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Interneuronal targets of spiny cells axons. The major classes of spiny excitatory neocortical neurones (blue) and their local circuit connections onto inhibitory interneurones. Red cells are those for which significant inputs from each of the classes of presynaptic neurones indicated have been demonstrated with paired intracellular recordings. Purple cells are the most likely candidates for postsynaptic targets where connections have been recorded, but the cell class(es) involved have not been studied in detail. White cells indicate types of interneurones that have been tested but which appear not to be significant targets for that type of presynaptic axon. These include layer 6 interneurones which receive little input from layer 6 CC pyramidal cells (but a strong input from CT cells) and many layer 3 interneurones not apparently innervated by layer 4 spiny cells. Only rarely have inputs from layer 4 spiny cells been recorded in layer 3 interneurones and these involved only the larger basket-type cells. Three broad groups of interneurones are included. Small, medium and large multipolar, fast spiking cells are indicated by round somata with radially extending dendrites. Many of these cells are PV-immunopositive basket cells, but in the cartoon these symbols also include (for simplicity) large adapting CCK basket cells and small VIP/CCK basket cells. The three major groups of bitufted, typically SOM-immunopositive cells are indicated by the larger fusiform somata with vertically oriented bitufted dendritic arbours. Small fusiform bipolar cells are also illustrated. An attempt is made to indicate the overall dimensions of the interneuronal classes, but for the larger cells these groups also include larger cells with longer dendrites.
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This review aims to summarize data obtained with different techniques to provide a functional map of the local circuit connections made by neocortical neurones, a reference for those interested in cortical circuitry and the numerical information required by those wishing to model the circuit. A brief description of the main techniques used to study...
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... The model includes a realistic account of the number of neurons (modeled according to Izhikevich 2004, Izhikevich 2003) and connectivity (Supplemental Table 1) within a barrel column for Layers (L) 2/3, with inputs arriving from the L4, mimicking the conditions in the in vitro/in vivo experiments. 13 different types of cortical neurons are included in the model (Markram et al. 2004;Thomson and Lamy 2007;Oberlaender et al. 2012). In L2/3, there are 9 types of neurons, 2 excitatory: L2 pyramidal neurons and L3 pyramidal neurons (Brecht et al. 2003;Feldmeyer et al. 2006); 7 inhibitory: PV+ fast-spiking neurons (Holmgren et al. 2003;Packer and Yuste 2011), PV+ bursting neurons (Blatow et al. 2003), SST+ Martinotti neurons (Wang et al. 2004;Kapfer et al. 2007;Fino and Yuste 2011), neurogliaform cells (Tamás et al. 2003;Wozny and Williams 2011), CR+ bipolar neurons (Xu et al. 2006;Caputi et al. 2009), CR+/VIP+ multipolar neurons (Caputi et al. 2009), and VIP+/CR− neurons (Porter et al. 1998). ...
... Our results suggest that excitatory and inhibitory neurons might focus on distinct individual and population information to optimize the availability of stimulus information in local networks. Since long-range projections of inhibitory neurons are rare (Thomson and Lamy 2007), the information content in the spiking of the inhibitory neurons is likely to be most relevant for local processing. ...
Transformation of postsynaptic potentials into action potentials is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein that somatic postsynaptic potentials accurately represent stimulus location on a trial-by-trial basis in single neurons, even 4 synapses away from the sensory periphery in the whisker system. This information is largely lost during action potential generation but can be rapidly (<20 ms) recovered using complementary information in local populations in a cell-type-specific manner. These results show that as sensory information is transferred from one neural locus to another, the circuits reconstruct the stimulus with high fidelity so that sensory representations of single neurons faithfully represent the stimulus in the periphery, but only in their postsynaptic potentials, resulting in lossless information processing for the sense of touch in the primary somatosensory cortex.
... The electron microscopic analyses were performed in layer III. The pyramidal cells located in layer III are the largest source of cortico-cortical axon projections (Felleman and Van Essen 1991;Thomson and Lamy 2007;Barbas 2015;D'Souza and Burkhalter 2017;Rockland 2019) and this layer has been extensively studied using physiological and morphological analysis (e.g. see Eyal et al. 2018;Gidon et al. 2020;Galakhova et al. 2022;Benavides-Piccione et al. 2024 and references therein). ...
Functional and structural studies investigating macroscopic connectivity in the human cerebral cortex suggest that high-order associative regions exhibit greater connectivity compared to primary ones. However, the synaptic organization of these brain regions remains unexplored. In the present work, we conducted volume electron microscopy to investigate the synaptic organization of the human brain obtained at autopsy. Specifically, we examined layer III of Brodmann areas 17, 3b, and 4, as representative areas of primary visual, somatosensorial, and motor cortex. Additionally, we conducted comparative analyses with our previous datasets of layer III from temporopolar and anterior cingulate associative cortical regions (Brodmann areas 24, 38, and 21). 9,690 synaptic junctions were 3D reconstructed, showing that certain synaptic characteristics are specific to particular regions. The number of synapses per volume, the proportion of the postsynaptic targets, and the synaptic size may distinguish one region from another, regardless of whether they are associative or primary cortex. By contrast, other synaptic characteristics were common to all analyzed regions, such as the proportion of excitatory and inhibitory synapses, their shapes, their spatial distribution, and a higher proportion of synapses located on dendritic spines. The present results provide further insights into the synaptic organization of the human cerebral cortex.
... Locally, L2/3 ITs send prominent descending axonal projections to L5 pyramidal cells (Weiler et al., 2008;Lefort et al., 2009;Petreanu et al., 2009;Harris and Shepherd, 2015). This robust output to L5 has been identified as an essential feature of neocortical microcircuitry, preserved in most regions and species (Thomson and Lamy, 2007;Weiler et al., 2008;Hooks et al., 2011). Long-range axons of supragranular pyramids establish connexions with ipsi-and contralateral cortical regions and the striatum (Petreanu et al., 2007;Anderson et al., 2010;Pidoux et al., 2011a). ...
... In the late 1980s, the observation of similarities in the composition and distribution of cortical neurons between species gave rise to the idea that a common canonical microcircuit might exist in mammals (Douglas et al., 1989;Douglas and Martin, 2004). Although the multidimensional nature of connectivity schemes rules out the possibility of a single cortical circuit, comparative analysis of the data accumulated in placental mammals does suggest the presence of shared principles of organisation and function (see Silberberg et al., 2002;Douglas and Martin, 2004;Thomson and Lamy, 2007;Harris and Shepherd, 2015 for reviews). The key stages in the integration of sensory input across cortical layers are described below, drawing primarily on research in rodents and cats. ...
... These thalamocortical projections predominantly terminate on L4, but also at the boundary between L5 and L6. For their part, axons from higherorder thalamic nuclei primarily target L1 and L5, while avoiding L4 neurons (Thomson and Lamy, 2007;Petreanu et al., 2009;Constantinople and Bruno, 2013;Harris and Shepherd, 2015). L4 is considered as the main thalamorecipient layer and the starting point of intracortical sensory processing. ...
Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.
... The construction of mammalian cortical microcircuit models, incorporating a vast diversity of genetically, morphologically, and electrophysiologically different neuron types, has been a long-standing challenge due to limited insights into their specific connectivities. Despite these complexities, recent studies [33][34][35] have culminated in a detailed cortical microcircuit model of the mouse V1 area [32] (Figs 1A and 2), which effectively replicates the computational , and L6, whereas L1 contains a single inhibitory Htr3a neuron type. Each of these neuron types is subdivided into various models of neurons. ...
In an ever-changing visual world, animals’ survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons’ responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.
... This is because, mixing at a level not directly involved in the generation of spindles (i.e., cortex), can be done with a broad dynamical range (fine knob), because the cortex is entrained by the spindle generator level (thalamoreticular), rather than being a generator itself. Cortical mixing is likely the prevalent mode of mixing of TC circuits, because it relies on interlaminar connectivity that constitutes the main component of cortical microcircuitry, that is, projections from neurons in L3/4 to pyramidal neurons in L5, or core cortical nodes in L4 and L6 receiving L1-3 signals that are also influenced by a wide kernel of matrix inputs (Krishnan et al., 2018;Thomson & Lamy, 2007;Yoshimura, Dantzker, & Callaway, 2005). To this effect, recent physiological studies have highlighted the laminar heterogeneity of human sleep spindles (Hagler et al., 2018;Krishnan et al., 2018;Thomson & Lamy, 2007;Yoshimura et al., 2005). ...
... Cortical mixing is likely the prevalent mode of mixing of TC circuits, because it relies on interlaminar connectivity that constitutes the main component of cortical microcircuitry, that is, projections from neurons in L3/4 to pyramidal neurons in L5, or core cortical nodes in L4 and L6 receiving L1-3 signals that are also influenced by a wide kernel of matrix inputs (Krishnan et al., 2018;Thomson & Lamy, 2007;Yoshimura, Dantzker, & Callaway, 2005). To this effect, recent physiological studies have highlighted the laminar heterogeneity of human sleep spindles (Hagler et al., 2018;Krishnan et al., 2018;Thomson & Lamy, 2007;Yoshimura et al., 2005). ...
Sleep spindles are associated with the beginning of deep sleep and memory consolidation and are disrupted in schizophrenia and autism. In primates, distinct core and matrix thalamocortical (TC) circuits regulate sleep spindle activity through communications that are filtered by the inhibitory thalamic reticular nucleus (TRN); however, little is known about typical TC network interactions and the mechanisms that are disrupted in brain disorders. We developed a primate-specific, circuit-based TC computational model with distinct core and matrix loops that can simulate sleep spindles. We implemented novel multilevel cortical and thalamic mixing, and included local thalamic inhibitory interneurons, and direct layer 5 projections of variable density to TRN and thalamus to investigate the functional consequences of different ratios of core and matrix node connectivity contribution to spindle dynamics. Our simulations showed that spindle power in primates can be modulated based on the level of cortical feedback, thalamic inhibition, and engagement of model core versus matrix, with the latter having a greater role in spindle dynamics. The study of the distinct spatial and temporal dynamics of core-, matrix-, and mix-generated sleep spindles establishes a framework to study disruption of TC circuit balance underlying deficits in sleep and attentional gating seen in autism and schizophrenia.
... We assume that 1 ≪ K ≪ N, which means that the average number of connections is large but much smaller than the network size. This is a relevant limit for microcircuits in the brain, where each neuron receives a large number of inputs (K ∼ 1000), and connection probabilities are small, of order ∼10% in cortex [35][36][37][38][39] and 1% in hippocampus [40]. ...
... 4(a) and 6(b) (red line), similar to Ref. [52], we neglect the dynamics of the Δ parameters, and expand the overlap equations [Eqs. (37) and (38)] around the memory state m s and m 0 ¼ 0, leading to ...
... Our network simulations show that this approximation matches well the network dynamics (see Figs. 5 and 6) suggesting that the observed memory state instability is mainly governed by the overlap dynamics in Eqs. (37) and (38). ...
Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model, we investigate a model in which new memories are learned continuously and old memories are forgotten, using an online synaptic plasticity rule. We show that for a forgetting timescale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations. Therefore, fixed-point and chaotic attractors coexist in the network phase space. The network presents a continuum of statistically distinguishable memory states, where chaotic fluctuations appear abruptly above a critical age and then increase gradually until the memory disappears. We develop a dynamical mean field theory to analyze the age-dependent dynamics and compare the theory with simulations of large networks. We compute the optimal forgetting timescale for which the number of stored memories is maximized. We found that the maximum age at which memories can be retrieved is given by an instability at which old memories destabilize and the network converges instead to a more recent one. Our numerical simulations show that a high degree of sparsity is necessary for the dynamical mean field theory to accurately predict the network capacity. To test the robustness and biological plausibility of our results, we study numerically the dynamics of a network with learning rules and transfer function inferred from in vivo data in the online learning scenario. We found that all aspects of the network’s dynamics characterized analytically in the simpler model also hold in this model. These results are highly robust to noise. Finally, our theory provides specific predictions for delay response tasks with aging memoranda. In particular, it predicts a higher degree of temporal fluctuations in retrieval states associated with older memories, and it also predicts fluctuations should be faster in older memories. Overall, our theory of attractor networks that continuously learn new information at the price of forgetting old memories can account for the observed diversity of retrieval states in the cortex, and in particular, the strong temporal fluctuations of cortical activity.
... In particular, connections across PyCs and from PyCs to BaCs were modelled as highly depressing (Costa et al., 2013), thalamocortical connections as strong and strongly depressing , all inhibitory connections originating from BaCs, and notably those targeting MaCs, as moderately depressing (Ma et al., 2012;Thomson, 2007). All inhibitory connections originating from MaCs were modelled with static synapses (Ma et al., 2012) Markram (2007). ...
The mechanisms leading to the alternation between active (UP) and silent (DOWN) states during sleep slow waves (SWs) remain poorly understood. Previous models have explained the transition to the DOWN state by a progressive failure of excitation because of the build‐up of adaptation currents or synaptic depression. However, these models are at odds with recent studies suggesting a role for presynaptic inhibition by Martinotti cells (MaCs) in generating SWs. Here, we update a classical large‐scale model of sleep SWs to include MaCs and propose a different mechanism for the generation of SWs. In the wake mode, the network exhibits irregular and selective activity with low firing rates (FRs). Following an increase in the strength of background inputs and a modulation of synaptic strength and potassium leak potential mimicking the reduced effect of acetylcholine during sleep, the network enters a sleep‐like regime in which local increases of network activity trigger bursts of MaC activity, resulting in strong disfacilitation of the local network via presynaptic GABAB1a‐type inhibition. This model replicates findings on slow wave activity (SWA) during sleep that challenge previous models, including low and skewed FRs that are comparable between the wake and sleep modes, higher synchrony of transitions to DOWN states than to UP states, the possibility of triggering SWs by optogenetic stimulation of MaCs, and the local dependence of SWA on synaptic strength. Overall, this work points to a role for presynaptic inhibition by MaCs in the generation of DOWN states during sleep.
... Lastly, we examined the distribution of CCG classes across pairs of neurons involving layer 2/3 neurons. A wealth of evidence indicates that layer 2/3 neurons provide a major source of output to other neocortical areas (reviewed in Callaway, 1998;Douglas and Martin, 2004;Felleman and Van Essen, 1991;Harris and Shepherd, 2015;Thomson and Lamy, 2007). In macaque V1, layer 2/3 neurons send projections to higher visual areas such as V2 (Livingstone and Hubel, 1984;Rockland, 1992;Sincich and Horton, 2005) and V4 (Yukie and Iwai, 1985), and receive inputs from all the deeper cortical layers, including layers 4cα, 4cβ, 4A, 4B, 5, and 6 Callaway, 1998;Callaway and Wiser, 1996;Fitzpatrick et al., 1985;Kisvarday et al., 1989;Lachica et al., 1992; Lund and Boothe, 1975;Sawatari and Callaway, 2000;Vanni et al., 2020;Wiser and Callaway, 1996;Yarch et al., 2017;Yoshioka et al., 1994; Figure 7a). ...
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
... The copyright holder for this preprint (which this version posted April 29, 2022. ; https://doi.org/10.1101/2022.04.27.489802 doi: bioRxiv preprint by a wide kernel of matrix inputs (Krishnan et al., 2018;Thomson, 2007;Yoshimura et al., 2005). To this effect, recent physiological studies have highlighted the laminar heterogeneity of human sleep spindles (Hagler et al., 2018;Krishnan et al., 2018;Thomson, 2007;Yoshimura et al., 2005), but also the extensive mixing of spindles across layers of the human cortex (Ujma et al., 2021). ...
... ; https://doi.org/10.1101/2022.04.27.489802 doi: bioRxiv preprint by a wide kernel of matrix inputs (Krishnan et al., 2018;Thomson, 2007;Yoshimura et al., 2005). To this effect, recent physiological studies have highlighted the laminar heterogeneity of human sleep spindles (Hagler et al., 2018;Krishnan et al., 2018;Thomson, 2007;Yoshimura et al., 2005), but also the extensive mixing of spindles across layers of the human cortex (Ujma et al., 2021). ...
Sleep spindles are associated with the beginning of deep sleep and memory consolidation and are disrupted in schizophrenia and autism. In primates, distinct core and matrix thalamocortical (TC) circuits regulate sleep-spindle activity, through communications that are filtered by the inhibitory thalamic reticular nucleus (TRN) however, little is known about typical TC network interactions and the mechanisms that are disrupted in brain disorders. We developed a primate-specific, circuit-based TC computational model with distinct core and matrix loops that can simulate sleep spindles. We implemented novel multilevel cortical and thalamic mixing, and included local thalamic inhibitory interneurons, and direct layer 5 projections of variable density to TRN and thalamus to investigate the functional consequences of different ratios of core and matrix node connectivity contribution to spindle dynamics. Our simulations showed that spindle power in primates can be modulated based on the level of cortical feedback, thalamic inhibition, and engagement of model core vs. matrix, with the latter having a greater role in spindle dynamics. The study of the distinct spatial and temporal dynamics of core-, matrix-, and mix-generated sleep spindles establishes a framework to study disruption of TC circuit balance underlying deficits in sleep and attentional gating seen in autism and schizophrenia.
... 1101 Lastly, we examined the distribution of CCG classes across pairs of neurons involving layer 2/3 neurons. A wealth of evidence indicates that layer 2/3 neurons provide a major source of output to other neocortical areas (reviewed in Callaway, 1998;Douglas & Martin, 2004;Felleman & Van Essen, 1991;Harris & Shepherd, 2015;Thomson & Lamy, 2007)). In macaque V1, layer 2/3 neurons send projections to higher visual areas such as V2 (Livingstone & Hubel, 1984;Rockland, 1992;Sincich & Horton, 2005) and V4 (Yukie & Iwai, 1985), and receive inputs from all the deeper cortical layers, including layer 4C , 4C , 4A, 4B, 5 and 6 Callaway, 1998;Kisvarday, Cowey, Smith, & Somogyi, 1989;Lachica, Beck, & Casagrande, 1992;Jennifer S Lund & Boothe, 1975;Sawatari & Callaway, 2000;Vanni, Hokkanen, Werner, & Angelucci, 2020;Yarch, Federer, & Angelucci, 2017;Yoshioka, Levitt, & Lund, 1994) (Fig. 7a). ...
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional connections between neurons thereby providing an unprecedented view of local circuit interactions. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally connected neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the strength and synchrony of functional connections across the cortical column. Despite neurons residing within the same column, both measures of functional connectivity depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of connected pairs to categorize functional connections between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional connections within the full population. These classes of functional connections were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
