A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers

A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers View ORCID Profile Sofia Farina , View ORCID Profile Alessandro Cattabiani , View ORCID Profile Darshan Mandge , View ORCID Profile Polina Shichkova , View ORCID Profile James B. Isbister , View ORCID Profile Jean Jacquemier , James G. King , View ORCID Profile Henry Markram , View ORCID Profile Daniel Keller doi: https://doi.org/10.1101/2024.12.10.627740 Sofia Farina 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sofia Farina For correspondence: sofia.farina{at}outlook.com Alessandro Cattabiani 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alessandro Cattabiani Darshan Mandge 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Darshan Mandge Polina Shichkova 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland 2 Biognosys AG , Schlieren, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Polina Shichkova James B. Isbister 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James B. Isbister Jean Jacquemier 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean Jacquemier James G. King 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henry Markram 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland 3 Laboratory of Neural Microcircuitry, Brain Mind Institute, École polytechnique fédérale de Lausanne (EPFL) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henry Markram Daniel Keller 1 Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) , Campus Biotech, Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Keller Abstract Full Text Info/History Metrics Preview PDF Abstract The high energetic demands of the brain arise primarily from neuronal activity. Neurons consume substantial energy to transmit information as electrical signals and maintain their resting membrane potential. These energetic requirements are met by the neuro-glial-vascular (NGV) ensemble, which generates energy in a coupled metabolic process. In ageing, metabolic function becomes impaired, producing less energy and, consequently, the system is unable to sustain the neuronal energetic needs. We propose a multiscale model of electro-metabolic coupling in a reconstructed rat neocortex. This combines an electro-morphologically reconstructed electrophysiological model with a detailed NGV metabolic model. Our results demonstrate that the large-scale model effectively captures electro-metabolic processes at the circuit level, highlighting the importance of heterogeneity within the circuit, where energetic demands vary according to neuronal characteristics. Finally, in metabolic ageing, our model indicates that the middle cortical layers are particularly vulnerable to energy impairment. Introduction Electro-metabolic coupling, or the relationship between neuronal signalling activity and the metabolic processes that supply energy to sustain it, is fundamental to brain function. Despite its small size, the energetic demands of the brain are remarkably high, accounting for two-thirds of the body’s energy production [ 1 – 4 ]. This energy, in the form of adenosine triphosphate (ATP), is used mainly to maintain ionic gradients across neuronal membranes and is essential to generate action potentials and support synaptic transmission. Thus, action potentials and postsynaptic potentials are the main consumers of ATP, followed by the regulation of the neuronal resting potential [ 4 ]. In neurons, the restoration of ionic gradients relies on the sodium–potassium pump, also known as Na + -K + pump, which is the most energy-demanding molecular mechanism. Brain energy production relies on a complex interplay between blood flow, neurons, and astrocytes. This neuro-glia-vasculature (NGV) unit coordinates energy production and distribution, integrating signals from neurons and glial cells to regulate blood flow and deliver metabolic substrates. Blood vessels and capillaries supply the brain with nutrients and oxygen, which astrocytes and neurons then utilize for energy production. Astrocytes play a critical role as metabolic mediators between neurons and blood vessels: they regulate blood flow and convert glucose into lactate, which is then delivered to neurons to fuel ATP production [ 5 – 7 ]. Given the intricate relationships between neurons, astrocytes, and the vascular system, brain energy dynamics are highly sensitive to disruptions in any of these biological processes. Ageing can alter these metabolic processes and is recognized as a significant risk factor for neurodegenerative diseases [ 8 ]. It also significantly affects brain metabolism, primarily through a global decrease in cerebral blood flow, reducing oxygen and nutrient delivery while altering glucose metabolism [ 9 ]. This decline contributes to neurodegeneration, which is often associated with a reduction in brain volume [ 10 ]. The loss of brain volume reflects neuronal atrophy and synaptic loss, processes that are closely related to the lack of energy supply. Different regions of the brain exhibit varying susceptibilities to ageing and neurodegenerative diseases [ 11 ], with regions rich in synapses and possessing long-range axons being particularly vulnerable [ 12 ]. Many aspects of electro-metabolic coupling remain poorly understood, especially how the energy demands of neuronal activity are integrated and regulated at the circuit level by different cell types. The energy requirements of neurons are not uniform and can vary according to cell type, firing patterns, and synaptic activity. Computational models have been proposed to investigate electro-metabolic coupling [ 13 – 15 ] by considering a unitary NGV ensemble. These models are often compartmentalized and described through systems of ordinary differential equations (ODEs). The advent of supercomputers and the ability to reconstruct neuronal circuits in silico [ 16 ] have enabled large-scale simulations that facilitate the study of complete circuit behavior, including neuronal morphologies, heterogeneity, and synaptic interactions [ 17 – 20 ]. Although metabolically coupled ODE models provide valuable insight at the single-unit level, they are constrained by assumptions of homogeneity, overlooking detailed morphologies, electrical types, and neuronal interactions. Circuit models, on the other hand, have achieved high levels of detail in neuronal electrophysiology, but primarily focus on neurotransmission and do not account for energy supply mechanisms [ 20 , 21 ]. To bridge the gap between electrophysiology and metabolism, we develop a multiscale framework of a reconstructed rat brain neocortex that integrates an electrophysiological model with a metabolic model of NGV across multiple scales ( Fig. 1A ). The circuit design is derived mainly from anatomical reconstruction [ 17 ] and an electrophysiological framework [ 18 ], both of which are based on previous foundational research [ 16 ]. The anatomical model provides a detailed reconstruction of the neuronal circuit, while the electrophysiological component incorporates detailed neuronal electrical properties [ 19 ]. The electrophysiological model is combined with a comprehensive metabolic model [ 15 ], as schematized in Fig. B, which describes the metabolic supply chain of NGV through ODEs. Download figure Open in new tab Fig 1. Model description and profiling the reconstructed rat neocortical circuit. A: Reconstructed visualization of the reconstructed rat neocortical microcircuit with colour-coded vasculature, astrocytes, and neurons based on their energetic level (ATP) obtained by our simulation. B: Schematic overview of the coupling between the electrophysiological and metabolic models. C: Illustration of the time-coupling between electrophysiological and metabolic models to address the different timescales. Electrophysiology operates on a much faster time scale compared to metabolism. As a result, the two processes run in parallel with different numerical time steps and are synchronized at coupling intervals based on the slowest metabolic timescale. D: Morphological reconstruction of neurons in the circuit [ 18 ], with each neuron colour coded according to its respective cortical layer. E: Histogram showing the number of excitatory neurons (EXC) versus inhibitory neurons (INH) in the neocortical microcircuit. F: Layer and synaptic composition as percentage of the microcircuit. G: Microcircuit composition of electrical types (e-types) per layer in logarithmic scaling to sizes based on percentage. This multiscale model provides a state-of-the-art framework for exploring how dynamic energy management supports and regulates neural activity and how it is altered in different states and diseases. After describing and validating our framework, we demonstrated its application by characterizing the energetic cost of electro-metabolic coupling and examining how electrical features varied across layers and neuron types within the circuit. The extensive network enabled a statistical analysis of the impact of layers and cell types on electrical properties. We also simulated age-related changes in metabolic rates to explore the effects of metabolic ageing, enhancing the framework’s ability to model neocortical dysfunction. Fully open source, it offers high detail and adaptability, allowing applications at the scale of entire reconstructed neocortical microcircuits or smaller neuronal samples. Furthermore, it integrates seamlessly with other metabolic models represented as ODEs, providing a foundation for advancing research on electro-metabolic coupling. Results Model overview We implemented a reconstructed rat neocortical circuit ( Fig. 1A ) following the approach described in the literature [ 16 , 17 ]. The circuit consists of neurons, astrocytes, andvasculature elements [ 19 , 22 , 23 ]. Neurons were morphologically reconstructed [ 24 , 25 ] and distributed according to their natural densities [ 26 ]. The reconstructed circuit contains 129,348 neurons and is described in the section herein entitled Reconstructing a rat neocortical microcircuit. For this work, we extrapolated a microcircuit maintaining the original proportion of the layer comprising 27,962 neurons, which occupies a volume of 400× 600 ×1400 µ m 3 . Fig. 1B presents a schematic representation of the coupling between NGV metabolic processes and neuronal electrophysiology in the circuit. For each neuron, both the electrophysiological and corresponding metabolic components were solved simultaneously. Integration was mediated by Na + - K + pump activity, modelled according to previous work [ 27 ]. This approach accounted for the differing timescales, as neuronal electrophysiology operates much faster than metabolism. To address these differences, the two systems were solved independently in an interleaved manner and synchronized every 100 ms. The concentrations of ATP and ADP were updated accordingly, as shown in Fig. 1C . Neurons were distributed across ranks, and the entire computation was parallelized (more details are given in Section Materials and methods). Neuronal models are based on the NEURON simulator [ 28 , 29 ]. Neurons have different electrical (e-types), made with parameter-optimized models [ 19 ] and morphological types (m-types), as illustrated in Fig. 1 D (see and S1 Table in S1 Text). Each single cell was integrated into Neurodamus [ 30 ], a software framework that simulates the electrophysiological activity of the entire circuit. Compared to the original approach [ 18 , 19 ], this version includes adaptations for interaction with metabolism via ion-specific mechanisms. S2-S4 Tables in S1 Text describe the channels, pumps, co-transporters and ion dynamics mechanisms added to different e-types. The metabolic model, consisting of a system of ODEs, represents metabolic processes within the NGV assembly [ 15 ]. It encompasses several cellular compartments, including the neuronal and astrocytic cytosol, mitochondrial matrix and intermembrane space, interstitium, basal lamina, endothelium, capillaries, arteries (with fixed arterial concentrations of nutrients and oxygen), and the endoplasmic reticulum (with a fixed Ca 2+ pool). Enzymes and transporters define the rate equations that govern the dynamics of metabolite concentrations. Although astrocytic morphologies [ 22 ] are not explicitly incorporated in the mathematical framework, their volume fractions were included in the ODE system [ 14 ]. Layer-specific mitochondrial densities [ 31 ] were also included to capture metabolic heterogeneity between different cortical layers. Neuronal heterogeneity was addressed by considering variations in the m-types and e-types. Fig. 1E illustrates the total number of excitatory (EXC) glutamatergic pyramidal cells and inhibitory (INH) GABAergic cells within the microcircuit, highlighting the balance between excitatory and inhibitory populations. Neocortical layers span from the top of layer 1 to the bottom of layer 6, and Fig. 1 F presents the composition of the microcircuit layer, highlighting the distribution of EXC and INH neurons between layers. Additionally, Fig. 1G details the distribution of e-types between layers, specifying their presence and proportions in each layer. Model validation Individual components from single-cell dynamics to circuit-level interactions have been built with a bottom-up approach and have been validated in previous studies [ 15 , 17 – 19 ]. Here, we aimed to validate the coupled framework, investigating whether the combined system simulated biologically plausible concentrations and the expected circuit behaviour. In particular, we were interested in the balance of energy demand and response. We ran two main simulations on the microcircuit for 3000 ms: one in which neuronal activity operated with a constant energy level (ATP = 1.38 mM [ 15 ]) without activating the metabolic processes, and another in which both the electrophysiological and metabolic components were active, exchanging information every 100 ms. To obtain in vivo firing rates, we calibrated layer-specific input stimuli using a relative Ornstein–Uhlenbeck type of stimulus [ 18 ]. Fig. 2A shows the firing rates of inhibitory and excitatory neurons in the simulation with metabolism. These results aligned well with previously reported values in the literature [ 32 ]. Furthermore, we validated the multiscale setup by ensuring that the average key concentrations per neuron (ATP, ADP, Na + , K + , Ca 2+ , and Cl) at the end of the simulation ( T = 3000 ms) remained within the known physiological ranges [ 13 , 14 , 33 – 36 ] ( Fig. 2 B ). As expected, the simulations confirmed that all the concentration-related variables were in the range of biologically plausible values, indicating that, by combining the two models, the circuit behaved as expected. Download figure Open in new tab Fig 2. Model validation and insights on dynamic behaviour in electro-metabolic simulations. A: The electro-metabolic simulation reproduces the firing rates of inhibitory (blue) and excitatory (red) neurons within the expected physiological range [ 32 ] (light grey area). B: At the end of the simulation ( T = 3000, ms), the average intracellular concentrations of ATP, ADP, Ca 2+ , Cl - , Na + , and K + in all neurons in the circuit fall within physiologically acceptable ranges derived from various literature sources [ 13 , 14 , 33 – 36 ] (light grey area). C: The dynamic behaviour of intracellular K + , Na + , ATP, and voltage for five neurons from different layers and e-types in the electro-metabolic simulation (orange) is compared to the electrophysiological model, where ATP is held constant at 1.38, mM (brown). D: The violin plots compare the average ATP produced (fuchsia) to the average ATP consumed (cerulean) at the final simulation time ( T = 3000 ms) in all neurons, showing that metabolism generates enough energy to meet neuronal demands. We compared the dynamic behaviour of key variables in five neurons taken from different layers and e-types for the two simulations presented ( Fig. 2C ). The dynamic energy state inside neurons significantly influenced their electrophysiological behavior. ATP was generally lower leading to higher intracellular Na + and K + , as reduced energy availability limits the rate of the Na + - K + pump. Lower energy levels also affected the action potentials of neurons, as we observed that the same cells might or might not spike at different times, depending on whether only electrophysiological processes were considered or metabolism was included. Since ATP demand and supply coupled the electrophysiological and metabolic processes, it was important to ensure a balance between ATP production by the NGV unit and consumption by the Na + - K + pump. Fig. 2D compares the energy production from metabolism to neuronal consumption in the simulation with active metabolism, measured at the final time point ( T = 3000 ms) for all neurons in the microcircuit. The results showed that energy production exceeds consumption, demonstrating that the NGV unit sustained the energetic demands of neurons. This supported the physiological plausibility of the integrated framework. The violin plot further highlighted variability in energetic demands across e-types. For instance, the dSTUT e-type exhibited high energy demands, consuming and producing more ATP than other e-types. This suggested that dSTUT cells were more metabolically active, possibly due to a higher spiking frequency, requiring increased energy production to support their activity. This aligned with their delayed stuttering firing patterns, which are energetically intensive. In contrast, the cIR e-type produced and consumed the least ATP, indicating more energy efficient processes or a less active cell. This could explain its continuous irregular firing pattern, which might involve less energy-demanding neural functions. Finally, the excitatory cADpyr e-type stood out as the most significant contributor to total ATP consumption, consistent with the fact that excitatory cells were the most abundant type in the circuit [ 37 , 38 ]. Additional details are provided in Section 1 of S2 Text. The combination of the two models produced biologically meaningful simulations, yielding expected firing rates, physiologically plausible concentration ranges, and a dynamic representation of energy derived from a comprehensive metabolic model. The addition of ion-specific mechanisms enabled more realistic interaction between metabolism and electrophysiology, enhancing the biological accuracy of the simulations. Layer and electrical characterisation of energetic consumption Integrating electrophysiology with metabolism allowed investigation of the interaction between energy use and spiking activity. Building on the simulation of the previous section, where metabolism was active, we extracted key features from each of the 27,962 neurons in the microcircuit and performed statistical analyses to characterize the layers and e-types. Specifically, we investigated the relationship between average energy production and consumption throughout the simulation period by plotting a colour-coded scatter plot based on spike counts ( Fig. 3A ), where cells are grouped by e-type. The results showed a high correlation between energy supply and demand, with correlations greater than 0.95 for each e-type (see Section 2 in S2 Text). It was interesting to note that cells with high total spike counts (more than 10) responded by producing more ATP. In particular, cNAC, dSTUT, and cSTUT expressed this characteristic firing pattern according to previous work [ 19 ] and the observations made in Fig.2D . Positive Spearman correlations were also observed between final ATP production and spike count (Spearman correlations: cNAC = 0.578714; cSTUT = 0.390978; dSTUT = 0.491834), strengthening the link between metabolic activity and neuronal spiking activity. Download figure Open in new tab Fig 3. Correlation between ATP production and consumption across neuronal e-types. A: Scatter plot of the average ATP produced by metabolism versus ATP consumed by each neuron in the circuit, categorized by e-type. The data points are colour-coded based on the binned number of spikes throughout the entire simulation. B: Dynamical behaviour of voltage and ATP during an action potential (AP) for an excitatory (cADpyr e-type) in layer 4. ATP produced by metabolism (blue), ATP consumed by the electrophysiological model through the Na + - K + pump (fuchsia) and the ATP coupling between the two models (orange). Black crosses highlight the times when coupling between metabolism and electrophysiology occurs (every 100 ms). C: Layer-wise consumption of ATP molecules per AP (colour-coded) compared to consumption during the neuronal resting state (grey). The analysis reveals significantly higher consumption and variability of ATP during activity compared to rest, highlighting the distinct energy demands across the cortical layers. As mentioned above, the Na + - K + pump was essential for electro-metabolic coupling in our framework. It expels Na + ions from the neuron, consuming ATP in the process. Fig.3B shows the dynamic response of ATP, to an action potential (AP): as Na + entered the cell and K + was depleted, the voltage-dependent Na + - K + pump ramped up ATP consumption to expel Na + . Since this process is the most energy intensive, analysing ATP depletion in the electrophysiological system illustrated its effect on overall energy consumption. In Fig.3C , we compared the number of ATP molecules consumed per action potential with that consumed during the neuronal resting state. As expected, neurons exhibited substantial consumption of ATP at rest, on the order of 10 8 mMs - 1 , with consumption increasing nearly tenfold during an action potential, consistent with previous findings [ 39 – 41 ]. This analysis revealed significant energy dynamics, summarized in a statistical table presented in Section 3 in S2 Text. ATP levels were higher during activity in all layers, with layer 2 showing the highest variability and layer 3 showing the lowest levels of ATP and variability at rest. Furthermore, consumption of ATP during an AP was more variable, with a higher standard deviation and an interquartile range compared to the resting state. Statistical tests confirmed that these differences are highly significant (p < 0.001 for all layers), highlighting the distinct energy demands and regulatory mechanisms between active and resting states. These results suggested functional specialization of the cortical layers. In addition, we investigated several electrophysiological features. For each neuron in the circuit, we recorded firing activity and categorize neurons by layer and e-type. We then extracted the number of spikes, the maximum voltage, the AP amplitude, and the resting state potential at the end of the simulation using the feature extraction library [ 42 ]. Descriptive tables for these characteristics are provided in Section 4 in S2 Text and a visual representation of these electrical features is shown in Fig.4 A . To assess statistical significance, we first confirmed non-normal distribution in our samples using the Shapiro-Wilk test [ 43 ], then applied Dunn’s non-parametric test [ 44 ] with Bonferroni adjustment [ 45 ] for pairwise comparisons across layers and e-types (see Section 5 in S2 Text). This analysis revealed significant electrophysiological differences across layers, reflecting layer-specific variations in neuronal behavior. Download figure Open in new tab Fig 4. Layer and e-type characterization of electrical features for spiking neurons. Violin plots illustrating the distribution of total spike count, maximum voltage, AP amplitude, and resting membrane potential of neurons that fired during the electro-metabolic simulation. Data are grouped by cortical layers (left) and neuronal e-types (right), highlighting significant variations in firing activity, voltage profiles, and excitability across layers and cell types. The plot emphasizes the distinctive electrical properties of specific layers, such as layer 1 having high spiking activity and e-types such as cNAC, bSTUT and cADpyr cells. Spike count varied significantly between layers, suggesting non-uniform firing rates across the cortex, possibly due to intrinsic neuronal properties or layer interactions. Layer 1 exhibited high spiking activity, primarily due to a high proportion of cNAC cells, averaging 9.5 spikes per neuron. cNAC cells had different firing behaviors, and dSTUT and cADpyr also showed unique patterns in pairwise comparisons (Section 5 in S2 Text). For maximum voltage, significant differences were found between layers, while layers 3 and 5, as well as layers 4 and 6, exhibited similar profiles. bSTUT and cSTUT cells had the highest maximum voltage, followed by bNAC cells, while dNAC cells had the lowest. cADpyr cells showed the widest range of both maximum voltage and AP amplitude. Significant differences in AP amplitude were observed between layer 1 and the other layers, suggesting layer-specific variations in excitability or synaptic input. For the resting state, significant differences were also found across layers, with layer 5 exhibiting the widest range of values, and layer 1 demonstrating a marked difference compared to the other layers. Pairwise comparisons of e-types revealed notable differences, particularly between bSTUT and cADpyr cells. These findings were aligned with those of [ 46 ] with distributions for L5 and L2/3 similar to our results. Neurons are morphologically described as shown in Fig.1D . We investigated the relationship between surface area and the electrical features we have previously analysed. Fig.5A displays a scatter plot for each layer and feature, with the surface area colour-coded by e-types, revealing distinct clusters for certain e-types. Pearson’s correlations between the surface area and the four electrical characteristics of neurons with at least 20 samples are shown in Fig. 5B . Download figure Open in new tab Fig 5. Clustering of neuronal e-types by surface area and electrical features across layers A: Scatter plot of neuronal surface area versus spike count, maximum voltage, AP amplitude, and resting membrane potential, grouped by cortical layer and colour-coded by e-type. Different clusters emerge according to the e-type indicating a possible correlation. B: Pearson correlation between surface area and each of the four electrical features (AP amplitude, resting membrane potential, spike count, and maximum voltage) across the cortical layers for each e-type with at least 20 samples and a statistically significant p-value. The results reveal distinct patterns, with negative correlations dominating AP amplitude and maximum voltage, while the resting membrane potential shows predominantly positive correlations, highlighting cell- and layer-specific influences on neural behaviour. The correlation between surface area and various features revealed both positive and negative associations across different layers and e-types. Regarding AP amplitude, most correlations were negative, with the strongest negative correlations observed in cADpyr (-0.66) and bSTUT (-0.64), suggesting that as the area increased, the AP amplitude tended to decrease. In contrast, the resting state of the neuron showed mainly positive correlations, especially in cADpyr (0.46) and cNAC (0.32), indicating a trend towards an increase in steady-state voltage with larger area. Regarding spike count, correlations varied, with cADpyr showing a positive correlation (0.44) and cSTUT showing a negative correlation (-0.57), reflecting different behaviours in different cell types. For maximum voltage, stronger negative correlations were observed in cADpyr (-0.62) and bSTUT (-0.68), suggesting that larger areas were associated with lower maximum voltage. These findings highlight how the relationship between area and electrical features varies between different layers and cell types, indicating potential layer- and cell-specific influences on neural circuit behaviour. In summary, our findings revealed that the cortical layers had different electrophysiological profiles, with more pronounced differences in certain layers. These variations probably reflected functional differences, including excitability, synaptic connectivity, and membrane properties. Accurately representing ATP dynamics in the model revealed the distinct ATP requirements between types of neurons. Effect of metabolic ageing on neuronal electrophysiology Finally, we explored the impact of ageing on metabolism in the neocortical microcircuit, extending a previous model [ 15 ] in which ageing effects were incorporated by modifying enzyme and transporter expression levels according to experimental data from the literature [ 47 , 48 ]. Generally, enzyme expression declines with age in both neurons and astrocytes. Building on this earlier work, we simulated ageing in our model by introducing dysfunctions in the mitochondrial electron transport chain (ETC), which affect energy production in both neurons and astrocytes. We adjusted various concentrations of metabolites and cofactors to represent ageing [ 49 , 50 ]. Key changes included a reduction in arterial glucose and intracellular glucose levels [ 51 ], downregulation of beta-hydroxybutyrate [ 52 , 53 ], an increase in intracellular lactate [ 54 ], and depletion of the total NAD pool (encompassing both NADH and NAD + ), reflecting reduced NAD availability with age [ 55 ]. Synaptic glutamate release pools were also adjusted to account for ageing [ 50 ], while synaptic input was kept constant. Furthermore, the capacity for NADH shuttling between the cytosol and mitochondria was reduced [ 55 ]. In this setup, the reference simulation, presented first in Section Model validation, with metabolism maintained in a young state is labelled “Young”, while the simulation with applied ageing effects is labelled “Aged”. As expected, ageing affected energy production, reducing the available ATP in neurons. Fig. 6 A illustrates that, per layer, the average total concentration ATP at the end of the simulation ( T = 3000 ms) was lower in the aged condition than in the young. Download figure Open in new tab Fig 6. Effects of ageing on energy and spiking activity in different layers and e-types of neurons A: Average neuronal ATP levels in cortical layers at the end of the simulation ( T = 3000 ms) for Young (orange) and Aged (magenta) groups. On average, each cortical layer exhibits lower energy levels in Aged compared to Young. B: VVisualization of ATP differences between Young and Aged neurons along the y - and z -axes of the neocortex, highlighting regions with differences greater than 0.1 mM. The left color bar delineates the layers along the z -axis, clearly illustrating that layers 3 and 4 are more affected by aging. C: Layer-wise histogram of the difference in spiking between Young and Aged clustered by e-types. Positive bars represent neurons that spike in Aged but not in Young, while negative bars indicate neurons that spike in Young but not in Aged. The difference in ATP levels between Young and Aged neurons within the microcircuit is shown in Fig.6B . The scatter plot revealed that ageing appeared to have the greatest impact on layer 4, with an ATP difference of approximately 0.15 mM, followed by layer 3. This suggested that metabolic ageing and its associated energy deficits might originate in the central layers or that these layers might be particularly vulnerable to ageing effects. Their slightly higher mitochondrial volume fraction [ 31 ] likely increased their susceptibility to energy production impairments. As observed, ageing in the metabolic model affected energetic availability in neurons, thus altering neuronal spiking activity. As shown in Section 6 in S2 Text, which compares the total number of spikes throughout the simulation and the final ATP average for the Young and Aged groups, along with the simulation with inactive metabolism where ATP is fixed at 1.38 mM, our model indicated that greater availability ATP correlated with lower firing rates. This trend could be explained by the dynamic role of ATP concentration in regulating the Na + - K + pump. When this pump is impaired, the firing rates can increase [ 56 ], highlighting the critical role of the Na + - K + pump in neuronal function. The decreased availability of ATP resulted in less efficient pump operation, leading to a buildup of intracellular Na + and a reduction in K + gradients. This disturbance in ion homeostasis made it easier for neurons to reach the firing threshold, explaining the increased firing rates observed with reduced ATP. We further examined differences in spiking activity between Young and Aged neuronal types by plotting changes in spike counts ( Fig. 6C ). Positive bars represent neurons that spike in the Aged condition but not in the Young, while negative bars indicate neurons that spike in Young but not in Aged. In particular, layer 4 exhibited the most pronounced increase in spiking with age, consistent with the previous observation that ATP is more impaired in this layer, leading to higher energetic requirements. Among excitatory pyramidal cells, cADpyr, the largest differences occurred in layer 4, followed by layers 5–6, and then layer 3. Certain inhibitory types, such as cNAC and bSTUT/cSTUT, which are characterised by high spiking frequencies, also exhibited increased activity in the Aged condition. In general, our findings suggested that central layers might be the first to be affected by ageing, as suggested by [ 57 ], who noted that central cortical regions were particularly vulnerable due to their high synaptic density and energetic requirements. Discussion In this work, we presented a reconstructed rat neocortical microcircuit composed of neurons, glial cells, and blood vessels. The electrophysiological model incorporated neurons with morphological and electrical features to capture cortical heterogeneity. Multiple ionic channels were included, with a particular focus on the Na + - K + pump channels [ 27 ], which served as a connection mechanism to metabolic processes via ATP consumption. We employed an established metabolic model [ 15 ] which represented the NGV unit and solved it for each neuron in the circuit. Different time scales were carefully considered to ensure accurate coupling. Our proposed circuit framework ( Fig. 1 ) achieved a high level of biological description by coupling two fundamental processes, making it the first circuit scale model to include such detailed representations, releasing a powerful tool for in silico experiments. The coupled system allowed investigation of neuronal energy requirements and NGV energy supply ( Fig.2 )- Fig.3 ). The metabolic component responded by increasing energy production for neurons that had higher energy demands due to elevated spiking activity, highlighting varying energy needs across neuronal e-types. We compared ATP consumption by Na + –K + pump during action potentials to that of the resting state. Although consumption of ATP was high to maintain resting potential, it increased further during spiking due to higher demands [ 4 ]. Finally, we identified different electrical characteristics linked to cortical layers and e-types ( Fig.4 , Fig.5 ). This highlighted the importance of incorporating neuronal heterogeneity to reflect the natural variability of biological processes [ 58 ]. Our findings confirmed the specific functions of layer 5 and pyramidal cells [ 46 ]. We also characterized the inhibitory cell types and found distinct differences in layer 1 compared to the other layers. The resilience and functionality of biological systems often depend on this heterogeneity, and its loss can affect network performance [ 59 ]. Finally, we tested ageing in the metabolic model by tuning various parameters. When comparing the aged simulation to the young one, we observed a clear energetic depletion in the central layers, particularly in layer 4 ( Fig. 6 ). This suggested that ageing might first affect the central layers due to their higher mitochondrial density [ 31 ]. Regions of the brain with high synaptic density, such as the central cortical areas, are known to be more susceptible to ageing [ 11 ], and certain studies highlight a similar vulnerability in the central layers [ 57 ]. Furthermore, an increase in glial density and a reduction in neuronal nuclear area in ageing is observed in layers 2–6 [ 60 ] suggesting a compensatory glial response to support neurons. This aligns with our findings, where glial activation in the central layers during ageing may offset metabolic deficits. Moreover, while our model does not account for dynamic blood flow, we recognize that blood flow plays a crucial role in the maintenance of brain metabolism. The capillary density is particularly high in layers 2–4, with layer 4 being notably dense [ 61 ]. Although the mechanisms underlying layer-specific blood flow regulation remain debated [ 62 ], our findings suggest that ageing may increase the need for targeted blood flow regulation to support metabolic demands, especially in the central layers. If central layers are in fact more vulnerable to ageing effects, their dense capillary networks can play a key role in delivering nutrients and mitigating these age-related deficits. Lastly, we demonstrated that firing rates are influenced by energy levels. The Na + - K + pump activity was constrained by ATP availability in both the Young and Aged models, and firing rates increase when ATP was limited, similar to the effects observed when Na + - K + pump activity is blocked [ 56 ]. A model always comes with limitations and assumptions, and although our model captures the key components of both electrophysiology and metabolism, some limitations remain. Currently, the model uses neuronal morphology to solve electrophysiological components, but the metabolic model is applied uniformly for each neuron. A potential next step would be to solve the metabolic model in morphological-dependent geometries, incorporating not only neuronal but also astrocytic morphology, which is already morphologically described in the circuit [ 22 ]. Astrocytic morphologies play a critical role as metabolic mediators and may significantly influence metabolism [ 63 ]. Additionally, our results from simulating ageing with the metabolic model indicate that ageing impairs metabolism in a layer-specific manner. This suggests the need to enhance our simulations with a more detailed model of blood flow [ 23 , 64 , 65 ]. Incorporating such a model could allow simulation of capillary changes, such as diameter and volume fluctuations, driven by neuronal and astrocytic energy demands. This would help to assess the role of the density of blood capillaries in ageing. Furthermore, incorporating a model of the extracellular space could influence molecular diffusivity and clearance, potentially leading to further model enhancements. For example, previous models have already shown the importance of extracellular potassium [ 13 ]. In our study, we have focused solely on metabolic ageing. However, ageing also affects electrophysiology by reducing the distribution of channel density and by affecting neuronal morphologies [ 66 ]. From a numerical point of view, it is crucial to carefully select the duration of the coupling time intervals to prevent the system from straying away from biologically realistic dynamics while avoiding extra overhead due to frequent syncing of the simulators. Furthermore, the metabolism-coupled circuit is subject to the inherent limitations present in both the metabolic and electrophysiological models. In conclusion, we presented a detailed framework for a reconstructed rat neocortical circuit that coupled electrophysiology and metabolism. Our model revealed that ageing might first affect the central cortical layers. Since the model is open source, it can be further developed, applied to study any region of the brain, and used to model in silico ageing or other neurodegenerative diseases. Materials and methods Electrophysiological model The electrophysiological model was implemented using the NEURON simulation environment [ 28 ]. To account for neuronal heterogeneity, we incorporated a diverse range of m-types [ 16 , 18 , 24 , 67 ] and e-types [ 19 ]. We used BluePyEModel [ 68 ] software to construct the e-models. It combined BluePyOpt [ 69 ] for the optimization and validation of multi-objective models, and eFEL [ 42 ] and BluePyEfe [ 70 ] for the extraction of features. The model building process is similar to that described in previous work [ 19 ]. Neuron models were constructed for 34 ° C. Additional model constants and some initial parameters are mentioned in S5 Table in S1 Text. The circuit contains eleven e-types: These e-types were paired with various morphology types (m-types) (see S1 Table in S1 Text) to form different morpho-electric types (me-types). The m-types present in the circuit are as follows: Excitatory cell m-types: BPC: Bipolar PC; HPC: Horizontal PC; IPC: Inverted PC; TPC:A: Tufted PC, late bifurcation; TPC:B: Tufted PC, early bifurcation; TPC:C: Tufted PC, small tuft; UPC: Untufted PC; SSC: Spiny Stellate Cell. Inhibitory m-types: BP: Bipolar Cell; BTC: Bitufted Cell; CHC: Chandelier Cell; DAC: Descending Axon Cell; DBC: Double Bouquet Cell; HAC: Horizontal Axon Cell; LAC: Large Axon Cell; LBC: Large Basket Cell; MC: Martinotti Cell; NBC: Nest Basket Cell; NGC-DA: Neurogliaform Cell with dense axon; NGC: Neurogliaform Cell; NGC-SA: Neurogliaform Cell with sparse axon; SAC: Small Axon Cell ; SBC: Small Basket Cell This resulted in 212 distinct morpho-electric (me-type) combinations, derived from experimental data of the rat somatosensory cortex [ 18 ]. We first created 40 electrical models (e-models) and then generalised these e-models for multiple morphologies using emodel-generalisation software [ 67 ]. This process resulted in a total of 129,348 neuron models being used in the circuit. Generalisation involved applying the final mechanism parameters of these e-models to multiple reconstructed and cloned morphologies based on the possible me-type combinations. Various mechanisms: ion channels, pumps, co-transporters and ion dynamics mechanisms were used to construct electrical models. These included Sodium (Na v ) Channels: Voltage-gated transient Na (NaTg), Persistent Na (Nap Et2); voltage-gated potassium (K v ): transient K (K Tst), persistent K(K Pst), K v type 3.1 (SKv3 1); small-conductance calcium-activated K channel (SK E2), stochastic K v (StochKv3), D-type K v (KdShu2007); voltage-gated (Ca v ): high-voltage-activated Ca (Ca HVA2), low-voltage-activated (Ca LVAst); hyperpolarisation-activated cation channel (Ih), sodium-potassium-chloride co-transporters (Na-K-Cl Co-transporter (nakcc); sodium-potassium pump (nakpump); Leak channels and an ion dynamics mechanism (internalions) (S2 Table in S1 Text). S3 and S4 Tables in S1 Text show the locations where these mechanisms are inserted in different neuronal locations with different e-types. We modified these mechanisms from the original work [ 19 ] adding new channels to include changes in ionic concentration. In particular, the metabolism model communicates with electrical models using ATP and ADP concentrations. These models were re-optimised and validated to fit the electrical features [ 19 ], and also fit the resting- and steady-state ionic and ATP concentrations for different stimuli. Metabolic model The metabolic model was originally proposed by [ 15 ]. It consisted of a detailed system of ODEs describing metabolic interactions within an NGV unit combined with a classic Hodgkin-Huxley neuron model. The equations were derived from various sources in the literature [ 13 , 14 , 71 – 75 ]. The model is compartmentalized and included the neuronal and astrocytic cytosol, the mitochondrial matrix and intermembrane space, the interstitial space, the basal lamina, the endothelium, the capillary, and an artery (with fixed arterial concentrations of nutrients and oxygen). It also incorporated the endoplasmic reticulum, which maintains a fixed Ca 2+ pool. In this work, the original model had been predisposed to be coupled with an electrophysiological one through the Na + - K + pump. Tables 1-8 in S3 Text show the 183 equations, Tables 9-24 in S3 Text describe the fluxes, and Tables 25-28 in S3 Text the initial values. Moreover, we account for metabolic variability across the cortical layers by integrating mitochondrial densities specific to each layer [ 31 ]. Blood flow was not dynamically modelled, but we considered a fixed level of 0.0001 ml / min and a fixed blood volume of 0.023 ml / min. View this table: View inline View popup Download powerpoint Table 1. Neuron E-types and Their Descriptions Reconstructing a rat neocortical microcircuit We used an NGV circuit consisting of neurons, synthesized astrocytes, and the cerebral vasculature [ 22 , 23 ]. It is based on a detailed reconstruction of the somatosensory cortex of a juvenile rat that algorithmically models the precise anatomy, connectivity, and electrophysiology of neurons [ 16 – 18 ]. The neocortical volume is 14,18 mm 3 , which contains 129,348 neurons with 206 million synapses, 42,362 astrocytes (for a neuron-to-astrocyte ratio of 3:1 [ 76 ]), 19 million glial-glial cell connections, 185 million neuron-glial connections and 73,345 glial-vasculature connections. For the simulation presented in this work, we extracted a microcircuit of 27,962 neurons within a volume of 0.336 mm 3 , maintaining the proportions of the original layer of the circuit. Simulation methods and conditions The multiscale model consists of three primary elements: Neurodamus, Metabolism, and the Multiscale Orchestrator. Fig. 7 schematically illustrates the relationships and main interactions among these elements. The following discussion provides an overview of the main components and their interactions. Download figure Open in new tab Fig 7. Key components and interactions in the multiscale model Schematic representation of the relationships and key interactions among the components of the Multiscale model: Neurodamus, Metabolism, and the Multiscale Orchestrator. Neurodamus: simulates neuronal activity, covering neuron dynamics, synaptic interactions, ionic currents, and mechanisms that consume ATP. Metabolism: models energetic production within a NGV unit. During synchronization, the Multiscale Orchestrator exchanges critical variables between modules. ATP production and consumption are balanced, after which ADP is calculated and re-injected into the Metabolism simulator. Neurodamus Neurodamus [ 30 ] is a Python-based application designed to control simulations within the NEURON environment, implemented in C++ [ 28 ]. NEURON employs a semi-implicit Euler method for time integration, carefully balancing computational performance, accuracy, and stability. In Neurodamus, neurons are modelled as structured graphs of segments (Fig. D), with a precise location in space and connected by synapses. Mechanisms, synapses, and electrical currents are all simulated within the segments. The time integration step, set to 0.025 ms, is the finest in the entire model, capturing the intricate dynamics of neuronal activity with high temporal resolution. The application simulates not only the neurons themselves and their interactions via synapses but also the flow of electrical currents and various cellular mechanisms presented in Section . To enhance computational efficiency and scalability, Neurodamus automatically distributes neurons across multiple ranks, allowing for efficient parallelization of the simulation. This approach facilitates the handling of larger and more complex models. External conditions, such as potassium concentration ([K] o = 5 mM) and extracellular calcium ([Ca 2+ ] o = 1.1 mM), are kept constant throughout the simulation, ensuring a stable extracellular environment for neuronal function. Metabolism In the Metabolism module, we solve the metabolic model presented in the previous section. Unlike the electrophysiological model, where neuronal morphology is explicitly represented, the metabolic model does not incorporate morphological details, as it is described by a system of ODEs. For each neuron in the circuit, we solve the metabolic model, incorporating the metabolic contributions of neurons, astrocytes, and the vasculature. The primary output of this module is the updated ATP concentration for each neuron. From the numerical standpoint, the equations governing this system are non-linear, a common characteristic of biological models, leading to a stiff system of equations that presents unique computational challenges. These are managed by solving the system in Julia [ 77 ], using the Differential Equations Suite and specifically the Rosenbrock23 solver, which is particularly effective for stiff problems. The time step used in this module is the largest in the overall model, set at 100 ms. This choice is facilitated by the robustness of the Rosenbrock23 solver, which enables integration over a larger time step, thus reducing computational time while maintaining accuracy in the simulation. Additionally, the implementation takes advantage of the neuron distribution provided by Neurodamus and is automatically parallelized to enhance performance and efficiency. Multiscale Orchestrator The Multiscale Orchestrator, developed in Python, is responsible for coordinating the simulation process across different modules. Its key functions include: Initializing the simulators Managing the execution order to determine which simulator is active at any given time Synchronizing simulators by transferring values, performing area and volume averages, and handling unit conversions Recording essential quantities Performing sanity checks to ensure that critical quantities remain within specified limits The orchestration follows a defined sequence to account for the different time scales of the two biological processes, as illustrated in Fig. 1 C: Neurodamus advances until a synchronization time step. Metabolism then advances to the same synchronization time step. A syncing operation occurs where values are exchanged and converted as needed. This process repeats at regular intervals. To simplify synchronization, the synchronization interval is set to the smallest common multiple (SCM) of the simulator time steps, which in this case is 100 ms. Since values are updated during synchronization, discontinuities may appear in concentration and current traces at these specific time steps. The quantities synchronized between the simulators are: neuronal intracellular ATP balanced according to an additive splitting scheme [ 78 ], using the following formula: where the subscript j denotes a certain syncing step, in this way we account for the decrement or increment of the ATP quantities from Neurodamus and Metabolism over the full time step [ j - 1 , j ]. An example of how ATP is sync can be seen in Fig. 3B . neuronal intracellular ADP at the syncing step j is computed based on the [ATP] j following the relationship proposed in [ 14 ] and present in the original metabolic model [ 15 ], as follow: where A ≈1.44 mM is the total adenine nucleotide concentration and q AK = 0.92 the adenylate kinase equilibrium constant. Performance and computational requirements A typical simulation of the microcircuit over 3000 ms of simulated time took approximately 5 hours on a HPE SGI 8600 cluster. The Neurodamus framework and the NEURON simulator are optimized for computational efficiency, enabling them to handle the large-scale computations required to simulate microcircuit dynamics. Table 2 summarizes the key parameters and specifications for the simulation. View this table: View inline View popup Download powerpoint Table 2. Simulation Configuration It should be noted that the simulation’s primary bottleneck lies in computational power rather than memory, since the memory footprint is relatively minimal. In fact, the available memory exceeded several terabytes. Author contributions SF contributed to the conceptualization, data curation, investigation, methodology, formal analysis, visualization, and writing the original draft. AC participated in data curation, software development, methodology, visualization, and writing the original draft. DM was involved in data curation, methodology, and writing the original draft. PS contributed to the conceptualization, software development, methodology, validation, and reviewing and editing. JI contributed to methodology and reviewing and editing. JJ participated in data curation and reviewing and editing. JK provided supervision and resources. HM contributed to the conceptualization, funding acquisition, and resources. DK contributed to the conceptualization, supervision, and reviewing and editing of the manuscript. Acknowledgments The authors thank Cyrille Favreau and Elvis Boci for their contributions to Figure 1 , Weina Ji and Pramod Kumbhar for their support with the software, and Michel Camps and Braeden Benedict for their contributions to the Blue Brain Project. We also extend our thanks to Karin Holm for her editorial assistance. 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