Figure 4
Excitatory synaptic input–output relationship in layer 4 of the S1 barrel cortex. (A) Reconstructions of a L4 spiny stellate cell (left) and a L4 star pyramidal neuron (right) in rat barrel cortex (Feldmeyer et al., 1999). Modified with permission of John Wiley and Sons on behalf of The Physiological Society. (B) Diagram of the excitatory synaptic connections of and onto L4 spiny neurons (red neuron with blue axon) in the barrel cortex. Although layer 4 contains both spiny stellate and star pyramidal neurons and a few pyramidal cells only spiny stellate cells are shown for simplicity. Note that L4 spiny neurons provide synaptic output to virtually all layers in a barrel column. For detailed information on the location of synaptic contacts and differences in the connectivity of the three different excitatory L4 neurons see text. The thalamic region is represented by a single barreloid in the VPM nucleus of the thalamus; the VPM/POm border is marked by a dashed line. Red neuron; Dendrites and axon of the neuron for which the input–output relationship is described in this figure. Different cortical layers as indicated on the left. The thickness of the red arrows pointing to a postsynaptic (black) neurons indicates the connection probability between this and the black neurons as well as cortical and subcortical areas. The dashed red arrow in layer 5 marks a likely but not yet verified synaptic connection onto a corticocallosal L5 pyramidal cell. It should be noted that Black neurons: Dendrites and axon of neurons sending to and receiving synaptic input from to the red neuron. The thickness of the black arrows pointing to the red neuron indicates the connection probability between these neurons and the red neuron. Light blue arrows: Excitatory synaptic input from cortical regions outside the S1 barrel cortex. Magenta arrow: Synaptic input from the VPM (lemniscal (1) pathway. Green arrow: Synaptic input from the POm (paralemniscal pathway). However, for L4 spiny neurons synaptic input from outside thhe barrel cortex originates almost exclusively from the core of the barreloid in the dorsomedial part of the VPM. Abbreviations: VPM, ventroposterior medial nucleus of the thalamus; dm, dorsomedial part; vl, ventrolateral part; POm, posterior medial nucleus of the thalamus; L2P, L2 pyramidal cell; L3P, L3 pyramidal cell; L4SN, L4 spiny neuron; stL5P, slender-tufted L5A pyramidal cell; ttL5BP, thick-tufted L5B pyramidal cell; calL5P, corticocallosal L5 pyramidal cell; ccL6AP, corticocortical L6A pyramidal cell; ctL6AP, corticothalamic L6A pyramidal cell.
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Neocortical areas are believed to be organized into vertical modules, the cortical columns, and the horizontal layers 1-6. In the somatosensory barrel cortex these columns are defined by the readily discernible barrel structure in layer 4. Information processing in the neocortex occurs along vertical and horizontal axes, thereby linking individual...
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... a few L4-L4 connections, single uEPSPs were found to be sufficiently large to evoke action potentials ). The L4-L4 connection is almost the only intracortical synaptic input L4 spiny neurons receive ( Figure 4B). The con- nectivity ratios with excitatory neurons in all other layers of S1 barrel cortex are extremely low, often below 1% ( Lefort et al., 2009). ...
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Citations
... Connexions from ETs to ITs are less numerous (Morishima and Kawaguchi, 2006;Brown and Hestrin, 2009;Kiritani et al., 2012); ET intracortical projections mainly contributing to inter-areal communications (Nelson et al., 2013;Ueta et al., 2013;Harris and Shepherd, 2015). Most of inputs to L6 ITs in sensory cortices originate from local deep-layer neurons, while L6 ETs are primarily innervated by axons from higher-order cortical areas (Zhang and Deschênes, 1998;Mercer et al., 2005;Feldmeyer, 2012;Vélez-Fort et al., 2014). ...
... L4 principal neurons comprise two types of glutamatergic cells: spiny stellate and star pyramidal cells, which have broadly similar functional properties but vary in proportion between areas and species. In rodents, spiny stellate cells are abundant in the primary somatosensory cortex, but rare in the visual cortex (Peters and Kara, 1985;Feldmeyer, 2012). Conversely, these cells are prevalent in the primary visual cortex of cats, monkeys and humans, while remaining sparse and randomly distributed among pyramidal cells in the auditory cortex (Lund, 1984;Meyer et al., 1989;Smith and Populin, 2001;Nassi and Callaway, 2009). ...
... L2/3 ITs represent the second level of intracortical processing, distributing information within and beyond their home column through local and long-range corticocortical outputs. Depending on the behavioural context, sensory signals in L2/3 are further modulated by the integration of non-sensory information from other primary and/or associative cortical areas and from the thalamus (Feldmeyer, 2012;Harris and Shepherd, 2015). In vivo recordings revealed relatively low spontaneous and evoked firing rates in superficial pyramidal neurons, at least in rodents (de Kock and Sakmann, 2008;Sakata and Harris, 2009;O'Connor et al., 2010;Carton-Leclercq et al., 2023). ...
... PV interneurons and gamma oscillations have both been linked to a variety of psychiatric conditions [14,[16][17][18]. Primary somatosensory barrel cortex (BC) is an attractive place to study PV interneurons because of its well-defined functions and architecture [19][20][21][22]. BC is defined by the presence of barrels, cytoarchitectural units in L4 which each correspond to a single vibrissa [23]. ...
Parvalbumin (PV) interneurons are inhibitory fast-spiking cells with essential roles in directing the flow of information through cortical circuits. These neurons set the balance between excitation and inhibition and control rhythmic activity. PV interneurons differ between cortical layers in their morphology, circuitry, and function, but how their electrophysiological properties vary has received little attention. Here we investigate responses of PV interneurons in different layers of primary somatosensory barrel cortex (BC) to different excitatory inputs. With the genetically-encoded hybrid voltage sensor, hVOS, we recorded voltage changes in many L2/3 and L4 PV interneurons simultaneously, with stimulation applied to either L2/3 or L4. A semi-automated procedure was developed to identify small regions of interest corresponding to single responsive PV interneurons. Amplitude, half-width, and rise-time were greater for PV interneurons residing in L2/3 compared to L4. Stimulation in L2/3 elicited responses in both L2/3 and L4 with longer latency compared to stimulation in L4. These differences in latency between layers could influence their windows for temporal integration. Thus, PV interneurons in different cortical layers of BC respond in a layer specific and input specific manner, and these differences have potential roles in cortical computations.
... On the other hand, they could not make claims about the specific patterns of synaptic connectivity they originate from, as they could only predict functional connectivity from correlations in neuronal activity, but did not have access to the underlying structural connectivity of the neurons recorded. Additionally, results based on calcium imaging are limited to the superficial layers of the cortex, missing potential assemblies in the deeper layers, which would be of great interest as they serve as the output of the cortex [14,15]. ...
... Conversely, 0-indegree with respect to the late assembly decreases the membership probability for early, and most of the middle assemblies (blue curves with negative slope on the first and second panels of Fig 4D). This reflects the temporal order of their activation and the fact that neurons in the deeper layers, that dominate the late assembly, mostly project outside the cortex and not back to superficial layers [14,15]. The nI values across the diagonals for k = 1, 2 are larger than for k = 0, for all assemblies except the late one. ...
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb’s postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.
... Local neuronal activity (e.g., in the SSp-bfd) is influenced by inputs from multiple brain regions through direct and indirect afferent connections 29,30 . To fully understand local computation, it is therefore critical to simultaneously monitor activity in connected brain areas that are often distributed throughout the brain. ...
Blood Oxygen Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows for the non-invasive, indirect recording of neural activity across the whole brain in both animals and humans. However, the relationship between the local neural population activity and the vascular activity throughout the rodent brain is not completely understood. To investigate this relationship, we introduce a novel MRI compatible single-photon microscope capable of measuring cellular resolution Ca2+ activity of genetically defined neurons during whole-brain BOLD fMRI in awake behaving mice. Using this combined imaging approach, we found a difference in activity patterns between cells which was dependent on their location with respect to the vasculature. Notably, neurons near the vasculature showed pronounced negative activity during contralateral whisker movements at 3 Hz. In a second proof of concept experiment, we demonstrated the potential of recording both local neural activities, like those in the barrel field (SSp-bfd), and BOLD fMRI readings from interlinked brain regions. In sum, the presented technological advancement paves the way for studies examining the interplay between local brain circuits and overarching brain functions. In addition, the new approach enhances our understanding of the intricate vascular BOLD fMRI signal, providing insights into the determinants of local neurovascular functions and the brain's organizational framework across various scales.
... To mitigate against this, we discarded brains in which fb label is found in VPm or VPl. VPm neurons project to L4 and can target the apical tufts specifically in L2/3 (Killackey and Ebner 1973;Wimmer et al. 2010;Feldmeyer 2012;Oberlaender et al. 2012;Sermet et al. 2019;Guest et al. 2021). This approach mitigates against but does not completely eliminate connections that might target L2/3. ...
Neocortical layer 1 has been proposed to be at the center for top-down and bottom-up integration. It is a locus for interactions between long-range inputs, layer 1 interneurons, and apical tuft dendrites of pyramidal neurons. While input to layer 1 has been studied intensively, the level and effect of input to this layer has still not been completely characterized. Here we examined the input to layer 1 of mouse somatosensory cortex with retrograde tracing and optogenetics. Our assays reveal that local input to layer 1 is predominantly from layers 2/3 and 5 pyramidal neurons and interneurons, and that subtypes of local layers 5 and 6b neurons project to layer 1 with different probabilities. Long-range input from sensory-motor cortices to layer 1 of somatosensory cortex arose predominantly from layers 2/3 neurons. Our optogenetic experiments showed that intra-telencephalic layer 5 pyramidal neurons drive layer 1 interneurons but have no effect locally on layer 5 apical tuft dendrites. Dual retrograde tracing revealed that a fraction of local and long-range neurons was both presynaptic to layer 5 neurons and projected to layer 1. Our work highlights the prominent role of local inputs to layer 1 and shows the potential for complex interactions between long-range and local inputs, which are both in position to modify the output of somatosensory cortex.
... Mirroring the topographic, grid-like organization of whiskers on the contralateral whisker pad, L4 "barrel" regions (sometimes referred to as "hollows" to distinguish them from barrel cortex itself) are highly populated by clusters of spiny stellate neurons, with asymmetric dendritic fields (lacking an apical dendrite) directed toward dense thalamocortical axon (TCA) terminals that represent input arising from a single whisker. Located in the narrow regions interspersed between each barrel, L4 "septal" neurons possess a pyramidal morphology with distinct connectivity compared with their neighboring neurons in the barrel 7 . Whereas L4 stellate neurons receive thalamic input from ventroposteromedial (VPM) nucleus and have local connectivities that are mainly confined to within their home barrel column, L4 septal neurons display more widespread cortical interactions with their inputs coming from the medial part of the posterior medial (POm) thalamic nucleus [8][9][10] . ...
... Whereas L4 stellate neurons receive thalamic input from ventroposteromedial (VPM) nucleus and have local connectivities that are mainly confined to within their home barrel column, L4 septal neurons display more widespread cortical interactions with their inputs coming from the medial part of the posterior medial (POm) thalamic nucleus [8][9][10] . As such, barrel and septal circuits broadly refer to functional cortical circuits that are vertically aligned and connected with their corresponding L4 cell types/regions, distinctly encoding for whisker-specific spatiotemporal information and multi-whisker kinetic information, respectively 7,8,11 . ...
Excitatory spiny stellate neurons are prominently featured in the cortical circuits of sensory modalities that provide high salience and high acuity representations of the environment. These specialized neurons are considered developmentally linked to bottom-up inputs from the thalamus, however, the molecular mechanisms underlying their diversification and function are unknown. Here, we investigated this in mouse somatosensory cortex, where spiny stellate neurons and pyramidal neurons have distinct roles in processing whisker-evoked signals. Utilizing spatial transcriptomics, we identified reciprocal patterns of gene expression which correlated with these cell-types and were linked to innervation by specific thalamic inputs during development. Genetic manipulation that prevents the acquisition of spiny stellate fate highlighted an important role for these neurons in processing distinct whisker signals within functional cortical columns, and as a key driver in the formation of specific whisker-related circuits in the cortex.
... The population of glutamatergic excitatory neurons consists of pyramidal cells (PCs) and spiny stellate cells (spSCs) (for details on excitatory neuronal connectivity, see Feldmeyer [4]). The cell body of PCs can be located in every layer except L1 and the apical dendrite transverses several layers often forming an apical tuft in L1 and L2/3. ...
The neocortical network consists of two types of excitatory neurons and a variety of GABAergic inhibitory interneurons, which are organized in distinct microcircuits providing feedforward, feedback, lateral inhibition, and disinhibition. This network is activated by layer- and cell-type specific inputs from first and higher order thalamic nuclei, other subcortical regions, and by cortico-cortical projections. Parallel and serial information processing occurs simultaneously in different intracortical subnetworks and is influenced by neuromodulatory inputs arising from the basal forebrain (cholinergic), raphe nuclei (serotonergic), locus coeruleus (noradrenergic), and ventral tegmentum (dopaminergic). Neocortical neurons differ in their intrinsic firing pattern, in their local and global synaptic connectivity, and in the dynamics of their synaptic interactions. During repetitive stimulation, synaptic connections between distinct neuronal cell types show short-term facilitation or depression, thereby activating or inactivating intracortical microcircuits. Specific networks are capable to generate local and global activity patterns (e.g., synchronized oscillations), which contribute to higher cognitive function and behavior. This review article aims to give a brief overview on our current understanding of the structure and function of the neocortical network.
... Layer IV is the only one among the cortical layers that sends strong functional excitatory connections to all the other layers within the column (Staiger et al, 2000). Instead, only layer V receives functional excitatory inputs from all the other cortical layers within the column (Staiger et al, 2000, Feldmeyer, 2012. In the sensory cortex, these columns form a spatial pattern similar to the arrangement of the whiskers on the mystacial pad. ...
... In Figure 3, center row, 750 µm, the signal is coming from the top left corner (frame at 16.3 ms) and spreads quickly to the whole matrix (frame at 20.1 ms). This finding agreed with the known functional architecture of the barrel cortex, as this layer is the main target of thalamic axons (from VPM) and acts as a "hub" of intracolumnar information processing, distributing the signal coming from the whisker to all the other cortical layers in the same barrel-column 6 . Layer Va presented the widest range of behaviors, as two different ways of propagation were found: one coming from the bottom and moving upwards and one coming from the left side moving downwards but usually returning back to the top left corner (like the one in Figure 3, bottom row, 1200 µm). ...
... The evoked signal then faded away, lingering in layer IV ( Figure 4, 5 th column labelled with '23.2 ms', depth range: 600-840 µm) before disappearing completely from the recorded column. There, the predominant contribution to the initial part of the LFP signal was from layer V, apparently in contrast to the "hub" role of layer 4 in the intracolumnar information processing supported by connectionists 6 . Nevertheless, the signal from layer IV spiny-stellate cells is usually overshadowed by that of layer V pyramidal populations, making the reconstructions of the anatomical origin of those signals more difficult. ...
Electrical recordings proved to be an optimal technique to investigate neuronal networks and complex functions as sensory processing, memory and behavior (add ref). In the last 20 years, many advances were made in electrode production, starting from few channels to thousands of sensors embedded on the same probe. The technology behind probe manufacturing is also broad: metal electrodes as well as CMOS-based chips are commonly used in research. However, some probes are specifically designed for basic research and murine models (Carandini et al, neuropixels), thus are difficult to adapt for clinical practice, e.g. for brain implants and deep brain stimulation. To be eligible for medical applications, the ideal probe must have a highly translational capacity/configuration, limited dimensions, high biocompatibility and stable properties for long term implants.
CMOS based probes are promising candidates for clinical use, because of their thin layer of TiO2, which is a highly biocompatible material and can establish a tight capacitive coupling with the neural tissue, that ensure stable and reliable recordings.
... We found that dendritic morphology of IT, PT and CT neurons can be readily separated, with longer tufts in PT neurons, shorter tufts in IT neurons and few tufts in CT neurons, consistent with previous studies 28 (Fig. 2b and Extended Data Fig. 2c). In addition, IT and PT neurons showed larger basal dendrites than CT neurons, whereas CT neurons showed longer apical trunk than IT and PT neurons (Extended Data Fig. 2c). ...
... For CT neurons, we found that, unlike the dendrites of CT neurons reported in the sensory cortex extending only to L4 (ref. 28), the dendrites of CT neurons in PFC can extend to L1 and can be further subdivided by their truck lengths (dendrite subtypes 1 and 2 versus subtypes 3 and 4). The key morphological features distinguishing each dendrite subtype are summarized by a conditional inference tree shown in Extended Data Fig. 2e. ...
... We found that the neurons with different dendrite subtypes were also enriched distinct subtypes of local axons (Fig. 5f,g). For example, L2/3 IT dendrite subtypes 13, 14, 16 and 24 were enriched with a unique local axon subtype 7 arborizing not only in L2/3 but also in L5, resembling the so-called 'butterfly' pattern of axons found previously in sensory cortical neurons, in which they arborize extensively in L2/3 and L5 while avoiding L4 28 . Together, the local axons belonging to the neurons of different dendrite subtypes either descend or ascend to different laminar layers, likely corresponding to the feedforward or feedback function, respectively, in the local cortical circuit. ...
The structures of dendrites and axons form the basis for the connectivity of neural network, but their precise relationship at single-neuron level remains unclear. Here we report the complete dendrite and axon morphology of nearly 2,000 neurons in mouse prefrontal cortex (PFC). We identified morphological variations of somata, dendrites and axons across laminar layers and PFC subregions and the general rules of somatodendritic scaling with cytoarchitecture. We uncovered 24 morphologically distinguishable dendrite subtypes in 1,515 pyramidal projection neurons and 405 atypical pyramidal projection neurons and spiny stellate neurons with unique axon projection patterns. Furthermore, correspondence analysis among dendrites, local axons and long-range axons revealed coherent morphological changes associated with electrophysiological phenotypes. Finally, integrative dendrite–axon analysis uncovered the organization of potential intra-column, inter-hemispheric and inter-column connectivity among projection neuron types in PFC. Together, our study provides a comprehensive structural repertoire for the reconstruction and analysis of PFC neural network.
... We adopt a minimalistic structure of the thalamocortical network by limiting our model to the most important "ingredients", meaning VPM, TRN and barrel cortex layers 4 and 6, while leaving out other important structures and layers. Hence, we just add a direct shortcut connection from L4 to L6 within every cortical column, which is sufficient in our model to demonstrate cortical SSA, although in reality the flow of excitation from L4 will go through L3, L2 and L5 in sequence before arriving at L6 [38][39][40]. Additionally, we assume lateral cortico-cortical connections in layer 6 as they have been demonstrated in the rat barrel cortex [41] and even in layer 6b [42]. These projections are hypothesized to give rise to fast horizontal spread of single whisker-evoked excitation. ...
In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity (MMN) and on the level of single neurons as stimulus-specific adaptation (SSA). Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The "minimalistic" architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities.
