Neuroscience aims to characterize the dynamics of the brain and understand how they emerge as a product of anatomy and physiology. This is challenging, however, due to the complexity of the brain’s multiscale organization and its study in situ. Large-scale, data-driven, biophysically detailed models (Markram et al., 2015; Billeh et al., 2020) offer a tool that can complement laboratory investigation. This ‘bottom-up’ approach combines detailed constituent models into a 3D model of a given brain volume, thus consolidating disparate, multiscale data sources. Such models aim to provide a general tool for multiscale investigation; access and manipulation of any model component enable predictions about how these components combine to shape emergent dynamics. With increasing biological data, model refinement and validation must be continuous. This necessitates (1) open sourcing of models with high-quality software tools for iterative, community-driven refinement and validation, and (2) rigorous methodology for model parameterization and validation, such that model iterations can be meaningfully compared. Here and in our companion paper (Reimann et al., 2024), we present our methodology for parameterizing the anatomy and physiology of a cortical model, and validating its emergent dynamics. This enabled predictions about how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. Elsewhere, this has allowed the presented model to be used to study the formation of cell assemblies (Ecker et al., 2024b), functional synaptic plasticity (Ecker et al., 2024a), propagation of activity between cortical areas (Bolaños-Puchet and Reimann, 2024), the role of non-random connectivity motifs on network activity (Pokorny et al., 2025) and reliability (Egas Santander et al., 2025), the composition of high-level electrical signals such as the EEG (Tharayil et al., 2025), and how spike sorting biases population codes (Laquitaine et al., 2024).

Specifically, we built and validated a model of the entire non-barrel primary somatosensory cortex (nbS1) comprising eight subregions. Whilst our previous data-driven, biophysically detailed model (Markram et al., 2015) provided insights at the scale of a single cortical column (Reimann et al., 2013; Reimann et al., 2017; Reimann et al., 2022; Nolte et al., 2019; Nolte et al., 2020; Newton et al., 2021), the new model is ~140 times larger and to our knowledge offers the first simulations of in vivo-like spontaneous and stimulus-evoked activity in a biophysically detailed cortical model with interregion connectivity. In the companion paper, we introduce the anatomical model (Figure 1, step 1), describing how neuron morphologies were placed within an atlas-based geometry and connected through local and mid-range synapses. Here, we describe our improved techniques to model and validate the electrical properties of neurons and synapses (Figure 1, steps 2 and 3; Reva et al., 2023; Barros-Zulaica et al., 2019), and to compensate for input from missing brain areas (Figure 1, step 4). These improvements enabled enhanced validation of emerging in vivo-like activity (Figure 1, step 5), including the reproduction and extension of five published studies in rodent sensory cortex under a single in vivo-like regime (Figure 1, step 6).

Figure 1

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Validating that network activity emerges from the same interactions driving in vivo dynamics, requires (1) an approach to tackle the large parameter space without overfitting and (2) comparison of emerging dynamics with laboratory experiments. With respect to (1), we strongly prefer parameterization with directly measured quantities over fitting parameters to yield the correct emerging activity. Additionally, where fitting is applied, we adhere to a principle of compartmentalization of parameters. That is, once a parameter has been parameterized at one biological level, it is no longer a free parameter at a higher level. For example, the maximal conductance of a synapse is fit to biological amplitudes of postsynaptic potentials, but is then never updated in the process of reaching in vivo-like population activity. If a connection is valid on the single-cell level, its contribution at the population level should be equally valid. As a result, the emerging in vivo-like activity is the consequence of only 10 free parameters representing the strength of extrinsic input from other brain regions into 9 layer-specific excitatory and inhibitory populations, and a parameter controlling the noise structure of this extrinsic input. These parameters were fitted using a novel methodology presented here and were in accordance with the mean number of missing synapses for each population (Felleman and Van Essen, 1991; Harris et al., 2019; Gao et al., 2022).

With respect to (2), we conducted the following validations: spontaneous activity reproduced layer-wise in vivo firing rates (or a specified proportion of in vivo firing rates to account for in vivo recording bias; Wohrer et al., 2013), varied along a spectrum of asynchronous to synchronous activity, exhibited spatially structured fluctuations, and produced long-tailed firing rate distributions with sub 1 Hz peaks as in vivo (Wohrer et al., 2013; Buzsáki and Mizuseki, 2014). Under the same parameterization, the model reproduced precise millisecond dynamics of layer-wise populations in response to simple stimuli. These initial validations demonstrated that the model was in a more accurate regime compared to Markram et al., 2015—an essential step before testing more complex or larger-scale validations. For example, under the same parameterization we then observed selective propagation of stimulus-evoked activity to downstream areas, and reproduced and extended more complex experiments through accurate modeling of targeted optogenetic stimulation and lesions. Importantly, we highlight where emergent activity shows discrepancies with in vivo activity, to guide future data-driven model refinement.

The model generated a number of predictions (Supplementary file 1), including about the role of different layers in driving layer 2/3 stimulus-responses and how inhibitory interneuron types encode contrast, synchronous, and rate-coded information. Additionally, with access to the full structural connectome and tools for precisely editing it (Pokorny et al., 2025), we were able to make predictions about the relationship between structure and function. For example, we predict that an increase in the prevalence of non-random connectivity motifs towards deeper layers leads to a matched increase in spiking correlations, and that subregions more strongly innervated by mid-range connectivity have higher correlated activity locally. Additionally, we generated a new connectome that captured recently characterized spatially-specific targeting rules for different inhibitory neuron types (Schneider-Mizell et al., 2025) in the MICrONS electron microscopy dataset (MICrONS Consortium, 2025), such as increased perisomatic targeting by PV+ neurons, and increased targeting of inhibitory populations by VIP+ neurons. Comparing activity to the original connectome gave predictions about the role of these additional targeting rules. For example, inhibitory populations were more strongly inhibited (increasingly towards central layers) and required more non-local drive to reach the firing rates observed in vivo. Evoked responses increased and decreased in superficial and deeper excitatory populations respectively, suggesting layer-specific roles of the more specific inhibitory targeting.

To provide a framework for further studies and integration of experimental data, the full model is made available with simulation tools, as well as a smaller subvolume with the optional new connectome capturing inhibitory targeting rules from electron microscopy (Figure 1, step 7). The detailed modeling approach provides a one-to-one correspondence with most types of experimental data, allowing different datasets to be readily integrated. Due to the incredible speed of discovery in neuroscience, an integrative model will always be lagging behind the latest available data. We believe the solution is to provide a scientifically solid, validated model with clearly characterized strengths and weaknesses, along with the tools to advance or customize it for individual projects. We have therefore also made our tools for building and improving the model openly available: https://www.github.com/BlueBrain.

The companion paper describes the full anatomical nbS1 model containing 4.2M morphological neuron models, connected through 9.1B local synapses and 4.1B mid-range synapses (Reimann et al., 2024). Each neuron is modeled as a multicompartmental model belonging to one of 60 morphological types (m-types; Figure 2A1), and is either an instance or statistical variant of an exemplar in a pool of 1017 morphological reconstructions. Neurons were placed (orientated towards the surface) within an atlas-based geometry (Figure 2A2) based on estimated layer-wise density profiles of different morphological types. Local connectivity is based on axo-dendritic overlap with neighboring neurons, whilst mid-range connectivity combines data on interregion connectivity and laminar innervation profiles. Thalamocortical afferents were also modeled based on laminar innervation to the barrel cortex. To simulate emergent activity it is necessary to model and validate the electrical properties of these neurons and synapses (Figure 1, steps 2 and 3). These modeling steps are based on published methods and data sources (Table 1) and are summarized first. The method for compensating for missing synapses and the remaining results are then described (Figure 1, steps 4–6).

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Improved modeling and validation of synaptic physiology

The biological realism of synaptic physiology was improved relative to Markram et al., 2015 using additional data sources and by extending the stochastic version of the Tsodyks–Markram model (Tsodyks and Markram, 1997; Markram et al., 1998; Fuhrmann et al., 2002; Loebel et al., 2009) to feature multivesicular release, which in turn improved the accuracy of the coefficient of variations (CV; std/mean) of postsynaptic potentials (PSPs) as described in Barros-Zulaica et al., 2019 and Ecker et al., 2020. The model assumes a pool of available vesicles that is utilized by a presynaptic action potential, with a release probability dependent on the extracellular calcium concentration ($[C{a}^{2+}{]}_o$; Ohana and Sakmann, 1998; Rozov et al., 2001; Borst, 2010). Additionally, single vesicles spontaneously release as an additional source of variability with a low frequency (with improved calibration relative to Markram et al., 2015). The utilization of vesicles leads to a postsynaptic conductance with bi-exponential kinetics. Short-term plasticity (STP) dynamics in response to sustained presynaptic activation are either facilitating (E1/I1), depressing (E2/I2), or pseudo-linear (I3). E synaptic currents consist of both AMPA and NMDA components, whilst I currents consist of a single $GAB{A}_A$ component, except for neurogliaform cells, whose synapses also feature a slow $GAB{A}_B$ component. The NMDA component of E synaptic currents depends on the state of the Mg²⁺ block (Jahr and Stevens, 1990), with the improved fitting of parameters to cortical recordings from Vargas-Caballero and Robinson, 2003 by Chindemi et al., 2022.

The workflow for determining a dense parameter set for all synaptic pathways, starting with sparse data from the literature, is described for the use case of hippocampal CA1 in Ecker et al., 2020 and briefly in the ‘Methods’. We combined data sources used in Markram et al., 2015, with a large number of recent data sources (Qi and Feldmeyer, 2016; Barros-Zulaica et al., 2019; Yang et al., 2020; Yang et al., 2022). The resulting pathway-specific parameters are listed in the tables of Supplementary files 2–4, the most common short-term dynamics are depicted in Figure 3A1–2, and the assignment of STP profiles to different pathways are shown in Figure 3A3. PSP amplitudes and their CV closely matched their biological counterparts ($r=0.99$, $n=27$; Figure 3B1; table in Supplementary file 5 and $r=0.63$, $n=10$; Figure 3C1; table in Supplementary file 6, respectively). The dense parameter set also allowed prediction of PSP amplitudes and CVs for all cortical pathways (Figure 3B2, C2). The frequency of miniature postsynaptic currents (mPSCs) were also in line with in vitro measurements ($r=0.92$, $n=5$; Figure 3D; table in Supplementary file 7).

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Defining subvolumes and populations

To enable smaller simulations and targeted analyses, we defined standardized partitions comprising all neurons (and their connections) contained in a subvolume of the full nbS1 model. We decomposed the model into full-depth hexagonal prisms of a certain diameter that we call hexagonal subvolumes. These are slightly curved, following the geometry of the cortex, and have an intact layer structure. When the diameter of the hexagons is 520 $\mu m$, comparable to the size of the single cortical column model from Markram et al., 2015, we call these subvolumes columns. Taken together, a central column and the six columns surrounding it define a seven-column subvolume (Figure 4A). Additionally, when we separately analyze the E and I neurons in different layers, we refer to them as neuron populations of the model or a subvolume. Groups of I neurons that predominantly express either PV, Sst, or 5HT3aR markers are referred to as inhibitory subpopulations.

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Stimulus-responses reproduce in vivo dynamics with millisecond-scale precision

Recreating simple whisker deflection experiments

We compared stimulus-responses with in vivo barrel cortex responses to both single whisker deflections and active whisker touches under anaesthetized (Reyes-Puerta et al., 2015b) and awake states (Yu et al., 2019), respectively. While the model is of non-barrel somatosensory regions, this nevertheless provides validations of overall excitability and the laminar structure of responses reflecting general trends of cortical processing. We activated a percentage ($F_P$) of the VPM fibers projecting to the central column of the seven-column subvolume (Figure 5A1). For each selected thalamic fiber, spike times were drawn from VPM peristimulus time histogram (PSTH) recorded in vivo for the two stimulus types (Figure 5A2; ‘Methods’). For both stimulus types, a single thalamic stimulus was presented 10 times at 1-second intervals at three intensities ($F_P:5\mathrm{\%},10\mathrm{\%},15\mathrm{\%}$).

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Validation of millisecond precise in vivo population responses and corresponding prediction of in vivo spontaneous firing rates

For the parameter combination $[C{a}^{2+}{]}_o=1.05$ mM, $R_{OU}=0.4$, $P_{FR}=0.3$ and $F_P=10\mathrm{\%}$, E and I populations in each layer show clear responses on each trial (Figure 5B; three trials shown), except for L1. Figure 5—videos 1 and 2 show all simulated combinations of the four meta-parameters. Out of 72 parameter combinations, 21 passed an initial assessment of in vivo similarity to the in vivo data based on latencies and decays (see ‘Methods’, Figure 5C, Figure 5—figure supplement 1). Responses remained localized to within 600 $\mu m$ of the stimulus location (Figure 5D, Figure 5—video 3). In vivo, layer-wise I populations precede the corresponding E populations and response latencies increase with distance from thalamus, except for the fast response of L4 I. The latencies and decays are matched by criteria passing simulations (Figure 5C, E, left), except for a longer L4 I latency, and shorter and longer sustained responses of L2/3 E and L4 E. PV+ and Sst+ subpopulations respond reliably in layers 2–6, but the 5HT3aR+ subpopulation responses are weaker and less reliable (Figure 5—figure supplement 1). Under the active whisker touch paradigm, PV+ latencies precede Sst+ latencies in vivo, and either precede or are simultaneous with Sst+ latencies in silico (Figure 5E, right).

The passing parameter combinations form a contiguous region in the parameter space (Figure 5F), characterized by combinations of low $P_{FR}$, $[C{a}^{2+}{]}_o$ and $F_P$ with higher $R_{OU}$, which displayed weakly correlated spontaneous activity (compare Figure 5F with Figure 4G2). This offers a prediction that true in vivo firing rates are within the range of these lower FRs. Specifically, simulations with $P_{FR}$ from 0.1 to 0.5 robustly support realistic stimulus-responses, with the middle of this range (0.3) corresponding with estimates of in vivo recording bias; both the previous estimates of Olshausen and Field, 2006 and from a spike sorting study using this model (Laquitaine et al., 2024). The remaining results apply to the criteria passing simulations. We characterized the magnitude of responses by $R_E$, the ratio between the peak of the evoked response and the pre-stimulus baseline activity level. Assuming that extracellular sampling biases are similar during spontaneous and evoked activity, $R_E$ provides a bias-free metric for comparing response magnitude with in vivo. Decreasing $R_{OU}$ and $P_{FR}$, and increasing $[C{a}^{2+}{]}_o$ and $F_P$ led to a global increase of $R_E$, and results matched in vivo closely for a large part of the parameter space (Figure 5F).

Interestingly, the parameter combination closest to in vivo was central in the parameter space of criteria passing simulations (Figure 5G, left). Values of $R_E$, normalized by the mean $R_E$ across populations, were remarkably constant (Figure 5G, right), indicating that variation of the parameters provides a global scaling of response magnitude. Moreover, I populations matched the in vivo reference closely, while the responses of L2/3 E were slightly greater, and for L4 E and L5 E slightly weaker. The region of the parameter space where responses match in vivo for the active whisker touch stimulus was the same as for single whisker deflections (Figure 5—figure supplement 2). Latencies were also similar to in vivo, with the main discrepancy being slower responses in L4 (E and I). With respect to the meta-parameters, we found that the best match for the anesthetized state was characterized by lower firing rates ($P_{FR}$) and reduced external noise levels (lower $R_{OU}$) compared to the awake active whisker touch paradigm (white areas in Figure 5F, Figure 5—figure supplement 2B). These results fit expectation for anaesthetized and awake states respectively, and provide predictions of their spontaneous firing rates, noise levels and corresponding in silico regimes.

Prediction of 2–9% population response sparsity

Response sparsity (i.e., the proportion of neurons spiking at least once following a stimulus) varied across populations and meta-parameter combinations, but was around the range of 10–20% reported in vivo in the barrel cortex (Barth and Poulet, 2012; Figure 5—figure supplement 1D). Interestingly, excitatory population responses were sparser than the corresponding inhibitory layer-wise populations across layers. The observed heterogeneous layer-wise population sparsities offer a prediction for in vivo. Moreover, the majority of responding cells spike only once to single whisker deflections (Figure 5—figure supplement 1E), as reported in vivo (Isbister et al., 2021).

Validation and prediction through reproduction and extension of complex laboratory experiments using a single model parameterization

Building upon the in vivo-like activity states, we combined various simulation techniques to recreate laboratory experiments from the literature (see ‘Methods’). For these experiments, we use the parameter combination: $[C{a}^{2+}{]}_o=1.05$ mM, $R_{OU}=0.4$, and $P_{FR}=0.3$, as this combination supports realistic in vivo-like responses to the two stimulus types over a range of fiber percentages ($F_P$).

Exploring the canonical model: Layer-wise contributions to L2/3 stimulus-responses

In the canonical model of the cortex (reviewed, e.g., in Lübke and Feldmeyer, 2007; Feldmeyer, 2012) information from the thalamus arrives to L4, then propagates to L2/3, from there to L5 (which serves as the main cortico-cortical output layer) and lastly to L6 (which projects back to the thalamus). The coordinated action of all layers of S1 is required for high-level behavioral tasks (Park et al., 2020). As the canonical model is based on the highest layer-wise density of axons, it cannot describe all interactions in the cortex. For example, VPM does not only innervate L4, but also the border of L5 and L6, as well as the bottom of L2/3 (Meyer et al., 2010; Constantinople and Bruno, 2013; Sermet et al., 2019).

Recreating modulation of L2/3 PCs during optogenetic inactivation of L4 PCs during whisker deflection

To study how L4 contributes to the stimulus preference of L2/3 PCs, Varani et al., 2022 used optogenetic inactivation of L4 PCs during whisker stimulation and quantified the changes in the subthreshold response of L2/3 PCs. They found that the early phase of the subthreshold response significantly differed from the control condition if the whisker was deflected in either the most or the least preferred direction (see the top and bottom rows of their Figure B,C). From this they concluded that both L4 and VPM contribute to the direction tuning of L2/3 PCs. After reproducing their experimental conditions in silico (Figure 6A, B, Figure 6—figure supplement 2; see ‘Methods’), we confirmed that we can reproduce their results, that is, subthreshold responses of L2/3 PCs decreased, when L4 PCs were inhibited (Figure 6C for preferred direction whisker stimulation; see ‘Methods’).

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Prediction that L4 PCs enhance L2/3 PC responses directly, not through disynaptic inhibition

We then leveraged our in silico setup to study what Varani et al., 2022 could not because of methodological limitations. In our reading, the authors aimed to test how direct E connections from L4 PCs to L2/3 PCs influence the stimulus representation in L2/3. This connection cannot specifically be blocked in vivo, instead (95% of) the L4 PC population is inhibited (as well as some lower L3 PCs). In our setup, we could selectively block the connection and found almost the same result (compare Figure 6C and D, left). This extends the conclusion of Varani et al., 2022: L4 PCs contribute to the stimulus preference of L2/3 PCs via direct E connections, and not via disynaptic inhibition.

Prediction of the contribution of different layer-wise populations to L2/3 PC response

The authors also discussed studying L5 PCs’ contribution to L2/3 responses (as a large fraction of L5 PC axons terminate in L2/3), but this is infeasible with current mouse lines. Leveraging our model, we found that L5 contributes much less to subthreshold L2/3 traces than L4 (Figure 6D, right). Extending to other presynaptic layers, we found that the contribution of L2/3 is similar to that of L4, whereas inputs from L6 are negligible (Figure 6D, right). Whilst mouse lines targeting L5 PCs might arrive soon (which could validate our prediction), blocking L2/3 connections between L2/3 cells without hyperpolarizing the same L2/3 population seems only achievable in silico.

Recreation of contrast tuning modulation by optogenetic activation of PV+ interneurons

Shapiro et al., 2022 compared the modulatory effects of optogenetic activation of interneuron subtypes on contrast tuning of neurons in V1. Whilst visual regions differ from somatosensory regions, the study provided an opportunity to explore the generality of interactions between E and I subpopulations. We therefore created a version of the study in silico, presenting spatio-temporally modulated patterns with various contrast levels through the VPM inputs (Figure 6E, F; see ‘Methods).

Overall, we found within the single simulated column 228 PCs and 259 PV+ interneurons with robust contrast tuning of their responses (see ‘Methods’). Within spike detection range of a vertically penetrating extracellular electrode (50 µm; Henze et al., 2000; Buzsáki, 2004), we found 15 PCs and 13 PV+ interneurons with robust tuning, which is roughly comparable to the original study (average of 8.2 tuned PCs and 3.0 PV +neurons per mouse Shapiro et al., 2022). Unlike the original study we found no Sst+ neurons with robust tuning.

Additionally, we found the following results, all in line with Shapiro et al., 2022: The firing rates of both PCs and PV+ neurons were affected by optogenetic activation of the PV+ subpopulation (Figure 6G), resulting in changes to the contrast tuning curves of these populations (Figure 6H, Figure 6—figure supplement 4A). The main effect of the optogenetic manipulation was an increase in contrast tuning for PV+ and a decrease for PCs. However, at high contrasts, we observed a paradoxical effect of the optogenetic stimulation on L6 PV+ neurons, reducing their activity with increasing stimulation strength (Figure 6—figure supplement 4B; Mahrach et al., 2020). This effect did not occur under gray screen conditions (i.e., at contrast 0.0) with a constant background firing rate of 0.2 Hz or 5 Hz, respectively (not shown). The individual tuning curves could be accurately fit by sigmoidal functions ($r_{PV+}^2=0.995\pm 0.008$), and consequently the effect of optogenetic activation could be quantified in terms of changes to the fitted parameters (Figure 6H2 inset, Figure 6—figure supplement 5; ‘Methods’). We found great variability between neurons with increasing trends for parameter $m$ in PV+ and $c_{50}$ in PCs, and decreasing trends for $R_{max}$ in PV+ and both $m$ and $R_{max}$ in PCs. Unlike in Shapiro et al., 2022, we found in addition an increasing trend of $c_{50}$ also in PV+ interneurons (Figure 6—figure supplement 5).

Comparing three abstract mathematical models (Shapiro et al., 2022; ‘Methods’), we found that the saturation additive model best captured the effect of the optogenetic manipulation for PV+ neurons (Figure 6—figure supplement 6A, B1), as in the reference study. The relative contributions of saturation and addition varied strongly between individual neurons (Figure 6—figure supplement 6C1). At low intensities of the optogenetic manipulation, this distribution matched qualitatively the results of Shapiro et al., 2022, but at higher intensities we observed a shift towards stronger saturation. Combining the saturation additive model with a simplified, population-level model of PC firing (Shapiro et al., 2022; ‘Methods’) provided a good description of the observations (Figure 6—figure supplement 6B2) with relative contributions of saturation and addition that qualitatively match the reference (Figure 6—figure supplement 6C2).

Encoding of synchronous and rate-coded signals by inhibitory subpopulations

Prince et al., 2021 studied how different I subpopulations represent signals that are encoded as changes in the firing rate or synchronicity of PCs (Figure 6I). We repeated their study in silico (Figure 6J), but using in vivo-like conditions as the authors have suggested in their ‘Discussion’. The original study pointed out the importance of membrane properties and short-term dynamics of synaptic inputs onto the subpopulations in shaping the results. Note that the lower synaptic reliability in vivo weakens synaptic depression (Borst, 2010), and the ongoing background input leads to higher membrane conductance (Destexhe et al., 2001); therefore, our results are expected to differ substantially and provide an independent prediction for a different dynamic state. While in the original in vitro study stimulation of 10 PCs was sufficient to find a decodable signal, we expected a larger number to be required under our in vivo-like conditions due to the lower $[C{a}^{2+}{]}_o$, and the higher level of noise (see ‘Methods).

Indeed, we found little discernible mutual information in I spiking for the activation of 10 PCs, but that mutual information increased with the number of activated neurons (Figure 6K, right). For 1000 activated neurons each of the three I subpopulations in L2/3 showed clear but qualitatively unique modulation for both encoding schemes (Figure 6J). As in Prince et al., 2021, we found differences in the encoding capabilities of the I subpopulations (Figure 6K). We found strongest encoding in PV+ neurons for both schemes, and overall low encoding strength in Sst+ neurons. Further, we found overall low amounts of mutual information for the synchronous stimulus, which is in line with Prince et al., 2021, as they linked encoding of that stimulus to depressing synaptic dynamics, which would be weakened in vivo.

Exploring the effect of inhibitory targeting trends observed in electron microscopy (EM)

Additional targeting rules recreate quantitative EM-connectivity trends

We next explored how certain trends in inhibitory targeting observed in a recent mouse electron microscopic connectome (Schneider-Mizell et al., 2025; Figure 7A) affect spontaneous and evoked activity. Consequently, an alternative connectome which quantitatively recreates these trends has been built (Reimann et al., 2024). We refer to this as the Schneider–Mizell compatible connectome (SM-connectome). It ensures that PV+ neurons target the perisomatic region, VIP+ neurons target other inhibitory neurons, and cells in L1 connect predominantly monosynaptically. Consequently, inhibition onto E neurons is sparser and closer to the soma (Figure 7A2). After validating the presence of these trends, we characterized how this affects the dynamics of the spontaneous and evoked states (Figure 7A3). Note that individual synapses were recalibrated to preserve the pathway specific reference PSP amplitudes. As expected, the largest change was for PV+ neurons where the average number of vesicles in the release-ready pool $N_{RRP}$ was increased by 2.4 times.

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EM targeting trends increase inhibition of inhibitory populations enabling increased long-range excitation

Inspite of this, the change in connectivity affected the activity sufficiently that recalibration of missing synaptic compensation was needed to attain the target firing rates for the original meta-parameter combinations (Figure 7—figure supplement 1). Mean conductances onto I neurons had to be increased, indicating the efficacy of the newly introduced I-I targeting by VIP+ neurons (Figure 7B1). On the other hand, conductances for E neurons remained roughly the same, except for L6. Curiously, we found that the firing rates of I populations were reduced when the internal connectivity was activated compared to when it was inactive, further highlighting the efficacy of the I-I targeting (Figure 7B2, Figure 7—figure supplement 1). For E neurons in L2-5, activating internal connectivity increased firing rates, but less than in the original connectome. As the number of inhibitory synapses innervating them decreased (Figure 7A2), this indicates that the placement of PV +synapses closer to the soma increased the efficacy of inhibitory as expected. Taken together, this indicates that in the SM-connectome, I populations are more externally driven and more internally inhibited (as a function of proximity to L4), and L6 E more internally driven during spontaneous activity. Over the different meta-parameter combinations, correlation in the network decreased relative to the original circuit with some exceptions (Figure 7B3, Figure 7—figure supplement 1). This indicates that activity in the SM-connectome is shifted slightly towards a more asynchronous state.

By correlating the structural and functional changes we concluded that the increased conductance injection into the I populations can be explained by the increased inhibitory synapse count (Figure 7C). Conversely, the mostly decreased synapse counts for E populations did not lead to a decrease of conductance injection, further indicating that the more perisomatic positioning of PV+ synapses compensates for the reduced synapse counts.

EM targeting trends affect population-wise stimulus-evoked response magnitudes and latencies

The lack of effect onto most of the E populations disappeared in the evoked state (Figure 7D). Overall, responses increase strongly in L23 and slightly decreased in L5 and L6 (Figure 7D1), while latencies of responses increased in every layer (Figure 7D2), suggesting different roles of the more specific inhibitory targeting in different layers.

EM targeting trends hyperpolarize Sst+ and HT3aR+ late response, and disinhibit L5/6 E

Studying somatic membrane potentials for different subpopulations in response to whisker deflections shows that PV+, L23E, and L4E subpopulations are largely unaffected in the SM-connectome (Figure 7E). Interestingly, Sst+ and 5HT3aR+ subpopulations show a strong hyperpolarization in the late response that is not present in the original connectome. Interestingly, this corresponds with a stronger late response in L5/6 E populations, which could be caused by disinhibition due to the Sst+ and 5HT3aR+ hyperpolarization. This could be explored further in follow-up studies using our connectome manipulator tool (Pokorny et al., 2025).

Mid-range connectivity determines independent functional units and selective propagation of stimulus-responses at larger spatial scales

Stable spontaneous activity only emerges in nbS1 at predicted in vivo firing rates

After calibrating the model of extrinsic synaptic input for the seven-column subvolume, we tested to what degree the calibration generalizes to the entire nbS1. Notably, this included the addition of mid-range connectivity (Reimann et al., 2024). The total number of local and mid-range synapses in the model was 9138 billion and 4075 billion, that is, on average full model simulations increased the number of intrinsic synapses onto a neuron by 45%. Particularly, we ran simulations for $P_{FR}\in [0.1,0.15,...,0.3]$ using the OU parameters calibrated for the seven-column subvolume for $[C{a}^{2+}{]}_o=1.05$ mM and $R_{OU}=0.4$. Each of these full nbS1 simulations produced stable non-bursting activity (Figure 8A), except for the simulation for $P_{FR}=0.3$, which produced network-wide bursting activity (Figure 8—videos 1 and 2). Activity levels in the simulations of spontaneous activity were heterogeneous (Figure 8B, Figure 8—video 3). In some areas, firing rates were equal to the target $P_{FR}$, whilst in others they increased above the target (Figure 8C). In the more active regions, mean firing rates (averaged over layers) were on the order of 30–35% of the in vivo references for the maximum non-bursting $P_{FR}$ simulation (target $P_{FR}:0.25$). This range of firing rates again fits with the estimate of firing rate bias from our paper studying spike sorting bias (Laquitaine et al., 2024) and the meta-parameter range supporting realistic stimulus-responses in the seven-column subvolume. This also predicts that the nbS1 cannot sustain higher firing rates without entering a bursting regime.

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Independent functional subunits emerge during spontaneous activity with accurate layer and distance-dependence of noise correlations

The subregion with consistently higher firing rates was the upper-limb representation (S1ULp), separated from subregions of lower activity by the dysgranular zones (S1DZ, S1DZO). This demonstrates that the larger spatial scale of the model supports several independently acting functional units. Locations with higher firing rates were accompanied by higher spiking correlations of the local population (Figure 8B2, Figure 8—video 3), indicating activity that was more driven by intrinsic excitation. Unlike in the simulations of smaller volumes, the correlation emerged mostly from short, transient packets of activity in L6 (Figure 8A). Consequently, deviation from the target $P_{FR}$ in those cases was largest in L6 (Figure 8C). Taken together, it appears as if the additional synapses from mid-range connectivity moved the model along the spectrum from extrinsically vs. intrinsically driven, but further for some regions than for others. We confirmed a divergence into two populations: one with correlation slightly increased from the simulations of smaller volumes, and one with almost maximal correlations (Figure 8D). This distinction was determined mostly in L6 with increases of correlations in other layers remaining small.

The distance dependence of correlations followed a similar profile to that observed in a dataset characterizing spontaneous activity in the somatosensory cortex (Reyes-Puerta et al., 2015a; compare red line in Figure 8I with Figure 8—figure supplement 1). In the in vivo dataset spiking correlation was also low but highest in lower layers, with short ‘up-states’ in spiking activity constrained to L5 and 6 (see Figure 1E and F in Reyes-Puerta et al., 2015a). In the model, they are constrained to L6.

Stable propagation of evoked activity through mid-range connectivity only emerges in nbS1 at predicted in vivo firing rates

We repeated the previous single whisker deflection evoked activity experiment in the full model, providing a synchronous thalamic input into the forelimb sub-region (S1FL; Figure 8E, Figure 8—videos 4–6). Responses in S1FL were remarkably similar to the ones in the seven-column subvolume, including the delays and decays of activity (Figure 8F). However, in addition to a localized primary response in S1FL within 350 $\mu m$ of the stimulus, we found several secondary responses at distal locations (Figure 8E, Figure 8—video 6), which was suggestive of selective propagation of the stimulus-evoked signal to downstream areas efferently connected by mid-range connectivity. The response of the main activated downstream area (visible in Figure 8E) was confined to L6 (Figure 8G). In a follow-up study using the model to explore the propagation of activity between cortical regions (Bolaños-Puchet and Reimann, 2024), it is described how the model contains both a feedforward projection pattern, which projects to principally to synapses in L1 and L23, and a feedback-type pattern, which principally projects to synapses in L1 and L6. On visualizing the innervation profile from the stimulated hexagon to the downstream hexagon, we can see that we have stimulated a feedback pathway (Figure 8—figure supplement 1).

Membership in mid-range connectivity rich club predicts correlation of spontaneous activity

As the model is homogeneous across the different subregions in terms of neuronal morphologies and physiology, we expected the differences in spontaneous and evoked activity across the model to be explained by the topology of synaptic connectivity (Reimann et al., 2024). While the extrinsic input from the OU injections was uncorrelated by design, synaptic input from local connectivity would feature at least some degree of correlation. This led to asking, how correlated was the input from mid-range connections? We estimated for each pair of mid-range connections innervating a 460 $\mu m$ hexagonal subvolume its spiking correlation and compared the resulting distribution to the correlation within the subvolume itself (see ‘Methods’; Figure 8H, upper). We found low correlations for most pairs, but with a significant number of high-correlation outliers for subvolumes with high internal correlations. Moreover, when we considered the mean of the measure, we found that it explains most of the variability in internal correlations, for both spontaneous (Figure 8H, lower) and evoked states (Figure 8—figure supplement 1).

This led to the following tentative explanation: Local connectivity provides inputs from within 500 $\mu m$, which is virtually guaranteed to be correlated to some degree (Figure 8I upper vs. lower, orange). This is a result of connections being locally dense, as they are concentrated within a small neighborhood (Figure 8 lower, green vs. orange). mid-range connectivity originates from further away, sampling from different functional units that may or may not be correlated, and being more diluted (Figure 8I lower, green vs. blue). Consequently, mid-range inputs are likely to act not much different from the uncorrelated OU process, unless this is overcome by a non-random topology of mid-range connectivity.

Specifically, we considered the mid-range connectivity at meso-scale in terms of the graph of synapse counts between 460 $\mu m$ subvolumes. When the graph was thresholded at 8 million synapses, most subvolumes lacked connections at that strength completely, but others formed a single connected component, indicating the formation of a rich club (Colizza et al., 2006; Figure 8—figure supplement 1J, upper, red and orange). We found that subvolumes with high internal correlation were part of the rich club, or directly adjacent to one of its members (Figure 8J). In the presence of strong thalamic inputs, this could be overcome, as the increased activity level spread along sufficiently strong mid-range connections (Figure 8K), albeit with substantial delays between 60 and 100 ms. This could induce high correlations in strongly connected parts of the model, outside the rich club as well (Figure 8—figure supplement 1), and shows that the model supports selective propagation of activity to areas efferently connected with mid-range connectivity.

We presented novel methods to build, simulate, and validate models of cortical tissue that correspond directly with 3D digitized brain atlases. As demonstrated, this enables laboratory experiments to have a simulatable in silico counterpart, and vice versa: predictions made by the model automatically have a precise correspondence in biology. By recreating and extending five laboratory experiments under a single model parameterization, we provided strong model validation and demonstrated the model’s natural versatility. To our knowledge the simulations of the full nbS1 represent the first simulations of stable in vivo-like spontaneous and stimulus-evoked activity in a large-scale biophysically detailed model of multiple cortical subregions connected through local and mid-range connectivity. The model generated an initial set of predictions about the relationship between cortical structure and function, and provides a basis for future studies and predictions. The table in Supplementary file 1 describes each quantitative prediction made in this article, and how it might be tested in vivo. Additionally, all assumptions are listed in the table in Supplementary file 8. We made the model and simulation tools available for use, enabling community-driven testing, exploration and refinement.

Excitation from non-modeled brain regions is modeled by injecting somatic conductances and is calibrated for specific populations. For the purpose of calibration, we presented a novel technique, which allows rapid and methodical characterization and validation of emergent model activity. The efficiency of the technique allows rapid recalibration after changes to the model such as adding new brain regions or changing anatomical details. Connected divided by unconnected firing rates measured during the process are also a prediction of the degree to which the activity of a population is determined by incoming connections from local vs. extrinsic sources. We found an increase of the importance of local connectivity from superficial to deeper layers, in line with the canonical view of the cortex (Lübke and Feldmeyer, 2007; Feldmeyer, 2012), which places layers 5 and 6 at the end of local information flow.

Although abstract relative to the model, the injection amplitudes could be predicted from the number of missing synapses, suggesting it provides a functional representation of their anatomical counterparts. The modular model of extrinsic connectivity can be refined or replaced in the future or when larger fractions of the brain are modeled. The calibration framework could optimize per population parameters for other compensation methods, whilst still offering an interpretable spectrum of firing rate regimes at different levels of $P_{FR}$. For example, more realistic compensation schemes could be explored which introduce (a) correlations between the inputs received by different neurons and (b) compensation distributed across dendrites, as well as at the soma. We predict that such changes would make spontaneous activity more correlated at the lower spontaneous firing rates which supported in vivo-like responses ($P_{FR}:0.1-0.5$), which would in turn make stimulus-responses more noise correlated.

Our approach to obtaining in vivo-like activity contrasts with a recent approach in a large-scale biophysical model of primary visual cortex of similar scale to our smaller subvolume (Billeh et al., 2020). There, extrinsic inputs were delivered through dendritic synapses instead of somatic injection, which is more anatomically realistic, and intrinsic recurrent weights were adjusted to match extracellularly recorded firing rates. They find a single activity state, instead of the in vivo-compatible spectrum here that allowed us to contrast anesthetized and awake states. The model in general differs from hybrid models, which jointly use biologically detailed models and point neuron models (Billeh et al., 2020; Dura-Bernal et al., 2023), and also from the work of Egger et al., 2020, who modeled activation of L5 pyramidal cells by constraining patterns of synaptic input based on receptive fields and predicted anatomical innervation.

Given the complexity of the model, how can we be confident that its activity is in vivo-like? First, we demonstrated correlated spontaneous dynamics in the form of global and local fluctuations and dynamic E-I balance, as found experimentally (Renart et al., 2010). Second, population firing rate distributions were long-tailed with sub 1 Hz peaks, and were similar for spontaneous and stimulus-evoked activity, as reported (Wohrer et al., 2013). Moreover, mean firing rates below 1 Hz are required by metabolic constraints (Attwell and Laughlin, 2001; Lennie, 2003). In response to simple whisker stimuli, response sparsity, spike counts, and the temporal profile and amplitudes of layer-wise populations were similar to in vivo under anesthetized and awake conditions, for different regions of the meta-parameter space. The effect of the spontaneous meta-parameters on evoked responses, also shows that the networks spontaneous state affects stimulus-responses, as observed in vivo (Isbister et al., 2021). Although from the barrel system, these stimuli offer some of the simplest stimulus-response paradigms, enabling principles of neural dynamics and information processing to be studied; in particular, the correspondence between atomic units of sensory information and neural representations. The model also predicts the number of thalamic fibers stimulated for whisker-flick stimuli. Such validations are crucial for complex models, as they provide context for more complex validations. Moreover, in a rich and complex nonlinear system, the source of discrepancies with in vivo activity are simpler to ascertain under simpler protocols.

For more complex validations, we reproduced protocols from three laboratory experiments. These experiments used a wide array of techniques, which could be accurately recreated due to the model’s close correspondence with biological tissue. Additionally, we could go beyond the original experiments. For example, we predicted an increased number of neurons would be required to have a decodable effect on postsynaptic activity under in vivo conditions over the in vitro conditions of the original experiment. This can be explained by lower synaptic reliability under in vivo conditions. Also, we predicted a paradoxical effect of optogenetic stimulation on L6 PV+ interneurons, namely a decrease in firing with increased stimulus strength. This is reminiscent of the paradoxical responses found by Mahrach et al., 2020 in the mouse anterior lateral motor cortex (in L5, but not in L2/3) and barrel cortex (no layer distinction), respectively. While Mahrach et al., 2020 conducted their recordings in awake mice not engaged in any behavior, we observed this effect only when drifting grating patterns with high contrast were presented. Nevertheless, consistent with their findings, we found the effect only in deep but not in superficial layers, and only for PV+ interneurons but not for PCs. Our model could therefore be used to improve the understanding of this paradoxical effect in follow-up studies. These examples demonstrate that the approach of modeling entire brain regions can be used to further probe the topics of the original articles and cortical processing. This could be continued in the future using paradigms that take advantage of the multiple regions offered by the model to study processing in cortical hierarchies.

Responses to whisker flick stimuli were closer to biology for values of the $P_{FR}$ meta-parameter between 0.1 and 0.5, according with the known inflation of mean firing rates stemming from the bias of extracellular spike sorting techniques towards larger and more active neurons (Olshausen and Field, 2006). Supporting our estimates, patch-clamp experiments show ubiquitous neuronal silence (Crochet and Petersen, 2006), and spontaneous and stimulus-evoked barrel cortex firing rates as much as 10 times lower than for extracellular recordings (0.05–0.15 Hz vs. 0.8–1.5 Hz; Olshausen and Field, 2006). This is also in line with our recent work using the model, which estimated a spike sorting bias corresponding to $P_{FR}=0.3$ using virtual extracellular electrodes (Laquitaine et al., 2024). Each recording technique comes with challenges and biases (Shoham et al., 2006; Olshausen and Field, 2006; Barth and Poulet, 2012; Wohrer et al., 2013). Calcium imaging and patch-clamp experiments are less biased towards frequently spiking neurons (Wohrer et al., 2013), yet the former can only infer spiking activity and may be biased towards neurons that favor marker expression. Patch-clamp techniques can reduce firing rates, be biased towards larger or more active neurons (Olshausen and Field, 2006) and are limited to recording a few neurons simultaneously, preventing characterizations of single trial population dynamics. An earlier version of the model has been used to simulate cortical extracellular potentials (Reimann et al., 2013). We can now simulate modern extracellular electrodes and compare multi-unit activity and spike sorting results to the in vivo-like ground truth to gain further insights into the potential over-estimation of mean firing rates.

During simulations of the entire nbS1, we found emerging spatial inhomogeneities in network activity. Specifically, we observed sharp transitions in firing rates and pairwise correlations at disgranular zones. This highlights that the model supports several, independently acting functional units. Analysis of spatial correlations suggests a radius of approximately 400 $\mu m$ for a single unit. This property was not present in the previous intra-regional scale model (Markram et al., 2015), but is rather an emergent property of the interregional scale, demonstrating that we achieved our goal of building a model that can be used to study mid-range interactions between regions. Analyzing the source of the inhomogeneities suggested a prominent role of a non-random rich-club structure of mid-range connections at the meso-scale. Within functional units, we found that inhomogeneous increases of spiking correlations were confined to spontaneous packets of L6 activity.

In keeping with the philosophy of compartmentalization of parameters and continuous model refinement (see Introduction), it was essential to improve validity at the columnar scale (relative to Markram et al., 2015) as part of demonstrating validity of the full nbS1. Indeed, improved parameterization and validation at smaller scales was essential to parameterizing background input which generated robust nbS1 activity within realistic $[C{a}^{2+}{]}_o$ and firing rate ranges. We view this as a major achievement, as it was unknown whether the model would achieve a stable and meaningful regime at the start of our investigation. Whilst we would have liked to go further, our primary goal was to publish a well characterized model as an open resource that others could use to undertake further in-depth studies. In this regard, we are pleased that the parameterization of the nbS1 model has already been used to study EEG signals (Tharayil et al., 2025), as well as propagation of activity between two subregions (Bolaños-Puchet and Reimann, 2024). Investigation, improvement and validation must be continued at all spatial scales in follow-up papers with detailed description, figures and analysis, which cannot be covered in this article. Each new study increases the scope and validity of future investigations. In this way, this model and paper act as a stepping stone towards more complex questions of interest to the community such as perception, task performance, predictive coding and dendritic processing. This was similar for Markram et al., 2015 where the initial paper was followed by more detailed studies. Unlike the Markram et al., 2015 model, the new model can also be exploited by the community and has already been used in a number of follow-up papers studying (Ecker et al., 2024a; ; Bolaños-Puchet and Reimann, 2024; Pokorny et al., 2025; Egas Santander et al., 2025; Tharayil et al., 2025; Laquitaine et al., 2024). We believe that the number of use cases for such a general model is vast and is made larger by the increased size of the model.

It is important to highlight where the model deviates from biological data. First, the model omits several known anatomical elements, such as glia and the presence of gap junctions between certain neuron-types, but the model’s spatial context provides a natural scaffold for them. Our modeling techniques assume generality of rules and parameters, unless indicated by the data. Therefore, violations indicate that biological rules and data may be more specific. For example, model thalamic inputs innervate any dendrite placed in the targeted layers equally, but this did not reproduce the timing of inhibitory subpopulation responses. Stronger innervation of PV+ neurons than other inhibitory neurons may rectify this. However, it is unclear whether innervation should be anatomically or physiologically stronger, as the best available data mixes structural and physiological strength (Sermet et al., 2019).

In our previous work (Roussel et al., 2023), we linked mouse inhibitory me-models to transcriptomic types (t-types) in a whole mouse cortex transcriptomic dataset (Gouwens et al., 2019). This can provide a direct correspondence in future large-scale mouse models. As we model only a single electrical type for pyramidal cells, there is no one-to-one correspondence between our me-models and the 10 different pyramidal cell types identified there. We are not currently aware of any method which can recreate the electrical features of different types of pyramidal cells using only generic ion channel models. To achieve the firing pattern behavior of more specific electrical types, usually ion channel kinetics are tweaked, and this would violate the compartmentalization of parameters. In future we hope to build morpho-electric-transcriptomic type (met-type) models by selecting gene-specific ion channel models (Ranjan et al., 2019; Ranjan et al., 2024) based on the met-type’s gene expression. Data specific to different neuron sections (i.e., soma, AIS, apical/basal dendrites) of different met-types, such as gene expression, distribution of ion channels, and voltage recordings under standard single cell protocols, would be particularly useful.

Finally, when required, we generalized data from different animals and brain regions to build our rat nbS1 model. This is the accepted state-of-the-art in computational neuroscience, for example, all 19 data-driven models of rodent microcircuitry listed in Figure 2 of the recent review of Ramaswamy, 2024 conduct some sort of mixing, including the advanced mouse V1 model of the Allen Institute (Billeh et al., 2020). Whilst a first truly single species model would be a great advance, it is not required to be the basis of in silico research. Our model (and previous ones) can be used to study the many cortical mechanisms that are common to the closely related organisms rat and mouse. This paper and its companion paper serve to present a methodology for modeling micro- and mesoscale anatomy and physiology, which can be applied for other cortical regions and species. With the rapid increase in openly available data, efforts are already in progress to build models of mouse brain regions with reduced reliance on data mixing thanks to much larger quantities of available atlas-based data. This also includes data for the validation of emergent network-level activity. Here we chose to compare network-level activity to data mostly from the barrel cortex, as well as a single study from primary visual cortex. Whilst a lot of the data used to build the model was from the barrel cortex, the barrel cortex also represents a very well characterized model of cortical processing for simple and controlled sensory stimuli. The initial comparison of population-wise responses in response to accurate thalamic input for single whisker deflections was essential to demonstrating that the model was closer to in vivo, and we were unaware of similar data for non-barrel somatosensory regions. Moreover, our optogenetic and lesion study demonstrated the capacity to compare and extend studies of canonical cortical processing in the whisker system.

However, we cannot always predict the effect caused by mixing data sources. An interesting example of this is described in the companion paper (Reimann et al., 2024), stating that certain rules of inhibitory connectivity found in a mouse electron-microscopic (EM) connectome (Schneider-Mizell et al., 2025) cannot be explained by an non-selective pruning axo-dendritic appositions. We demonstrated the utility of the model to explore the effect of any connectivity rule, using the example of a mouse EM-compatible ‘SM-connectome’. After rewiring and refitting synaptic, and conductance injection parameters, we found that the purely perisomatic targeting by PV+ neurons and the increased inhibitory targeting by VIP+ neurons shifted how much individual layers were driven by intrinsic and extrinsic excitation. This could not be explored with current techniques in vivo and highlights a strength of in silico experimentation. On the other hand, this data source contradicts some of our rat nbS1 parameters, for example, the number of synapses per connection from light microscopy (Markram et al., 2015) do not match with the ones obtained from mouse V1 EM. Thus we used the rat data sources for building our baseline model, but make both models available in a simulatable format (see ‘Data availability’).

Simulatable models and simulation data are made available at the following links: - Anatomical and physiological model (nbS1): http://www.doi.org/10.7910/DVN/HISHXN - Anatomical and physiological model (seven column subvolume): http://www.doi.org/10.5281/zenodo.7930276 - SM-connectome reproducing inhibitory trends from MICrONS with instructions of how to combine with the original seven column subvolume model: http://www.doi.org/10.5281/zenodo.10677883 - Simulation data: http://www.doi.org/10.5281/zenodo.17793038. The following data sourced used to construct the model are published in the following articles and made available at the following links: Anatomical model & Neuron morphology reconstructions (Reimann et al., 2024): http://www.doi.org/10.5281/zenodo.6906785 - Electrical neuron models & ion channel models (Reva et al., 2023): http://www.doi.org/10.5281/zenodo.7930276 - Volumetric atlases (Bolaños-Puchet et al., 2024): http://www.doi.org/10.5281/zenodo.8165004 - PSP ampitudes, CV of PSP amplitudes & mPSC frequencies, listed with their sources in the respective tables of Supplementary files 5–7.

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