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Cetartiodactyls include terrestrial and marine species, all generally endowed with a comparatively lateral position of their eyes and a relatively limited binocular field of vision. To this day, our understanding of the visual system in mammals beyond the few studied animal models remains limited. In the present study, we examined the primary visual cortex of Cetartiodactyls that live on land (sheep, Père David deer, giraffe); in the sea (bottlenose dolphin, Risso’s dolphin, long-finned pilot whale, Cuvier’s beaked whale, sperm whale and fin whale); or in an amphibious environment (hippopotamus). We also sampled and studied the visual cortex of the horse (a closely related perissodactyl) and two primates (chimpanzee and pig-tailed macaque) for comparison. Our histochemical and immunohistochemical results indicate that the visual cortex of Cetartiodactyls is characterized by a peculiar organization, structure, and complexity of the cortical column. We noted a general lesser lamination compared to simians, with diminished density, and an apparent simplification of the intra- and extra-columnar connections. The presence and distribution of calcium-binding proteins indicated a notable absence of parvalbumin in water species and a strong reduction of layer 4, usually enlarged in the striated cortex, seemingly replaced by a more diffuse distribution in neighboring layers. Consequently, thalamo-cortical inputs are apparently directed to the higher layers of the column. Computer analyses and statistical evaluation of the data confirmed the results and indicated a substantial correlation between eye placement and cortical structure, with a markedly segregated pattern in cetaceans compared to other mammals. Furthermore, cetacean species showed several types of cortical lamination which may reflect differences in function, possibly related to depth of foraging and consequent progressive disappearance of light, and increased importance of echolocation.

ObjectiveStereotactic-EEG (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMM). However, the relationship between those mathematical models and the physics of the measurements is unclear. In addition, it is challenging to represent SEEG data by combining NMMs and volume conductor models due to the intermediate spatial scale represented by these measurements.ApproachWe provide a framework combining the multicompartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in layer 3, 5 and 6 pyramidal cells due to a synaptic input. With this approach, it is possible to realistically simulate the current source density (CSD) depth profile inside a cortical patch due to inputs localized into a single cortical layer and the induced voltage measured by two SEEG contacts using a volume conductor model. Based on this approach, we built a framework to connect the activity of a NMM with a volume conductor model and we simulated an example of SEEG signal as a proof of concept.Main resultsCSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display substantial differences (of up to a factor of four in magnitude in our example). Thus, the inputs coming from different neural populations do not contribute equally to the electrophysiological recordings. A direct consequence of this is that the raw output of neural mass models is not a good proxy for electrical recordings. We also show that the simplest CSD model that can accurately reproduce SEEG measurements can be constructed from discrete monopolar sources (one per cortical layer).SignificanceOur results highlight the importance of including a physical model in NMMs to represent measurements. We provide a framework connecting microscale neuron models with the neural mass formalism and with physical models of the measurement process that can improve the accuracy of predicted electrophysiological recordings.

Although corticothalamic neurons (CThNs) represent the largest source of synaptic input to thalamic neurons, their role in regulating thalamocortical interactions remains incompletely understood. CThNs in sensory cortex have historically been divided into two types, those with cell bodies in layer 6 (L6) that project back to primary sensory thalamic nuclei and those with cell bodies in layer 5 (L5) that project to higher order thalamic nuclei and subcortical structures. Recently, diversity among L6 CThNs has increasingly been appreciated. In the rodent somatosensory cortex, two major classes of L6 CThNs have been identified: one projecting to the ventral posterior medial nucleus (VPM-only L6 CThNs) and one projecting to both VPM and the posterior medial nucleus (VPM/POm L6 CThNs). Using rabies-based tracing methods in mice, we asked whether these L6 CThN populations integrate similar synaptic inputs. We found that both types of L6 CThNs received local input from somatosensory cortex and thalamic input from VPM and POm. However, VPM/POm L6 CThNs received significantly more input from a number of additional cortical areas, higher order thalamic nuclei, and subcortical structures. We also found that the two types of L6 CThNs target different functional regions within the thalamic reticular nucleus (TRN). Together, our results indicate that these two types of L6 CThNs represent distinct information streams in the somatosensory cortex and suggest that VPM-only L6 CThNs regulate, via their more restricted circuits, sensory responses related to a cortical column while VPM/POm L6 CThNs, which are integrated into more widespread POm-related circuits, relay contextual information. This article is protected by copyright. All rights reserved.

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the component neural circuitry giving rise to these signals remains unknown. Specifically, the functional architecture of cortical columns which results in attention-associated electric fields has not been explored. Here we detail the laminar cortical circuitry underlying an attention-associated electric field often measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Lastly, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field capable of being measured extracranially and the relative contribution of each column depends upon the underlying functional architecture.

Throughout the animal kingdom, the structure of the central nervous system varies widely from distributed ganglia in worms to compact brains with varying degrees of folding in mammals. The differences in structure may indicate a fundamentally different circuit organization. However, the folded brain most likely is a direct result of mechanical forces when considering that a larger surface area of cortex packs into the restricted volume provided by the skull. Here, we introduce a computational model that instead of modeling mechanical forces relies on dimension reduction methods to place neurons according to specific connectivity requirements. For a simplified connectivity with strong local and weak long-range connections, our model predicts a transition from separate ganglia through smooth brain structures to heavily folded brains as the number of cortical columns increases. The model reproduces experimentally determined relationships between metrics of cortical folding and its pathological phenotypes in lissencephaly, polymicrogyria, microcephaly, autism, and schizophrenia. This suggests that mechanical forces that are known to lead to cortical folding may synergistically contribute to arrangements that reduce wiring. Our model provides a unified conceptual understanding of gyrification linking cellular connectivity and macroscopic structures in large-scale neural network models of the brain.

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 and the level of single neurons as stimulus-specific adaptation. 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 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 spindle-like, 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.Author summaryCortical sensory neurons have been shown to be capable of novelty detection, that is they respond more vigorously when a novel, unexpected stimulus is presented, and less so when the stimulus is part of a predictable sequence. However, the neural mechanism underlying this capability is not yet fully understood. Here, we developed a thalamocortical network model that accounts for novelty detection and reproduces physiologically observed neural response patterns in the somatosensory cortex. Specifically, our results demonstrate that the novelty signal arises from the complex recurrent interplay between thalamic neurons and cortical neurons in layers 4 and 6. This work therefore provides a concrete mechanism that can serve as a starting point for further investigating the neural circuit mechanisms underlying novelty detection.

In a typical electrophysiology experiment, synaptic stimulus is delivered in a cortical layer (1–6) and neuronal responses are recorded intracellularly in individual neurons. We recreated this standard electrophysiological paradigm in brain slices of mice expressing genetically encoded voltage indicators (GEVIs). This allowed us to monitor membrane voltages in the target pyramidal neurons (whole-cell), and population voltages in the surrounding neuropil (optical imaging), simultaneously. Pyramidal neurons have complex dendritic trees that span multiple cortical layers. GEVI imaging revealed areas of the brain slice that experienced the strongest depolarization on a specific synaptic stimulus (location and intensity), thus identifying cortical layers that contribute the most afferent activity to the recorded somatic voltage waveform. By combining whole-cell with GEVI imaging, we obtained a crude distribution of activated synaptic afferents in respect to the dendritic tree of a pyramidal cell. Synaptically evoked voltage waves propagating through the cortical neuropil (dendrites and axons) were not static but rather they changed on a millisecond scale. Voltage imaging can identify areas of brain slices in which the neuropil was in a sustained depolarization (plateau), long after the stimulus onset. Upon a barrage of synaptic inputs, a cortical pyramidal neuron experiences: (a) weak temporal summation of evoked voltage transients (EPSPs); and (b) afterhyperpolarization (intracellular recording), which are not represented in the GEVI population imaging signal (optical signal). To explain these findings [(a) and (b)], we used four voltage indicators (ArcLightD, chi-VSFP, Archon1, and di-4-ANEPPS) with different optical sensitivity, optical response speed, labeling strategy, and a target neuron type. All four imaging methods were used in an identical experimental paradigm: layer 1 (L1) synaptic stimulation, to allow direct comparisons. The population voltage signal showed paired-pulse facilitation, caused in part by additional recruitment of new neurons and dendrites. “Synaptic stimulation” delivered in L1 depolarizes almost an entire cortical column to some degree.

Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized μECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, μECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to ≤±200 μm, i.e., a single cortical column. Biophysical simulations reproduce experimental findings, and indicate that neurons in cortical layers V and VI contribute ∼85% of evoked high-gamma signal recorded at the surface. Cell number and synchronicity were the primary biophysical properties determining laminar contributions to evoked μECoG signals, while distance was only a minimal factor. Thus, evoked μECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.In BriefBaratham et al., investigated the localization and origins of sensory evoked ECoG responses. They experimentally found that ECoG responses were anisotropically localized ≤±200 μm, i.e., a single cortical column. Biophysically detailed simulations revealed that neurons in layers V &VI were the primary sources of evoked ECoG responses, in contrast to common thinking.HighlightsEvoked μECoG signals are localized on the surface to a cortical column.Neurons in cortical layers V and VI constitute the vast majority of the signal recorded at the surface.Different laminar contributions to ECoG signal are driven by cell density and synchronicity.

NDNF selectively labels neurogliaform inhibitory interneurons of layer 1. In this issue of Neuron, Cohen-Kashi Malina et al. (2021) show that NDNF activity is highly correlated with arousal level. During high arousal state, NDNF inhibits the dendrites while disinhibiting the soma of nearby pyramidal neurons.

This paper presents an original biomorphic neuron model, which differs from common IT models by a more complex synapse structure and from biological models by replacement of differential equations that describe the change in potential over time with explicit recurrence expressions by approximation of experimental data in the cortical neuron, and therefore, by transition from the spiking information coding to the coding using the average frequency of action potentials per a simulation step. This approach ensures sufficiently simple and efficient calculation of an ultra-large neural network in the stand-alone hardware with limited computing resources. The model consists of three separate functional parts: dendrites, soma, and axon, which allows implementing any connections between functional parts of different neurons, thus making the neural network architecture more flexible. To perform functional testing of the neuron model, the test neural network performing simple association and constructed as a consequent stack of functional blocks with primary connections organized using experimental neurophysiological data was simulated. It is shown that encoding of the information transmitted by the impulses, similar to biological ones, allows using memristors for calculating recurrence expressions that describe the change in the quantity of neurotransmitter receptors of the dendrite membrane. The elaborated biomorphic neuron model, defined conceptual principles of a neural network construction based on it, as well as replacement of synapses in the neural network with memristors will allow building an ultra-large biomorphic neural network that simulates the functioning of a separate brain cortical column in the stand-alone hardware—a biomorphic neuroprocessor.

Event-related fields of the magnetoencephalogram are triggered by sensory stimuli and appear as a series of waves extending hundreds of milliseconds after stimulus onset. They reflect the processing of the stimulus in cortex and have a highly subjectspecific morphology. However, we still have an incomplete picture of how event-related fields are generated, what the various waves signify, and why they are so subject-specific. Here, we focus on this problem through the lens of a computational model which describes auditory cortex in terms of interconnected cortical columns as part of hierarchically placed fields of the core, belt, and parabelt areas. We develop an analytical approach arriving at solutions to the system dynamics in terms of normal modes: damped harmonic oscillators emerging out of the coupled excitation and inhibition in the system. Each normal mode is a global feature which depends on the anatomical structure of the entire auditory cortex. Further, normal modes are fundamental dynamical building blocks, in that the activity of each cortical column represents a combination of all normal modes. This approach allows us to replicate a typical auditory event-related response as a weighted sum of the single-column activities. Our work offers an alternative to the view that the event-related field arises out of spatially discrete, local generators. Rather, there is only a single generator process distributed over the entire network of the auditory cortex. We present predictions for testing to what degree subject-specificity is due to cross-subject variations in dynamical parameters rather than in the cortical surface morphology.

In order to study dynamic biological processes in-vivo in mammalian organisms techniques are required which enable non-invasive imaging at large tissue depth with sub-cellular resolution. However, optical aberrations and scattering in biological tissue lead to signal loss and a degradation of both spatial resolution and penetration depth. Here, we combine two powerful optical techniques, multi-photon microscopy and adaptive optics, to push the depth limit further while retaining diffraction limited resolution. We apply these techniques to open questions in the field of neuron and glia biology. By utilizing three-photon excitation at the 1300 nm spectral excitation window we achieve highresolution imaging of GFP-labeled neurons up to a depth of 1.2 mm in the in-vivo mouse brain. Furthermore, we have combined our approach with indirect modal-based wavefront correction and synchronization of our microscope to the animal’s heart pulsation. This allowed us to improve the resolution up to ~6-fold and achieve synaptic resolution throughout an entire cortical column. With adaptive optics correction, small structures such a dendritic branches thus become clearly visible at over 1mm depth in the hippocampus. Furthermore, our adaptive three-photon microscope system enabled, for the first time, to investigate calcium dynamics of fibrous astrocytes which reside in the corpus callosum and whose dynamics have previously not been possible to image.

The recursive growing neural gas algorithm (RGNG) is a variant of the classic GNG that was specifically designed to model the response behavior of groups of biological neurons. It was used successfully to describe the behavior of entorhinal grid cells as well as entorhinal cells that show grid like activity in response to saccadic eye movements. More recently, the RGNG algorithm was integrated into a model of cortical column function as part of an autoassociative memory cell. To facilitate future research involving the simulation of hundreds to thousands of neuron groups we present an alternative algorithm to the RGNG as a drop-in replacement in the context of neuron group modeling. The differential growing neural gas (DGNG) is structurally less complex, more efficient to compute, and more robust in terms of the input space representation that is learned while retaining most of the RGNG’s important characteristics. We provide a formal definition of the DGNG algorithm and demonstrate its characteristics with a first set of experiments.

In order to build the perceptual scene, the brains of mammals have developed neural circuits, specialized in analyzing and mixing different sources of sensory information. This ability requires a dynamic multimodal interchange of information along all stations of the sensory pathways from the brainstem to the cerebral cortex. When one sensory system fails, the brain cortex reorganizes its neural networks to preserve intermodal processing, what is known as cross-modal plasticity. In deafened ferrets, “de novo” emerging somatosensory responses have been shown by single unit recording in the auditory cortex (AC), undoubtedly demonstrating a multimodal sensory conversion in the brain cortex after sensory deprivation (Allman et al., 2009). Since receptive fields involve inhibitory GABA interactions, as demonstrated by iontophoresis (Tremere et al., 2001), such sensory conversion may reflect imbalanced cortical multimodal neurotransmission. Our results in a model of bilateral long-term deafness indicate that hearing deprivation induces an altered functional intermodal interaction which involves increased activation of the visual cortex (VC). Also, in humans, VC overactivation after permanent and long-term deafness has been demonstrated using visual evoked potentials (Neville et al., 1983). It is known that cross-modal balance for sensory processing between primary cortices is the result of a combination of thalamic drivers’ activation, horizontal polymodal connections, and intrinsic microcircuit elaboration of neuronal responses in the cortical columns. Data will be presented in this talk pointing out that after chronic and permanent deafness in the rat, a cross-modal reorganization is triggered by changes in inhibitory circuits. Such rebound of inhibition has been shown by increases in gene expression and immunoreactivity for GAD 65 and GAD 67 as well as by increases in parvalbumin positive (PV) fast-spiking interneurons. Overactivation of the VC in our model, as demonstrated by changes in activity-dependent early immediate expression genes c-Fos and Arc/Arg 3.1 and VEPs recordings (Pernia et al., 2017), is generated by imbalanced horizontal interactions as indicated by restricted changes of immunocytochemical markers in layers 2/3. However, such imbalance does not equally affect both primary cortices. Because GABA interneurons specifically increase in primary AC, greater inhibition of cortical column microcircuits could be expected (fast-spiking control). Our results also indicate that two homeostatic mechanisms actively work for a dynamic compensation of the out-of-balance bimodal relationship after deafness: (1) Increases in the expression and protein synthesis of AMPA receptors in the AC (which indicates an effort to compensate changes in its thalamic drivers’ activation), and (2) the up-regulation of Arc/Arg3.1 shown by us in the VC which supports a reactive mechanism to compensate overactivation in the VC. In sum after prolonged deafness, cross-modal reorganization at long term induces the overactivation of neighboring sensory cortices (in particular VC) as a result of a dynamic compensation of the horizontal feedbacks. We have recently shown that anodal continuous current stimulation allows restricted over-activation of the AC (Colmenarez-Raga et al., 2019). A restricted stimulation with anodal currents (activation) in the AC may be able to rebalance cross-modal reaction, potentially improving cortical processing after cochlear implantation. New strategies of directional restricted neuromodulation of sensory cortices by electric fields by using the novel method of non-invasive deep brain stimulation via temporally interfering electric fields (Grossman et al., 2017) will be also discussed in this talk.

Sensory stimulation of forelimb produces cortical evoked responses in the somatosensory hindlimb cortex in a layer-dependent manner Spinal cord injury favours the input statistics of cortico-cortical connections between intact and deafferented cortices After spinal cord injury supragranular layers exhibit better integration of spontaneous of corticocortical information while infragranular layers exhibit better integration of evoked sensory stimulation Cortical reorganization is a layer-specific phenomenon ABSTRACT: Cortical areas have the capacity of large-scale reorganization following sensory deafferentation. However, it remains unclear whether this phenomenon is a unique process that homogenously affects an entire deprived cortical region or it is suitable to changes depending on neuronal networks across distinct cortical layers. Here, we studied how local circuitries within each layer of the deafferented cortex set the basis for neuroplastic changes after immediate thoracic spinal cord injury (SCI) in anaesthetised rats. In vivo electrophysiological recordings from deafferented hindlimb somatosensory cortex showed that SCI induces layer-specific changes mediating evoked and spontaneous activity. In supragranular layers 2/3, SCI increased gamma oscillations and the ability of these neurons to initiate up-states during spontaneous activity, suggesting altered corticocortical network and/or intrinsic properties that may serve to maintain the excitability of the cortical column after deafferentation. On the other hand, SCI enhanced infragranular layers' ability to integrate evoked-sensory inputs leading to increased and faster neuronal responses. Delayed evoked-responses onset were also observed in layers 5/6, suggesting alterations in thalamocortical connectivity. Altogether, our data indicate that SCI immediately modifies local circuitries within the deafferented cortex allowing supragranular layers to better integrate spontaneous corticocortical information, and thus modifying column excitability, and infragranular layers to better integrate evoked-sensory inputs to preserve subcortical outputs. These layer-specific neuronal changes may guide the long-term alterations in neuronal excitability and plasticity associated to the rearrangements of somatosensory networks and the appearance of central sensory pathologies usually associated with spinal cord injury. This article is protected by copyright. All rights reserved.

With its six layers and ~12000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents’. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex’s granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

Cortical columns interact through dynamic routing of neuronal activity. Monitoring these interactions in animals performing a behavioral task as close as possible to real time will advance our understanding of cortical computation. We developed the Multiplane Mesoscope which combines three established concepts in microscopy: spatio-temporal multiplexing, remote focusing, and random-access mesoscopy. With the Multiplane Mesoscope, we recorded excitatory and inhibitory neuronal subpopulations simultaneously across two cortical areas and multiple cortical layers in behaving mice. In the context of a visual detection of change task, we used this novel platform to study cortical areas interactions and quantified the cell-type specific distribution of neuronal correlations across a set of visual areas and layers. We found that distinct cortical subnetworks represent expected and unexpected visual events. Our findings demonstrate that expectation violations modify signal routing across cortical columns and establish the Allen Brain Observatory Multiplane Mesoscope as a unique platform to study signal routing across connected pairs of cortical areas.

In 1981, David Hubel and Torsten Wiesel received the Nobel Prize for their research on cortical columns—vertical bands of neurons with similar functional properties. This success led to the view that “cortical column” refers to the basic building block of the mammalian neocortex. Since the 1990s, however, critics questioned this building block picture of “cortical column” and debated whether this concept is useless and should be replaced with successor concepts. This paper inquires which experimental results after 1981 challenged the building block picture and whether these challenges warrant the elimination “cortical column” from neuroscientific discourse. I argue that the proliferation of experimental techniques led to a patchwork of locally adapted uses of the column concept. Each use refers to a different kind of cortical structure, rather than a neocortical building block. Once we acknowledge this diverse-kinds picture of “cortical column”, the elimination of column concept becomes unnecessary. Rather, I suggest that “cortical column” has reached conceptual retirement: although it cannot be used to identify a neocortical building block, column research is still useful as a guide and cautionary tale for ongoing research. At the same time, neuroscientists should search for alternative concepts when studying the functional architecture of the neocortex. keywords: Cortical column, conceptual development, history of neuroscience, patchwork, eliminativism, conceptual retirement.

Background This study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer’s disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, “radiality”, as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column. Methods We analyzed neuroimages from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers. Results The results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward. Conclusion Radiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Efficient inference and generalization guided the representational choices in the original computational model. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.

Elucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developedParallelRNA andDNA analysis afterDeep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

In humans, visual stimuli can be perceived across an enormous range of light levels. Evidence suggests that different neural mechanisms process different subdivisions of this range. For instance, in the retina, stimuli presented at very low (scotopic) light levels activate rod photoreceptors, whereas cone photoreceptors are activated relatively more at higher (photopic) light levels. Similarly, different retinal ganglion cells are activated by scotopic versus photopic stimuli. However, in the brain, it remains unknown whether scotopic versus photopic information is: 1) processed in distinct channels, or 2) neurally merged. Using high-resolution functional magnetic resonance imaging at 7 T, we confirmed the first hypothesis. We first localized thick versus thin-type columns within areas V2, V3, and V4, based on photopic selectivity to motion versus color, respectively. Next, we found that scotopic stimuli selectively activated thick- (compared to thin-) type columns in V2 and V3 (in measurements of both overlap and amplitude) and V4 (based on overlap). Finally, we found stronger resting-state functional connections between scotopically dominated area MT with thick- (compared to thin-) type columns in areas V2, V3, and V4. We conclude that scotopic stimuli are processed in partially segregated parallel streams, emphasizing magnocellular influence, from retina through middle stages of visual cortex.

Animals frequently make decisions based on sensory cues. In such a setting, the overlap in the information on the stimulus and on the choice is crucial for the formation of informed behavioral decisions. Yet, how the information on the stimulus and on the choice interact in the brain is poorly understood. Here, we study the representation of a binary decision variable in the primary visual cortex (V1) while macaque monkeys perform delayed match-to-sample task on naturalistic visual stimuli close to psychophysical threshold. Using population vectors, we demonstrate the overlap in decoding spaces on binary stimulus classes “match/non-match” and binary choices “same /different” of the animal. Leveraging this overlap, we use learning from the invariant information across the two classification problems to predict the choice of the animal as a time-dependent population signal. We show the importance of the across-neuron organization and the temporal structure of spike trains for the decision signal and suggest how noise correlations between neurons with similar decoding selectivity are helpful for the accumulation of the decision signal. Finally, we show that decision signal is primarily carried by bursting neurons in the superficial layers of the cortex.Author summaryV1 is necessary for normal visual processing and is known to process features of visual stimuli such as orientation, but whether V1 also encodes behavioral decisions is an unresolved issue, with conflicting evidence. Here, we demonstrate that V1 encodes a mixed variable that contains the information about the stimulus as well as about the choice. We learn the structure of population responses in trials pertaining to the variable “stimulus+choice”, and apply the resulting population vectors to trials that differ only about the choice of the animal, but not about the stimulus class. Moreover, we learn structure of population responses on time-averaged data and then apply it on time-dependent (spiking) data. During the late phase of the trial, this procedure allows to predict the upcoming choice of the animal with a time-dependent population signal. The spiking signal of small neural population is sparse, and we hypothesize that positive correlations between neurons in the same decoding pool help the transmission of the decision-related information downstream. We find that noise correlations in the same decoding pool are significantly stronger than across coding pools, which corroborates our hypothesis on the benefit of noise correlations for the read-out of a time-dependent population signal.

Neural networks are complex algorithms that loosely model the behaviour of the human brain. They play a significant role in computational neuroscience and artificial intelligence. The next generation of neural network models is based on the spike timing activity of neurons: spiking neural networks (SNNs). However, model parameters in SNNs are difficult to search and optimise. Previous studies using genetic algorithm (GA) optimisation of SNNs were focused mainly on simple, feedforward, or oscillatory networks, but not much work has been done on optimising cortex-like recurrent SNNs. In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations. We considered a cortical column based SNN comprising 1000 Izhikevich spiking neurons for computational efficiency and biologically realism. The model parameters explored were the neuronal biased input currents. First, we found for this particular SNN, the optimal parameter values for targeted population averaged firing activities, and the convergence of algorithm by ~100 generations. We then showed that the GA optimal population size was within ~16-20 while the crossover rate that returned the best fitness value was ~0.95. Overall, we have successfully demonstrated the feasibility of implementing GA to optimise model parameters in a recurrent cortical based SNN., Comment: 6 pages, 6 figures

Primary visual cortex (V1) is absolutely necessary for normal visual processing, but whether V1 encodes upcom-ing behavioral decisions based on visual information is an unresolved issue, with conflicting evidence. Further,no study so far has been able to predict choice from time-resolved spiking activity in V1. Here, we hypothesizedthat the choice cannot be decoded with classical decoding schemes due to the noise in incorrect trials, but itmight be possible to decode with generalized learning. We trained the decoder in the presence of the informationon both the stimulus class and the correct behavioral choice. The learned structure of population responseswas then utilized to decode trials that differ in the choice alone. We show that with such generalized learningscheme, the choice can be successfully predicted from spiking activity of neural ensembles in V1 in single trials,relying on the partial overlap in the representation between the stimuli and the choice. In addition, we showthat the representation of the choice is primarily carried by bursting neurons in the superficial layer of thecortex. We demonstrated how bursting of single neurons and noise correlations between neurons with similardecoding selectivity helps the accumulation of the choice signal.

Whisker-guided decision making in mice is thought to critically depend on information processing occurring in the primary somatosensory cortex. However, it is not clear if neuronal activity in this “early” sensory region contains information about the timing and speed of motor response. To address this question we designed a new task in which freely moving mice learned to associate a whisker stimulus to reward delivery. The task was tailored in such a way that a wide range of delays between whisker stimulation and reward collection were observed due to differences of motivation and perception. After training, mice were anesthetized and neuronal responses evoked by stimulating trained and untrained whiskers were recorded across several cortical columns of barrel cortex. We found a strong correlation between the delay of the mouse behavioral response and the timing of multiunit activity evoked by the trained whisker, outside its principal cortical column, in layers 4 and 5A but not in layer 2/3. Circuit mapping ex vivo revealed this effect was associated with a weakening of layer 4 to layer 2/3 projection. We conclude that the processes controlling the propagation of key sensory inputs to naive cortical columns and the timing of sensory-guided action are linked.

Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.

The basic organization principles of the primary visual cortex (V1) are commonly assumed to also hold in the association cortex such that neurons within a cortical column share functional connectivity patterns and represent the same region of the visual field. We mapped the visual receptive fields (RFs) of neurons recorded at the same electrode in the ventral intraparietal area (VIP) and the lateral prefrontal cortex (PFC) of rhesus monkeys. We report that the spatial characteristics of visual receptive fields between adjacent neurons differed considerably, with increasing heterogeneity from VIP to PFC. In addition to receptive field incongruences, we found differential functional connectivity between putative inhibitory interneurons and pyramidal cells in PFC and VIP. These findings suggest that local receptive field topography vanishes with hierarchical distance from visual cortical input and argue for increasingly modified functional microcircuits in non-canonical association cortices that contrast V1. SIGNIFICANCE STATEMENT Our visual field is thought to be represented faithfully by the early visual brain areas; all the information from a certain region of the visual field is conveyed to neurons situated close together within a functionally-defined cortical column. We examined this principle in the association areas, prefrontal cortex and ventral intraparietal area of rhesus monkeys and found that adjacent neurons represent markedly different areas of the visual field. This is the first demonstration of such non-canonical organization of these brain areas.

Congenital deafness leads to functional deficits in the auditory cortex for which early cochlear implantation can effectively compensate. Most of these deficits have been demonstrated functionally. Furthermore, the majority of previous studies on deafness have involved the primary auditory cortex; knowledge of higher-order areas is limited to effects of cross-modal reorganization. In this study, we compared the cortical cytoarchitecture of four cortical areas in adult hearing and congenitally deaf cats (CDCs): the primary auditory field A1, two secondary auditory fields, namely the dorsal zone and second auditory field (A2); and a reference visual association field (area 7) in the same section stained either using Nissl or SMI-32 antibodies. The general cytoarchitectonic pattern and the area-specific characteristics in the auditory cortex remained unchanged in animals with congenital deafness. Whereas area 7 did not differ between the groups investigated, all auditory fields were slightly thinner in CDCs, this being caused by reduced thickness of layers IV-VI. The study documents that, while the cytoarchitectonic patterns are in general independent of sensory experience, reduced layer thickness is observed in both primary and higher-order auditory fields in layer IV and infragranular layers. The study demonstrates differences in effects of congenital deafness between supragranular and other cortical layers, but similar dystrophic effects in all investigated auditory fields.

Background Immunofluorescent staining coupled with axial optical sectioning allows for assessment of native three-dimensional structure of brain tissue. Typical challenges of analyzing network structure include limitations driven by magnification/field of view, spatial resolution, tissue thickness, staining quality of dense cell types, data quantifiability and the quantity of simultaneous staining targets. New Method This manuscript demonstrates many methodological advancements. Software-aided alignment of the cortical slice and stereotaxic atlas maximizes ROI-identification accuracy. Tissue compression during antigen retrieval enhances epitope availability without damaging tissue. A thorough factorial experiment focusing on Smi-311 staining highlights the enhancements in image quality from our extended staining protocol. Mosaic scanning techniques and subsequent four-channel alignment ensures high data quality. Results Cortical column datasets [800μm x 3000μm x 70μm] utilizing sequential optical sectioning were successfully generated from three rats. Each rat provided three coronal sections in each of two regions, M1 and S1BF, from which data cubes were generated per hemisphere, totaling 36 high-magnification four-color datasets. Comparison with Existing Method(s) Typical confocal assessments of brain tissue do not utilize such thick tissue slices nor collect entire cortical columns from the cortical surface to the grey/white interface at a resolution that can map fine filamentous processes. The simultaneous collection of our four specific structural markers - neuronal, astrocytic, vascular and nuclear – is novel and the quantitative optimization of staining protocols through a factorial design rare. Conclusions Building upon this preliminary success in protocol development, future work will encompass volumetric modeling and quantitative analysis of regional network architecture.

How the somatosensory cortex (S1) encodes complex patterns of touch, such as those that occur during tactile exploration, is poorly understood. In the mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to two-whisker sequences that involve the columnar whisker (CW) and one specific surround whisker (SW), usually in a SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW-CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, thus providing a basis for higher-order feature extraction.

The state of synchronizing oscillations in the cortex are related to the dynamics characteristics of synapses, and the precise dynamics of synapses maybe varying because the number of receptors with different dynamics is uncertain. In order to analysis the effect of synapses dynamics on the synchronization oscillations in the cortex, a cortical column model is presented based on the anatomy data of turtle’s visual cortex. Compared with the regular feed-forward network, this model could spontaneously produce synchronizing oscillations. The results shows that, when the fast-changed receptor is dominant in excitatory synapses, the oscillation’s synchronism decline but frequency increase, and the slow-changed receptor in inhibitory synapses has the similar effect. Moreover, a proper ration of two kinds of receptor could maximum the efficiency of information transmission. These findings are beneficial for us to understand the intrinsic mechanisms of synchronizing oscillations in brain.

Objective Intracortical microstimulation (ICMS) is widely used in neuroscientific research. Earlier work from our lab showed the possibility to combine ICMS with neuronal recordings on the same shank of multi-electrode arrays and consequently inside the same cortical column in vivo. The standard stimulus pulse shape for ICMS is a symmetric, biphasic current pulse. Here, we investigated the role of the leading-phase polarity (cathodic- versus anodic-leading) of such single ICMS pulses on the activation of the cortical network. Approach Local field potentials (LFPs) and multi-unit responses were recorded in the primary auditory cortex (A1) of adult guinea pigs (n = 15) under ketamine/xylazine anesthesia using linear multi-electrode arrays. Physiological responses of A1 were recorded during acoustic stimulation and ICMS. For the ICMS, the leading-phase polarity, the stimulated electrode and the stimulation current where varied systematically on any one of the 16 electrodes while recording at the same time with the 15 remaining electrodes. Main results Cathodic-leading ICMS consistently led to higher response amplitudes. In superficial cortical layers and for a given current amplitude, cathodic-leading and anodic-leading ICMS showed comparable activation patterns, while in deep layers only cathodic-leading ICMS reliably generated local neuronal activity. ICMS had a significantly smaller dynamic range than acoustic stimulation regardless of leading-phase polarity. Significance The present study provides in vivo evidence for a differential neuronal activation mechanism of the different leading-phase polarities, with cathodic-leading stimulation being more effective, and suggests that the waveform of the stimulus should be considered systematically for cortical neuroprosthesis development.

Incoming stimuli are encoded collectively by populations of cortical neurons, which transmit information by using a neural code thought to be predominantly redundant. Redundant coding is widely believed to reflect a design choice whereby neurons with overlapping receptive fields sample environmental stimuli to convey similar information. Here, we performed multi-electrode laminar recordings in awake monkey V1 to report significant synergistic interactions between nearby neurons within a cortical column. These interactions are clustered non-randomly across cortical layers to form synergy and redundancy hubs. Homogeneous sub-populations comprising synergy hubs decode stimulus information significantly better compared to redundancy hubs or heterogeneous sub-populations. Mechanistically, synergistic interactions emerge from the stimulus dependence of correlated activity between neurons. Our findings suggest a refinement of the prevailing ideas regarding coding schemes in sensory cortex: columnar populations can efficiently encode information due to synergistic interactions even when receptive fields overlap and shared noise between cells is high.

Broadband High-frequency Activity (BHA; 70-150 Hz), also known as "high gamma," a key analytic signal in human intracranial recordings is often assumed to reflect local neural firing (multiunit activity; MUA). Accordingly, BHA has been used to study neuronal population responses in auditory (1,2), visual (3,4), language (5), mnemonic processes (6-9) and cognitive control (10,11). BHA is arguably the electrophysiological measure best correlated with the Blood Oxygenation Level Dependent (BOLD) signal in fMRI (12-13). However, beyond the fact that BHA correlates with neuronal spiking (12, 14-16), the neuronal populations and physiological processes generating BHA are not precisely defined. Although critical for interpreting intracranial signals in human and non-human primates, the precise physiology of BHA remains unknown. Here, we show that BHA dissociates from MUA in primary visual and auditory cortex. Using laminar multielectrode data in monkeys, we found a bimodal distribution of stimulus-evoked BHA across depth of a cortical column: an early-deep, followed by a later-superficial layer response. Only, the early-deep layer BHA had a clear local MUA correlate, while the more prominent superficial layer BHA had a weak or undetectable MUA correlate. In many cases, particularly in V1 (70%), supragranular sites showed strong BHA in lieu of any detectable increase in MUA. Due to volume conduction, BHA from both the early-deep and the later-supragranular generators contribute to the field potential at the pial surface, though the contribution may be weighted towards the late-supragranular BHA. Our results demonstrate that the strongest generators of BHA are in the superficial cortical layers and show that the origins of BHA include a mixture of the neuronal action potential firing and dendritic processes separable from this firing. It is likely that the typically-recorded BHA signal emphasizes the latter processes to a greater extent than previously recognized.

INTRODUCTION Neural circuits across the brain are composed of structures spanning seven orders of magnitude in size that are assembled from thousands of distinct protein types. Electron microscopy has imaged densely labeled brain tissue at nanometer-level resolution over near-millimeter-level dimensions but lacks the contrast to distinguish specific proteins and the speed to readily image multiple specimens. Conversely, confocal fluorescence microscopy offers molecular contrast but has insufficient resolution for dense neural tracing or the precise localization of specific molecular players within submicrometer-sized structures. Last, superresolution fluorescence microscopy bleaches fluorophores too quickly for large-volume imaging and also lacks the speed for effective brain-wide or cortex-wide imaging of multiple specimens. RATIONALE We combined two imaging technologies to address these issues. Expansion microscopy (ExM) creates an expanded, optically clear phantom of a fluorescent specimen that retains its original relative distribution of fluorescent tags. Lattice light-sheet microscopy (LLSM) then images this phantom in three dimensions with minimal photobleaching at speeds sufficient to image the entire Drosophila brain or across the width of the mouse cortex in ∼2 to 3 days, with multiple markers at an effective resolution of ∼60 by 60 by 90 nm for 4× expansion. RESULTS We applied expansion/LLSM (ExLLSM) to study a variety of subcellular structures in the brain. In the mouse cortex, we quantified the volume of organelles, measured morphological parameters of ~1500 dendritic spines, determined the variation of distances between pre- and postsynaptic proteins, observed large differences in postsynaptic expression at adjacent pyramidal neurons, and studied both the azimuthal asymmetry and layer-specific longitudinal variation of axonal myelination. In Drosophila , we traced the axonal branches of olfactory projection neurons across one hemisphere and studied the stereotypy of their boutons at the calyx and lateral horn across five animals. We also imaged all dopaminergic neurons (DANs) across the brain of another specimen, visualized DAN morphologies in all major brain regions, and traced a cluster of eight DANs to their termini to determine their respective cell types. In the same specimen, we also determined the number of presynaptic active zones (AZs) across the brain and the local density of all AZs and DAN-associated AZs in each brain region. CONCLUSION With its high speed, nanometric resolution, and ability to leverage genetically targeted, cell type–specific, and protein-specific fluorescence labeling, ExLLSM fills a valuable niche between the high throughput of conventional optical pipelines of neural anatomy and the ultrahigh resolution of corresponding EM pipelines. Assuming the development of fully validated, brain-wide isotropic expansion at 10× or beyond and sufficiently dense labeling, ExLLSM may enable brainwide comparisons of even densely innervated neural circuits across multiple specimens with protein-specific contrast at 25-nm resolution or better.

The segregation of figures from the background is an important step in generating a visual percept. In primary visual cortex (V1), figures evoke stronger neural firing than backgrounds but it is unknown how this figure-ground modulation (FGM) arises and whether it is necessary for perception. Here we show, using optogenetic silencing in mice, that FGM in V1 is necessary for figure-ground segregation. We find that the source of this enhanced activity is feedback from cortical higher visual areas. Neurons in higher areas also exhibit FGM and optogenetic silencing of higher areas strongly reduced FGM in V1. In V1, figures enhanced the activity of vasoactive intestinal peptide-expressing interneurons and suppressed somatostatin-positive interneurons, causing an increased activation of the cortical column. Our results provide new insight in how lower and higher areas of the visual cortex interact with each other to shape visual perception.

Though the cortical column is well-studied, the function of cells tuned to adjacent sensors (heterogeneous cells) is unknown, especially regarding sensory spatial acuity in behaving animals. We increased tuning heterogeneity in mice vibrissal primary somatosensory cortex (vS1) by environmental enrichment. Single-neuron activity in vS1 and vibrissal premotor cortex (vM2) was recorded using two-photon calcium imaging while mice performed a two-adjacent-whisker discrimination task. Enriched animals performed better at spatial discrimination, despite increased whisker tuning heterogeneity. Heterogeneous vS1 cells and vM2 cells are strong neural correlates of behavior. Single-column inhibition decreased the animal’s spatial acuity and vM2 cells’ ability to encode the animal’s decision. Our results suggest that the functional overlap between columns in vS1 does not translate to obscured spatial acuity in sensory percept. Rather, heterogeneous vS1 cells play an important role in the animal’s perceptual decision and the neural processes in the frontal cortex underlying such decisions.

The classical view of sensory information mainly flowing into barrel cortex at layer IV, moving up for complex feature processing and lateral interactions in layers II and III, then down to layers V and VI for output and corticothalamic feedback is becoming increasingly undermined by new evidence. We review the neurophysiology of sensing and processing whisker deflections, emphasizing the general processing and organisational principles present along the entire sensory pathway—from the site of physical deflection at the whiskers to the encoding of deflections in the barrel cortex. Many of these principles support the classical view. However, we also highlight the growing number of exceptions to these general principles, which complexify the system and which investigators should be mindful of when interpreting their results. We identify gaps in the literature for experimentalists and theorists to investigate, not just to better understand whisker sensation but also to better understand sensory and cortical processing.

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot-savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm(3). With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.

Despite the hippocampus being extensively studied, controversy remains as to its role in cognitive processing. The current paper presents a theoretical argument that the hippocampus has only one purpose: the binding of parallel cortical circuits. The paper begins with a discussion of cortical columns as the common binary digit (bit) for all neocortical processing. This is followed by details on the Dimensional Systems Model and its explanation of cortical circuitry. As opposed to any independent function in cognitive processing, the hippocampal cells are viewed as serving a slave function to cortical circuits since they are activated as part of a cortical-hippocampal-thalamic-cortical circuit. As part of that circuit, the hippocampus serves the role of reactivating the cortical circuits involved in complex memories (i.e., involving multiple cortical circuits) to facilitate the consolidation of the involved cortical columns’ interconnections. Next there is a brief discussion of prior theoretical views explaining hippocampal involvement in memory and spatial representations. To facilitate an appreciation of the unique aspects of the new model, it is contrasted against the extensively developed Component Process Model. Conclusions focus on how a complete model of cortical cognitive processing is necessary to adequately explain the purpose of the hippocampus, viewed as a complex structure with an elegantly simple function.

Neocortical circuits can be altered by sensory and motor experience, with experimental evidence supporting both anatomical and electrophysiological changes in synaptic properties. Previous studies have focused on changes in specific neurons or pathways—for example, the thalamocortical circuitry, layer 4–3 (L4–L3) synapses, or in the apical dendrites of L5 neurons— but a broad-scale analysis of experience-induced changes across the cortical column has been lacking. Without this comprehensive approach, a full understanding of how cortical circuits adapt during learning or altered sensory input will be impossible. Here we adapt an electron microscopy technique that selectively labels synapses, in combination with a machine-learning algorithm for semiautomated synapse detection, to perform an unbiased analysis of developmental and experience-dependent changes in synaptic properties across an entire cortical column in mice. Synapse density and length were compared across development and during whisker-evoked plasticity. Between postnatal days 14 and 18, synapse density significantly increases most in superficial layers, and synapse length increases in L3 and L5B. Removal of all but a single whisker row for 24 h led to an apparent increase in synapse density in L2 and a decrease in L6, and a significant increase in length in L3. Targeted electrophysiological analysis of changes in miniature EPSC and IPSC properties in L2 pyramidal neurons showed that mEPSC frequency nearly doubled in the whisker-spared column, a difference that was highly significant. Together, this analysis shows that data-intensive analysis of column-wide changes in synapse properties can generate specific and testable hypotheses about experience-dependent changes in cortical organization.SIGNIFICANCE STATEMENTDevelopment and sensory experience can change synapse properties in the neocortex. Here we use a semiautomated analysis of electron microscopy images for an unbiased, column-wide analysis of synapse changes. This analysis reveals new loci for synaptic change that can be verified by targeted electrophysiological investigation. This method can be used as a platform for generating new hypotheses about synaptic changes across different brain areas and experimental conditions.

Summary: The cerebral cortex is organized in vertical columns that contain neurons with similar functions. The cellular micro-architecture of such columns is an essential determinant of brain dynamics and cortical information processing. However, a detailed understanding of columns is incomplete, even in the best studied cortical regions, and mostly restricted to the upper cortical layers. Here, we developed a two-photon Ca2+-imaging-based method for the serial functional mapping of all pyramidal layers of the mouse primary auditory cortex at single-neuron resolution in individual animals. We demonstrate that the best frequency-responsive neurons are organized in all-layers-crossing narrow columns, with fuzzy boundaries and a bandwidth of about one octave. This micro-architecture is, in many ways, different from what has been reported before, indicating the region and stimulus specificity of functional cortical columns in vivo. : Tischbirek et al. report a two-photon Ca2+-imaging-based approach to map sensory-evoked neuronal activity from L2/3 to L6 of mouse cortex. In the primary auditory cortex, the authors identify functional microcolumns at cellular resolution that bring together large-scale tonotopy and locally heterogeneous frequency responses throughout all cortical layers. Keywords: two-photon microscopy, calcium imaging, cortical column, cortical layers, cellular micro-architecture, mouse auditory cortex, tonotopy

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

Summary Understanding how cortical inhibition shapes circuit function requires identifying the connectivity rules relating the response properties of inhibitory interneurons and their postsynaptic targets. Here we explore the orientation tuning of layer 2/3 inhibitory inputs in the ferret visual cortex using a combination of in vivo axon imaging, functional input mapping, and physiology. Inhibitory boutons exhibit robust orientation-tuned responses with preferences that can differ significantly from the cortical column in which they reside. Inhibitory input fields measured with patterned optogenetic stimulation and intracellular recordings revealed that these inputs originate from a wide range of orientation domains, inconsistent with a model of co-tuned inhibition and excitation. Intracellular synaptic conductance measurements confirm that individual neurons can depart from a co-tuned regime. Our results argue against a simple rule for the arrangement of inhibitory inputs supplied by layer 2/3 circuits and suggest that heterogeneity in presynaptic inhibitory networks contributes to neural response properties.

Optical and electron microscopy have made tremendous inroads in understanding the complexity of the brain, but the former offers insufficient resolution to reveal subcellular details and the latter lacks the throughput and molecular contrast to visualize specific molecular constituents over mm-scale or larger dimensions. We combined expansion microscopy and lattice light sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entireDrosophilabrain, including synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly neuropil domain. The technology should enable statistically rich, large scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.One Sentence SummaryCombined expansion and lattice light sheet microscopy enables high speed, nanoscale molecular imaging of neural circuits over large volumes.

Highlights • A simplified classification scheme for PCs by identifying apical dendritic morphology. • This classification scheme yielded 19 PC types cross all 6 layers of juvenile rat SSC. • Three aims are: • to introduce a simplified classification scheme for the PCs in different layers of SSC. • to present a full spectrum of various PCs based on a large amount of neurons reconstructed from SSC. • to reveal the structural organizing principles of PCs in different layers in a quantitative way., The majority of neurons in the neocortex are excitatory pyramidal cells (PCs). Many systematic classification schemes have been proposed based the neuronal morphology, the chemical composition, and the synaptic connectivity, etc. Recently, a cortical column of primary somatosensory cortex (SSC) has been reconstruction and functionally simulated (Markram et al., 2015). Putting forward from this study, here we proposed a simplified classification scheme for PCs in all layers of the SSC by mainly identifying apical dendritic morphology based on a large data set of 3D neuron reconstructions. We used this scheme to classify three types in layer 2, two in layer 3, three in layer 4, four in layer 5, and six types in layer 6. These PC types were visually distinguished and confirmed by quantitative differences in their morphometric properties. The classes yielded using this scheme largely corresponded with PC classes that were defined previously based on other neuronal and synaptic properties such as long-range projects and synaptic innervations, further validating its applicability. Therefore, the morphology information of apical dendrites is sufficient for a simple scheme to classify a spectrum of anatomical types of PCs in the SSC.

The primate prefrontal cortex is critical for executive functions including working memory, task switching, and response selection. The functional organization of this area has been a matter of debate over a period of decades. Early models proposed segregation of spatial and object information represented in working memory in the dorsal and ventral prefrontal cortex, respectively. Other models emphasized the integrative ability of the entire prefrontal cortex depending on task demands, not necessarily tied to working memory. An anterior-posterior hierarchy of specialization has also been speculated, in which progressively more abstract information is represented more anteriorly. Here we revisit this debate, updating these arguments in light of recent evidence in non-human primate neurophysiology studies. We show that spatial selectivity is predominantly represented in the posterior aspect of the dorsal prefrontal cortex, regardless of training history and task performed. Objects of different features excite both dorsal and ventral prefrontal neurons, however neurons highly specialized for feature information are located predominantly in the posterior aspect of the ventral prefrontal cortex. In accordance with neuronal selectivity, spatial working memory is selectively impaired by inactivation or lesion of the dorsal prefrontal cortex and object working memory by ventral inactivation or lesion. Neuronal responses are plastic depending on task training, but training too has dissociable effects on ventral and dorsal prefrontal cortex, with the latter being more plastic. Despite the absence of an overall topography, evidence exists for the orderly localization of stimulus information at a sub-millimeter scale, within the dimensions of a cortical column. Unresolved questions remain, regarding the existence or not of a functional map at the areal and columnar scale, and the link between behavior and neuronal activity for different prefrontal subdivisions.