Figure - available from: Frontiers in Neuroscience
This content is subject to copyright.
Regions innervated by the axons of inhibitory interneurones. The major classes of inhibitory interneurones (as outlined for Figure 6) are shown with approximate denditic arbour dimensions (dense color) and typical axonal arbours (transparent color). Proximally targeting small medium and large basket cells are in red (left). Some of the large basket cells in layers 3 and 4 have long horizontal axon branches that form discrete bouton clusters. These larger cells may also have major axons that descend, e.g., to layer 5 from layer 3 or to layer 6 from layer 4. Similarly, large layer 5 basket cells can have two large axonal ramifications, one in layer 5 and one in layer 3. All layers also contain small and medium sized basket cells whose axons and dendrites may be confined to the layer of origin. Dendrite-preferring interneurones are summarized on the right. Larger bitufted interneurones (typically SOM-immunopositive, in blue) include Martinotti cells in layers 2–6 whose fine dense axonal arbours course toward the pial surface and extend horizontally in layer 1, double bouquet cells in layers 3 and 4 with a dense axonal arbour near their origin and a long narrow “mares tail” of descending axons and other bitufted interneurones whose less dense axonal abours extend above and below the soma, but rarely reach layer 1. Bipolar interneurones most commonly found in the upper layers, some of which have long narrow, vertically oriented axonal arbours, are indicated in green. Finally a small multipolar burst firing interneurone is shown close to the layer 1/2 border (light green).
Source publication
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...
Similar publications
We simulate the inhibition of Ia-glutamatergic excitatory postsynaptic potential (EPSP) by preceding it with glycinergic recurrent (REN) and reciprocal (REC) inhibitory postsynaptic potentials (IPSPs). The inhibition is evaluated in the presence of voltage-dependent conductances of sodium, delayed rectifier potassium, and slow potassium in five α-m...
Background
Neuronal hyperactivity related to β-amyloid (Aβ) is considered an early warning sign of Alzheimer disease (AD). Although increasing evidence supports this opinion, the underlying mechanisms are still unknown.
Methods
Here, we recorded whole-cell synaptic currents and membrane potentials using patch clamping of acute hippocampal slices f...
Paraventricular thalamus (PVT) plays important roles in the regulation of emotion and motivation through connecting many brain structures including the midbrain and the limbic system. Although acetylcholine (ACh) neurons of the midbrain were reported to send projections to PVT, little is known about how cholinergic signaling regulates PVT neurons....
Background
Little is known about how the obesogenic environment influences emotional states associated with glial responses and neuronal function. Here, we investigated glial reactivation and neuronal electrophysiological properties in emotion-related brain regions of high-fat diet (HFD) and ob/ob mice under chronic stress.
Methods
The glial react...
The dynamic interactions between synaptic excitation and inhibition (E/I) shape membrane potential fluctuations and determine patterns of neuronal outputs; however, the spatiotemporal organization of these interactions within a single cell is poorly understood. Here, we investigated the relationship between local synaptic excitation and global inhi...
Citations
... 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. ...
... 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.
... 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.
... 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.
... where λ is the characteristic length, assumed to be equal for all types of connections, λ = 50 μm, which roughly corresponds to electrophysiological estimations from paired recordings [83]. In 1-d representation, the cortical area was considered as a segment of the length 2.5mm. ...
The mechanisms determining ictal discharge (ID) propagation are still not clear. In the present study, we aimed to examine these mechanisms in animal and mathematical models of epileptiform activity. Using double-patch and extracellular potassium ion concentration recordings in rat hippocampal-cortical slices, we observed that IDs moved at a speed of about 1 mm/s or less. The mechanisms of such slow propagation have been studied with a mathematical, conductance-based refractory density (CBRD) model that describes the GABA- and glutamatergic neuronal populations’ interactions and ion dynamics in brain tissue. The modeling study reveals two main factors triggerring IDs: (i) increased interneuronal activity leading to chloride ion accumulation and a consequent depolarizing GABAergic effect and (ii) the elevation of extracellular potassium ion concentration. The local synaptic transmission followed by local potassium ion extrusion and GABA receptor-mediated chloride ion accumulation underlies the ID wavefront’s propagation. In contrast, potassium ion diffusion in the extracellular space is slower and does not affect ID’s speed. The short discharges, constituting the ID, propagate much faster than the ID front. The accumulation of sodium ions inside neurons due to their hyperactivity and glutamatergic currents boosts the Na ⁺ /K ⁺ pump, which terminates the ID. Knowledge of the mechanism of ID generation and propagation contributes to the development of new treatments against epilepsy.
... Layer II/III is the output layer for cortico-cortical communication; whereas layer V mainly projects to subcortical nuclei, spinal cord, distant cortical areas (Kandel et al., 2013;Opris & Casanova, 2014;Schüz & Braitenberg, 2001;Shipp, 2007), and thalamus (Collins et al., 2018;Thomson & Lamy, 2007). ...
... "intrinsically burst firing" and "regular spiking" cells (Thomson & Lamy, 2007). ...
... Thalamic inputs arrive mainly in layer IV and I in primates (Kandel et al., 2013;Shipp, 2007;Thomson & Lamy, 2007). Whereas in rodents, it depends on the thalamic nucleus of origin. ...
A growing body of literature is showing an involvement of the cerebellum in managing time predictions and expectations of motor and cognitive events. On the other hand the medial prefrontal cortex (mPFC) is widely considered the area where there is the integration between internal models and cognition. Moreover, mPFC is involved in most of the models for time perception, making it an ideal candidate for handling predictive behaviors. Both tracing experiments and interval timing task highlighted a connection between these two areas. We thus developed a model to investigate the role of the cerebellum in the creation and update of implicit temporal predictions in mPFC of mice. We recorded the extracellular activity in the mPFC (specifically left prelimbic area, PrL) while the head-restrained mice perform the following variable foreperiod task : two cues are presented in a sequence followed by a reward delivered at either a fixed or a variable time point (randomly chosen between two possible delays). At the same time, we photoactivate cerebellar Purkinje cells of L7-Channelrhdopsin2 mice at specific frequencies, above contralateral Crus I. The aim of the optogenetic stimulation is to interfere with neuronal discharge in the mPFC. We confirm the foreperiod effect, already described in the literature, for which responses are faster and more accurate when an interval between a cue and a go signal/reward (foreperiod) is constant. Interestingly we report different behavior of two important prefrontal oscillations: delta (1.5-4Hz) and theta (4-10Hz). They show ramping behavior only when the foreperiod is variable and fixed, respectively. Moreover, cerebellar photostimulation affects these oscillations only if they are ramping. This is probably representative of different neural substrates recruited by the two foreperiod conditions.
... Furthermore, anatomical and physiological studies on non-human animals ( Constantinople and Bruno, 2013 ;Quiquempoix et al., 2018 ) indicate that L2/3 and L5/6 neurons receive direct inputs from L4 neurons, thus serving a secondary function in cortical processing of feedforward inputs. L2/3 pyramidal neurons are critical for receiving top-down signals from higher-level areas, whereas interlaminar links within L2/3 and L5/6 neurons support the temporal integration of feedforward and feedback signals for predicting future sensations ( Kachergis et al., 2014 ;Manita et al., 2015 ;Quiquempoix et al., 2018 ;Roelfsema and Holtmaat, 2018 ;Thomson, 2007 ). Therefore, L2/3 and L5/6 neurons are critical for integrating information related to sensory input with cortical feedback to reduce prediction errors. ...
The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer-specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short-delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.
... According to the obtained neurophysiological data [62,93], the diameter of a microcolumn of a column is d = 30 × 10 −6 m, and it contains 80-100 neurons located at five cellular levels. The microcolumns are combined into macrocolumns D = 500-100 × 10 −6 m containing about 100 microcolumns. ...
... 5. The ability to encode and remember information. [44,62,63,90,93] Available. ...
... 6. Common sensory (receptive) space. [44,46,62,63,71,73,74,90,93] One column for one signal detector. ...
How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of “triggering” a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.
