to transmit sensory information to the cortex. Our aim was to quantify the metabolic cost of different temporal patterns of neural activity across a range of average firing rates. We used a biophysically-realistic model of a TC relay neuron to simulate tonic and burst patterns of firing. We calculated the metabolic cost by converting the calculated ion fluxes into the demand for ATP to maintain homeostasis of intracellular ion concentrations. Most energy was expended on reversing Na⁺ entry during action potentials and pumping Ca²⁺ out of the cell. Average firing rate determined the ATP cost across firing patterns by controlling the overall number of spikes. Varying intraburst frequency or spike number in each burst influenced the metabolic cost by altering the interactions of inward and outward currents on multiple timescales, but temporal pattern contributed substantially less to the metabolic demand of neural activity as compared to average firing rate. These predictions should be considered when interpreting findings of functional imaging studies that rely of estimates of neuronal metabolic demand, e.g., functional magnetic resonance imaging.

Energy Cost of Action Potential Generation and Propagation in Thalamocortical Relay Neurons During Deep Brain Stimulation

Thalamocortical (TC) relay neurons generate antidromic and orthodromic action potentials (APs) during thalamic deep brain stimulation (DBS). To maintain signaling, each AP requires Na⁺/K⁺ pump to expend adenosine triphosphate (ATP) to restore Na⁺ and K⁺ gradients. Our aim was to estimate the energy demand associated with AP generation and propagation within TC relay cells during DBS. We used a morphology-based computational model to simulate the APs at different locations. We determined AP energy cost by calculating the amount of ATP required to reverse Na⁺ influx during the spike and measured metabolic efficiency by using Na⁺/K⁺ charge overlap. The ATP cost for AP generation exhibited location dependence, which was determined by spike shape, spatial morphology, and heterogeneously distributed currents. The APs in the axonal initial segment (AIS) were energetically efficient, but backpropagation to the soma and forward propagation to the axon were inefficient. Due to large surface area, the soma and AIS dominated the overall ATP usage. The AP cost also depended on membrane potential, which controlled T-type Ca²⁺ conductance and degree of availability of Na⁺ and K⁺ channels. The excitatory/inhibitory synaptic inputs affected spike cost by increasing/reducing the excitability of local cells. There was a tradeoff between AP cost and firing rate at high firing frequencies. We explained a fundamental link between biophysics of ionic currents, spatial morphology of neural segments, and ATP cost per AP. The predictions should be considered when understanding the functional magnetic resonance imaging data of thalamic DBS.

Constraints of Metabolic Energy on the Number of Synaptic Connections of Neurons and the Density of Neuronal Networks

Neuronal networks in the brain are the structural basis of human cognitive function, and the plasticity of neuronal networks is thought to be the principal neural mechanism underlying learning and memory. Dominated by the Hebbian theory, researchers have devoted extensive effort to studying the changes in synaptic connections between neurons. However, understanding the network topology of all synaptic connections has been neglected over the past decades. Furthermore, increasing studies indicate that synaptic activities are tightly coupled with metabolic energy, and metabolic energy is a unifying principle governing neuronal activities. Therefore, the network topology of all synaptic connections may also be governed by metabolic energy. Here, by implementing a computational model, we investigate the general synaptic organization rules for neurons and neuronal networks from the perspective of energy metabolism. We find that to maintain the energy balance of individual neurons in the proposed model, the number of synaptic connections is inversely proportional to the average of the synaptic weights. This strategy may be adopted by neurons to ensure that the ability of neurons to transmit signals matches their own energy metabolism. In addition, we find that the density of neuronal networks is also an important factor in the energy balance of neuronal networks. An abnormal increase or decrease in the network density could lead to failure of energy metabolism in the neuronal network. These rules may change our view of neuronal networks in the brain and have guiding significance for the design of neuronal network models.

On Energy Efficiency and the Brain's Resistance to Change: The Neurological Evolution of Dogmatism and Close-Mindedness

The brain has been described as very resistant to change. However, why it does this has not been fully explained. In this paper, I propose that resilience to the disruption of consolidated memory is at the heart of the brain's resistance to change, and this resilience is a consequence of its energy efficiency evolutionary adaptations. I discussed the implications of this energy efficiency adaptation on dogmatism, close-mindedness, and artificial intelligence.

Evaluating the gray and white matter energy budgets of human brain function

The insatiable appetite for energy to support human brain function is mainly supplied by glucose oxidation (CMR_glc(ox)). But how much energy is consumed for signaling and nonsignaling processes in gray/white matter is highly debated. We examined this issue by combining metabolic measurements of gray/white matter and a theoretical calculation of bottom-up energy budget using biophysical properties of neuronal/glial cells in conjunction with species-exclusive electrophysiological and morphological data. We calculated a CMR_glc(ox)-derived budget and confirmed it with experimental results measured by PET, autoradiography, ¹³C-MRS, and electrophysiology. Several conserved principles were observed regarding the energy costs for brain's signaling and nonsignaling components in both human and rat. The awake resting cortical signaling processes and mass-dependent nonsignaling processes, respectively, demand ∼70% and ∼30% of CMR_glc(ox). Inhibitory neurons and glia need 15-20% of CMR_glc(ox), with the rest demanded by excitatory neurons. Nonsignaling demands dominate in white matter, in near opposite contrast to gray matter demands. Comparison between ¹³C-MRS data and calculations suggests ∼1.2 Hz glutamatergic signaling rate in the awake human cortex, which is ∼4 times lower than signaling in the rat cortex. Top-down validated bottom-up budgets could allow computation of anatomy-based CMR_glc(ox) maps and accurate cellular level interpretation of brain metabolic imaging.

Computational modeling of the effect of temperature variations on human pancreatic β-cell activity

The effect of temperature variations on the pancreatic β-cell activity and the role of different model compartments in temperature sensing have been investigated using a computational modeling approach. The results of our study show that temperature variations by several degrees can change the dynamical states of the β-cell system. In addition, temperature variations can alter the characteristic features of the membrane voltage, which correlates with insulin secretion. Simulation results show that the ion channels such as the L-type calcium, the hERG potassium, sodium channels and the glycolysis pathway are the possible sites for sensing temperature variation. Results indicate that for a small temperature change, even though the frequency and amplitude of electrical activity are altered, the area under the membrane potential curve remains almost unchanged, which implies the existence of a thermoregulatory mechanism for preserving the amount of insulin secretion. Furthermore, the computational analysis shows that the β-cell electrical activity exhibits a bursting pattern in physiological temperature (37 °C) while in vitro studies reported almost the spiking activity at lower temperatures. Since hormone-secreting systems work more efficient in bursting mode, we propose that the pancreatic β-cell works better in the physiological temperature compared with the reference temperature (33 °C).

Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network

Selective pressure may drive neural systems to process as much information as possible with the lowest energy cost. Recent experiment evidence revealed that the ratio between synaptic excitation and inhibition (E/I) in local cortex is generally maintained at a certain value which may influence the efficiency of energy consumption and information transmission of neural networks. To understand this issue deeply, we constructed a typical recurrent Hodgkin-Huxley network model and studied the general principles that governs the relationship among the E/I synaptic current ratio, the energy cost and total amount of information transmission. We observed in such a network that there exists an optimal E/I synaptic current ratio in the network by which the information transmission achieves the maximum with relatively low energy cost. The coding energy efficiency which is defined as the mutual information divided by the energy cost, achieved the maximum with the balanced synaptic current. Although background noise degrades information transmission and imposes an additional energy cost, we find an optimal noise intensity that yields the largest information transmission and energy efficiency at this optimal E/I synaptic transmission ratio. The maximization of energy efficiency also requires a certain part of energy cost associated with spontaneous spiking and synaptic activities. We further proved this finding with analytical solution based on the response function of bistable neurons, and demonstrated that optimal net synaptic currents are capable of maximizing both the mutual information and energy efficiency. These results revealed that the development of E/I synaptic current balance could lead a cortical network to operate at a highly efficient information transmission rate at a relatively low energy cost. The generality of neuronal models and the recurrent network configuration used here suggest that the existence of an optimal E/I cell ratio for highly efficient energy costs and information maximization is a potential principle for cortical circuit networks.

Neuronal noise as an origin of sleep arousals and its role in sudden infant death syndrome

In addition to regular sleep/wake cycles, humans and animals exhibit brief arousals from sleep. Although much is known about consolidated sleep and wakefulness, the mechanism that triggers arousals remains enigmatic. Here, we argue that arousals are caused by the intrinsic neuronal noise of wake-promoting neurons. We propose a model that simulates the superposition of the noise from a group of neurons, and show that, occasionally, the superposed noise exceeds the excitability threshold and provokes an arousal. Because neuronal noise decreases with increasing temperature, our model predicts arousal frequency to decrease as well. To test this prediction, we perform experiments on the sleep/wake behavior of zebrafish larvae and find that increasing water temperatures lead to fewer and shorter arousals, as predicted by our analytic derivations and model simulations. Our findings indicate a previously unrecognized neurophysiological mechanism that links sleep arousals with temperature regulation, and may explain the origin of the clinically observed higher risk for sudden infant death syndrome with increased ambient temperature.

Synaptic E-I Balance Underlies Efficient Neural Coding

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Effects of channel blocking on information transmission and energy efficiency in squid giant axons

Action potentials are the information carriers of neural systems. The generation of action potentials involves the cooperative opening and closing of sodium and potassium channels. This process is metabolically expensive because the ions flowing through open channels need to be restored to maintain concentration gradients of these ions. Toxins like tetraethylammonium can block working ion channels, thus affecting the function and energy cost of neurons. In this paper, by computer simulation of the Hodgkin-Huxley neuron model, we studied the effects of channel blocking with toxins on the information transmission and energy efficiency in squid giant axons. We found that gradually blocking sodium channels will sequentially maximize the information transmission and energy efficiency of the axons, whereas moderate blocking of potassium channels will have little impact on the information transmission and will decrease the energy efficiency. Heavy blocking of potassium channels will cause self-sustained oscillation of membrane potentials. Simultaneously blocking sodium and potassium channels with the same ratio increases both information transmission and energy efficiency. Our results are in line with previous studies suggesting that information processing capacity and energy efficiency can be maximized by regulating the number of active ion channels, and this indicates a viable avenue for future experimentation.