A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

Dynamic changes in cortical neurotrophic factor-positive interneurons during sleep

Mammalian sleep is characterized by profound changes in the activity of cortical neurons which sum to electroencephalographic (EEG) rhythms linked to sleep regulation and function. Inhibitory interneurons (INs) influence these rhythms via direct inhibition of excitatory neurons and disinhibition that arises from IN-IN interactions. Therefore, understanding the activity of different classes of INs during sleep is required for a more complete understanding of how sleep influences cortical circuits. To this end, we investigated activity changes in the recently identified cortical layer 1 neuron-derived neurotrophic factor-positive (NDNF+) INs. This was accomplished using a combination of genetically encoded calcium imaging (GECI), miniature head mounted cameras, polysomnography, and GECI imaging in freely behaving mice. We find that NDNF+ INs are mostly active in rapid-eye-movement (REM) sleep and decrease their activity following total sleep deprivation. These results suggest that NDNF+ INs may play important roles in cortical EEG activity during REM sleep and compensatory changes following sleep loss.

A neural implementation model of feedback-based motor learning

Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex - known to mediate both movement correction and motor adaptation - during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.

Deciphering consciousness: The role of corticothalamocortical interactions in general anesthesia

General anesthesia is administered to millions of individuals each year, however, the precise mechanism by which it induces unconsciousness remains unclear. While some theories suggest that anesthesia shares similarities with natural sleep, targeting sleep-promoting areas and inhibiting arousal nuclei, recent research indicates a more complex process. Emerging evidence highlights the critical role of corticothalamocortical circuits, which are involved in higher cognitive functions, in controlling arousal states and modulating transitions between different conscious states during anesthesia. The administration of general anesthetics disrupts connectivity within these circuits, resulting in a reversible state of unconsciousness. This review elucidates how anesthetics impair corticothalamocortical interactions, thereby affecting the flow of information across various cortical layers and disrupting higher-order cognitive functions while preserving basic sensory processing. Additionally, the role of the prefrontal cortex in regulating arousal through both top-down and bottom-up pathways was examined. These findings highlight the intricate interplay between the cortical and subcortical networks in maintaining and restoring consciousness under anesthesia, offering potential therapeutic targets for enhancing anesthesia management.

Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3

Apical and basal dendrites of pyramidal neurons receive anatomically and functionally distinct inputs, implying compartment-level functional diversity during behavior. To test this, we imaged in vivo calcium signals from soma, apical dendrites, and basal dendrites in mouse hippocampal CA3 pyramidal neurons during head-fixed navigation. To capture compartment-specific population dynamics, we developed computational tools to automatically segment dendrites and extract accurate fluorescence traces from densely labeled neurons. We validated the method on sparsely labeled preparations and synthetic data, predicting an optimal labeling density for high experimental throughput and analytical accuracy. Our method detected rapid, local dendritic activity. Dendrites showed robust spatial tuning, similar to soma but with higher activity rates. Across days, apical dendrites remained more stable and outperformed in decoding of the animal's position. Thus, population-level apical and basal dendritic differences may reflect distinct compartment-specific input-output functions and computations in CA3. These tools will facilitate future studies mapping sub-cellular activity and their relation to behavior.

Prey capture learning drives critical period-specific plasticity in mouse binocular visual cortex

Critical periods are developmental windows of high experience-dependent plasticity essential for the correct refinement of neuronal circuitry and function. While the consequences for the visual system of sensory deprivation during the critical period have been well-characterized, far less is known about the effects of enhanced sensory experience. Here, we use prey capture learning to assess structural and functional plasticity mediating visual learning in the primary visual cortex of critical period mice. We show that prey capture learning improves temporal frequency discrimination and drives a profound remodeling of visual circuitry through an increase in excitatory connectivity and spine turnover. This global and persistent rewiring is not observed in adult hunters and is mediated by TNFα-dependent mechanisms. Our findings demonstrate that enhanced visual experience in a naturalistic paradigm during the critical period can drive structural plasticity to improve visual function, and promotes a long-lasting increase in spine dynamics that could enhance subsequent plasticity.

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally generated top-down information. In the neocortex, top-down projections primarily target layer 1, which contains NDNF (neuron-derived neurotrophic factor)-expressing interneurons and the dendrites of pyramidal cells. Here, we investigate the hypothesis that NDNF interneurons shape cortical computations in an unconventional, layer-specific way, by exerting presynaptic inhibition on synapses in layer 1 while leaving synapses in deeper layers unaffected. We first confirm experimentally that in the auditory cortex, synapses from somatostatin-expressing (SOM) onto NDNF neurons are indeed modulated by ambient Gamma-aminobutyric acid (GABA). Shifting to a computational model, we then show that this mechanism introduces a distinct mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons. This motif can control inhibition in a layer-specific way and introduces competition between NDNF and SOM interneurons for dendritic inhibition onto pyramidal cells on different timescales. NDNF interneurons can thereby control cortical information flow by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition.

Photic drive response in people with epilepsy: Exploring the interaction with background alpha rhythm

Photic drive responses (PDRs) are used to explore cortical hyperexcitability. We quantified PDRs and interactions with the alpha rhythm in people with epilepsy (PwE). Fifteen PwE (mean age ± SD 47.3 ± 4.6 years; 8 males), and 15 control subjects (mean age 52.7 ± 4.6 years; 9 males) underwent EEG with modified intermittent photic stimulation (IPS). The modification allowed so-called alpha-band gain to be measured. None of the PwE had demonstrated photosensitivity. The modified IPS method alternated eyes-open and eyes-closed conditions with and without IPS. The alpha-band gain appeared as N-fold changes in PDR when IPS (or its harmonics) and the alpha-bands overlapped. An epileptic attack within 1 month of testing significantly increased alpha-band gain by 1.36×. Generalised epilepsy (but not focal epilepsy) significantly decreased alpha-band gain y 0.79×. Each decade of age beyond the mean age significantly increased alpha-band gain by 1.09×. Similar significant interactions were seen between alpha and the second harmonic of IPS driving frequencies that matched alpha frequencies, i.e. for recent attack and, generalized epilepsy. The interactions thus appeared to be occurring between cortical IPS outputs and the alpha generator. These changes were most evident at electrodes O1 and O2. Investigating alpha-band gain using modified IPS offers a way to quantify cortical hyperexcitability in epilepsy and other diseases. It also provides new information about alpha and so too predictive coding, which appears to be at least partly governed by alpha.

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning

Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations. We find that dendritic ANNs are more robust to overfitting and match or outperform traditional ANNs on several image classification tasks while using significantly fewer trainable parameters. These advantages are likely the result of a different learning strategy, whereby most of the nodes in dendritic ANNs respond to multiple classes, unlike classical ANNs that strive for class-specificity. Our findings suggest that the incorporation of dendritic properties can make learning in ANNs more precise, resilient, and parameter-efficient and shed new light on how biological features can impact the learning strategies of ANNs.

The effects of estrogen depletion in female rats: differential influences on somato-motor and sensory cortices

Aging women experience a significant decline of ovarian hormones, particularly estrogen, following menopause, and become susceptible to cognitive and psychomotor deficits. Although the effects of estrogen depletion had been documented in the prefrontal and somatosensory cortices, its impact on somatomotor cortex, a region crucial for motor and cognitive functions, remains unclear. To explore this, we ovariectomized young adult female rats and fed subsequently with phytoestrogen-free diet and studied the effects of estrogen depletion on the somato-sensory and motor cortices. Low serum estrogen was confirmed prior to biochemical and morphological analyses. Results revealed that estrogen depletion differentially affected the two cortical areas: all three estrogen receptors were downregulated in the somatosensory cortex, whereas in the somatomotor cortex, G-protein-coupled estrogen receptor 30 was upregulated, estrogen receptor α decreased, and estrogen receptor β remained unaffected. Intracellular dye injections revealed decreased dendritic spines on layer III and V pyramidal neurons of the somato-sensory cortex but increased in those of the motor cortex. These were accompanied by decrease and increase of excitatory postsynaptic density protein 95 respectively. Since dendritic spines receive excitatory inputs, these findings suggest that estrogen depletion changes the excitatory connectivity of the somato-sensory and motor cortices in opposite directions. Notably, estradiol replenishment reversed the dendritic spine increase in the somatomotor cortex, confirming the estrogen dependency of this effect. The differential influence of estrogen depletion on these two cortices could have contributed to the cognitive and psychomotor abnormalities in postmenopausal females.

Vectorized instructive signals in cortical dendrites during a brain-computer interface task

Vectorization of teaching signals is a key element of virtually all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments1-5. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We leveraged a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results provide the first biological evidence of a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.

Cellular psychology: relating cognition to context-sensitive pyramidal cells

'Cellular psychology' is a new field of inquiry that studies dendritic mechanisms for adapting mental events to the current context, thus increasing their coherence, flexibility, effectiveness, and comprehensibility. Apical dendrites of neocortical pyramidal cells have a crucial role in cognition - those dendrites receive input from diverse sources, including feedback, and can amplify the cell's feedforward transmission if relevant in that context. Specialized subsets of inhibitory interneurons regulate this cooperative context-sensitive processing by increasing or decreasing amplification. Apical input has different effects on cellular output depending on whether we are awake, deeply asleep, or dreaming. Furthermore, wakeful thought and imagery may depend on apical input. High-resolution neuroimaging in humans supports and complements evidence on these cellular mechanisms from other mammals.

A dendrite is a dendrite is a dendrite? Dendritic signal integration beyond the "antenna" model

Neurons in central nervous systems receive multiple synaptic inputs and transform them into a largely standardized output to their target cells-the action potential. A simplified model posits that synaptic signals are integrated by linear summation and passive propagation towards the axon initial segment, where the threshold for spike generation is either crossed or not. However, multiple lines of research during past decades have shown that signal integration in individual neurons is much more complex, with important functional consequences at the cellular, network, and behavioral-cognitive level. The interplay between concomitant excitatory and inhibitory postsynaptic potentials depends strongly on the relative timing and localization of the respective synapses. In addition, dendrites contain multiple voltage-dependent conductances, which allow scaling of postsynaptic potentials, non-linear input processing, and compartmentalization of signals. Together, these features enable a rich variety of single-neuron computations, including non-linear operations and synaptic plasticity. Hence, we have to revise over-simplified messages from textbooks and use simplified computational models like integrate-and-fire neurons with some caution. This concept article summarizes the most important mechanisms of dendritic integration and highlights some recent developments in the field.

Learning enhances behaviorally relevant representations in apical dendrites

Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.

Non-apical plateau potentials and persistent firing induced by metabotropic cholinergic modulation in layer 2/3 pyramidal cells in the rat prefrontal cortex

Here we describe a type of depolarising plateau potentials (PPs; sustained depolarisations outlasting the stimuli) in layer 2/3 pyramidal cells (L2/3PC) in rat prefrontal cortex (PFC) slices, using whole-cell somatic recordings. To our knowledge, this PP type has not been described before. In particular, unlike previously described plateau potentials that originate in the large apical dendrite of L5 cortical pyramidal neurons, these L2/3PC PPs are generated independently of the apical dendrite. Thus, surprisingly, these PPs persisted when the apical dendrite was cut off (~50 μm from the soma), and were sustained by local calcium application only to the somatic and basal dendritic compartments. The prefrontal L2/3PCs have been postulated to have a key role in consciousness, according to the Global Neuronal Workspace Theory: their long-range cortico-cortical connections provide the architecture required for the "global work-space", "ignition", amplification, and sustained, reverberant activity, considered essential for conscious access. The PPs in L2/3PCs caused sustained spiking that profoundly altered the input-output relationships of these neurons, resembling the sustained activity suggested to underlie working memory and the mechanism underlying "behavioural time scale synaptic plasticity" in hippocampal pyramidal cells. The non-apical L2/3 PPs depended on metabotropic cholinergic (mAChR) or glutamatergic (mGluR) modulation, which is probably essential also for conscious brain states and experience, in both wakefulness and dreaming. Pharmacological tests indicated that the non-apical L2/3 PPs depend on transient receptor potential (TRP) cation channels, both TRPC4 and TRPC5, and require external calcium (Ca2+) and internal Ca2+ stores, but not voltage-gated Ca2+ channels, unlike Ca2+-dependent PPs in other cortical pyramidal neurons. These L2/3 non-apical plateau potentials may be involved in prefrontal functions, such as access consciousness, working memory, and executive functions such as planning, decision-making, and outcome prediction.

In vivo dual-plane 3-photon microscopy: spanning the depth of the mouse neocortex

Cortical computations arise from patterns of neuronal activity that span across all cortical layers and cell types. Three-photon excitation has extended the depth limit of in vivo imaging within the mouse brain to encompass all cortical layers. However, simultaneous three-photon imaging throughout cortical layers has yet to be demonstrated. Here, we combine non-unity magnification remote focusing with adaptive optics to achieve single-cell resolution imaging from two temporally multiplexed planes separated by up to 600 µm. This approach enables the simultaneous acquisition of neuronal activity from genetically defined cell types in any pair of cortical layers across the mouse neocortical column.

Bayesian Mechanics of Synaptic Learning Under the Free-Energy Principle

The brain is a biological system comprising nerve cells and orchestrates its embodied agent's perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised cognitive dynamics of the brain's higher-order functions. In this study, we continue to refine the FEP through a physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight changes and postsynaptic activity, conditioned on the presynaptic input, by deploying generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic efficacy in the brain with a simple model; in particular, we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal.

Mild focal cooling selectively impacts computations in dendritic trees

Focal cooling is a powerful technique to temporally scale neural dynamics. However, the underlying cellular mechanisms causing this scaling remain unresolved. Here, using targeted focal cooling (with a spatial resolution of 100 micrometers), dual somato-dendritic patch clamp recordings, two-photon calcium imaging, transmitter uncaging, and modeling we reveal that a 5°C drop can enhance synaptic transmission, plasticity, and input-output transformations in the distal apical tuft, but not in the basal dendrites of intrinsically bursting L5 pyramidal neurons. This enhancement depends on N-methyl-D-aspartate (NMDA) and Kv4.2, suggesting electrical structure modulation. Paradoxically, and despite the increase in tuft excitability, we observe a reduced rate of recovery from inactivation for apical Na+ channels, thereby regulating back-propagating action potential invasion, coincidence detection, and overall burst probability, resulting in an "apparent" slowing of somatic spike output. Our findings reveal a differential temperature sensitivity along the basal-tuft axis of L5 neurons analog modulates cortical output.

Transcriptomic Correlates of State Modulation in GABAergic Interneurons: A Cross-Species Analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical, and functional properties. In mouse visual cortex, they also differ in their modulation with an animal's behavioral state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalizes across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissimilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell-type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.

Anatomical and electrophysiological analysis of the fasciola cinerea of the mouse hippocampus

The hippocampus is considered essential for several forms of declarative memory, including spatial and social memory. Despite the extensive research of the classic subfields of the hippocampus, the fasciola cinerea (FC)-a medially located structure within the hippocampal formation-has remained largely unexplored. In the present study, we performed a morpho-functional characterization of principal neurons in the mouse FC. Using in vivo juxtacellular recording of single neurons, we found that FC neurons are distinct from neighboring CA1 pyramidal cells, both morphologically and electrophysiologically. Specifically, FC neurons displayed non-pyramidal morphology and granule cell-like apical dendrites. Compared to neighboring CA1 pyramidal neurons, FC neurons exhibited more regular in vivo firing patterns and a lower tendency to fire spikes at short interspike intervals. Furthermore, tracing experiments revealed that the FC receives inputs from the lateral but not the medial entorhinal cortex and CA3, and it provides a major intra-hippocampal projection to the septal CA2 and sparser inputs to the distal CA1. Overall, our results indicate that the FC is a morphologically and electrophysiologically distinct subfield of the hippocampal formation; given the established role of CA2 in social memory and seizure initiation, the unique efferent intra-hippocampal connectivity of the FC points to possible roles in social cognition and temporal lobe epilepsy.

Areal specializations in the morpho-electric and transcriptomic properties of primate layer 5 extratelencephalic projection neurons

Large-scale analysis of single-cell gene expression has revealed transcriptomically defined cell subclasses present throughout the primate neocortex with gene expression profiles that differ depending upon neocortical region. Here, we test whether the interareal differences in gene expression translate to regional specializations in the physiology and morphology of infragranular glutamatergic neurons by performing Patch-seq experiments in brain slices from the temporal cortex (TCx) and motor cortex (MCx) of the macaque. We confirm that transcriptomically defined extratelencephalically projecting neurons of layer 5 (L5 ET neurons) include retrogradely labeled corticospinal neurons in the MCx and find multiple physiological properties and ion channel genes that distinguish L5 ET from non-ET neurons in both areas. Additionally, while infragranular ET and non-ET neurons retain distinct neuronal properties across multiple regions, there are regional morpho-electric and gene expression specializations in the L5 ET subclass, providing mechanistic insights into the specialized functional architecture of the primate neocortex.

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.

GABA system in the prefrontal cortex involved in psychostimulant addiction

Drug addiction is a chronic and relapse brain disorder. Psychostimulants such as cocaine and amphetamine are highly addictive drugs. Abuse drugs target various brain areas in the nervous system. Recent studies have shown that the prefrontal cortex (PFC) plays a key role in regulating addictive behaviors. The PFC is made up of excitatory glutamatergic cells and gamma-aminobutyric acid (GABAergic) interneurons. Recently, studies showed that GABA level was related with psychostimulant addiction. In this review, we will introduce the role and mechanism of GABA and γ-aminobutyric acid receptors (GABARs) of the PFC in regulating drug addiction, especially in psychostimulant addiction.

Labile glutamate synaptic transmission in the adult CA1 stratum-lacunosum-moleculare region

The excitatory monosynaptic activation of hippocampal CA1 pyramidal cells is spatially segregated such that the proximal part of the apical dendritic tree in stratum radiatum (SR) receives input from the hippocampal CA3 region while the distal part in the stratum-lacunosum-moleculare (SLM) receives input mainly from the entorhinal cortex. The AMPA receptor-mediated (AMPA) signalling of SLM synapses in slices from neonatal rats was previously found to considerably differ from that of the SR synapses. In the present study, AMPA signalling of SLM synapses in 1-month-old rats has been examined, that is, when the hippocampus is essentially functionally mature. For the SR synapses, this time is characterized by a facilitatory shift in short-term plasticity, in the disappearance of labile postsynaptic AMPA signalling, a property thought to be important for early activity-dependent organization of neural circuits, and the expression of an adult form of long-term potentiation. We found that the SLM synapses alter their short-term plasticity similarly to that of the SR synapses. However, the labile postsynaptic AMPA signalling was not only maintained but substantially enhanced in the SLM synapses. The long-term potentiation observed was not of the adult form but like that of the neonatal SR synapses based on unsilencing of AMPA labile synapses. We propose that these features of the SLM synapses in the mature hippocampus will help to produce a flexible map of the multimodal sensory input reaching the SLM required for its conjunctive operation with the SR input to generate a proper functional output from the CA1 region.

Developmental Cajal-Retzius cell death contributes to the maturation of layer 1 cortical inhibition and somatosensory processing

The role of developmental cell death in the formation of brain circuits is not well understood. Cajal-Retzius cells constitute a major transient neuronal population in the mammalian neocortex, which largely disappears at the time of postnatal somatosensory maturation. In this study, we used mouse genetics, anatomical, functional, and behavioral approaches to explore the impact of the early postnatal death of Cajal-Retzius cells in the maturation of the cortical circuit. We find that before their death, Cajal-Retzius cells mainly receive inputs from layer 1 neurons, which can only develop their mature connectivity onto layer 2/3 pyramidal cells after Cajal-Retzius cells disappear. This developmental connectivity progression from layer 1 GABAergic to layer 2/3 pyramidal cells regulates sensory-driven inhibition within, and more so, across cortical columns. Here we show that Cajal-Retzius cell death prevention leads to layer 2/3 hyper-excitability, delayed learning and reduced performance in a multi-whisker-dependent texture discrimination task.

Functional Dynamics and Selectivity of Two Parallel Corticocortical Pathways from Motor Cortex to Layer 5 Circuits in Somatosensory Cortex

In the rodent whisker system, active sensing and sensorimotor integration are mediated in part by the dynamic interactions between the motor cortex (M1) and somatosensory cortex (S1). However, understanding these dynamic interactions requires knowledge about the synapses and how specific neurons respond to their input. Here, we combined optogenetics, retrograde labeling, and electrophysiology to characterize the synaptic connections between M1 and layer 5 (L5) intratelencephalic (IT) and pyramidal tract (PT) neurons in S1 of mice (both sexes). We found that M1 synapses onto IT cells displayed modest short-term depression, whereas synapses onto PT neurons showed robust short-term facilitation. Despite M1 inputs to IT cells depressing, their slower kinetics resulted in summation and a response that increased during short trains. In contrast, summation was minimal in PT neurons due to the fast time course of their M1 responses. The functional consequences of this reduced summation, however, were outweighed by the strong facilitation at these M1 synapses, resulting in larger response amplitudes in PT neurons than IT cells during repetitive stimulation. To understand the impact of facilitating M1 inputs on PT output, we paired trains of inputs with single backpropagating action potentials, finding that repetitive M1 activation increased the probability of bursts in PT cells without impacting the time dependence of this coupling. Thus, there are two parallel but dynamically distinct systems of M1 synaptic excitation in L5 of S1, each defined by the short-term dynamics of its synapses, the class of postsynaptic neurons, and how the neurons respond to those inputs.

Dysfunctions of cellular context-sensitivity in neurodevelopmental learning disabilities

Pyramidal neurons have a pivotal role in the cognitive capabilities of neocortex. Though they have been predominantly modeled as integrate-and-fire point processors, many of them have another point of input integration in their apical dendrites that is central to mechanisms endowing them with the sensitivity to context that underlies basic cognitive capabilities. Here we review evidence implicating impairments of those mechanisms in three major neurodevelopmental disabilities, fragile X, Down syndrome, and fetal alcohol spectrum disorders. Multiple dysfunctions of the mechanisms by which pyramidal cells are sensitive to context are found to be implicated in all three syndromes. Further deciphering of these cellular mechanisms would lead to the understanding of and therapies for learning disabilities beyond any that are currently available.

Functional dynamics and selectivity of two parallel corticocortical pathways from motor cortex to layer 5 circuits in somatosensory cortex

In the rodent whisker system, active sensing and sensorimotor integration are mediated in part by the dynamic interactions between the motor cortex (M1) and somatosensory cortex (S1). However, understanding these dynamic interactions requires knowledge about the synapses and how specific neurons respond to their input. Here, we combined optogenetics, retrograde labeling, and electrophysiology to characterize the synaptic connections between M1 and layer 5 (L5) intratelencephalic (IT) and pyramidal tract (PT) neurons in S1 of mice (both sexes). We found that M1 synapses onto IT cells displayed modest short-term depression, whereas synapses onto PT neurons showed robust short-term facilitation. Despite M1 inputs to IT cells depressing, their slower kinetics resulted in summation and a response that increased during short trains. In contrast, summation was minimal in PT neurons due to the fast time course of their M1 responses. The functional consequences of this reduced summation, however, were outweighed by the strong facilitation at these M1 synapses, resulting in larger response amplitudes in PT neurons than IT cells during repetitive stimulation. To understand the impact of facilitating M1 inputs on PT output, we paired trains of inputs with single backpropagating action potentials, finding that repetitive M1 activation increased the probability of bursts in PT cells without impacting the time-dependence of this coupling. Thus, there are two parallel but dynamically distinct systems of M1 synaptic excitation in L5 of S1, each defined by the short-term dynamics of its synapses, the class of postsynaptic neurons, and how the neurons respond to those inputs.

An integrative, multiscale view on neural theories of consciousness

How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories.

Uncovering the Role of the Early Visual Cortex in Visual Mental Imagery

The question of whether the early visual cortex (EVC) is involved in visual mental imagery remains a topic of debate. In this paper, I propose that the inconsistency in findings can be explained by the unique challenges associated with investigating EVC activity during imagery. During perception, the EVC processes low-level features, which means that activity is highly sensitive to variation in visual details. If the EVC has the same role during visual mental imagery, any change in the visual details of the mental image would lead to corresponding changes in EVC activity. Within this context, the question should not be whether the EVC is 'active' during imagery but how its activity relates to specific imagery properties. Studies using methods that are sensitive to variation in low-level features reveal that imagery can recruit the EVC in similar ways as perception. However, not all mental images contain a high level of visual details. Therefore, I end by considering a more nuanced view, which states that imagery can recruit the EVC, but that does not mean that it always does so.

Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the potential biological underpinnings specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.

Laying the foundations for a theory of consciousness: the significance of critical brain dynamics for the formation of conscious states

Empirical evidence indicates that conscious states, distinguished by the presence of phenomenal qualities, are closely linked to synchronized neural activity patterns whose dynamical characteristics can be attributed to self-organized criticality and phase transitions. These findings imply that insight into the mechanism by which the brain controls phase transitions will provide a deeper understanding of the fundamental mechanism by which the brain manages to transcend the threshold of consciousness. This article aims to show that the initiation of phase transitions and the formation of synchronized activity patterns is due to the coupling of the brain to the zero-point field (ZPF), which plays a central role in quantum electrodynamics (QED). The ZPF stands for the presence of ubiquitous vacuum fluctuations of the electromagnetic field, represented by a spectrum of normal modes. With reference to QED-based model calculations, the details of the coupling mechanism are revealed, suggesting that critical brain dynamics is governed by the resonant interaction of the ZPF with the most abundant neurotransmitter glutamate. The pyramidal neurons in the cortical microcolumns turn out to be ideally suited to control this interaction. A direct consequence of resonant glutamate-ZPF coupling is the amplification of specific ZPF modes, which leads us to conclude that the ZPF is the key to the understanding of consciousness and that the distinctive feature of neurophysiological processes associated with conscious experience consists in modulating the ZPF. Postulating that the ZPF is an inherently sentient field and assuming that the spectrum of phenomenal qualities is represented by the normal modes of the ZPF, the significance of resonant glutamate-ZPF interaction for the formation of conscious states becomes apparent in that the amplification of specific ZPF modes is inextricably linked with the excitation of specific phenomenal qualities. This theory of consciousness, according to which phenomenal states arise through resonant amplification of zero-point modes, is given the acronym TRAZE. An experimental setup is specified that can be used to test a corollary of the theory, namely, the prediction that normally occurring conscious perceptions are absent under experimental conditions in which resonant glutamate-ZPF coupling is disrupted.

The spike-timing-dependent plasticity of VIP interneurons in motor cortex

The plasticity of inhibitory interneurons (INs) plays an important role in the organization and maintenance of cortical microcircuits. Given the many different IN types, there is an even greater diversity in synapse-type-specific plasticity learning rules at excitatory to excitatory (E→I), I→E, and I→I synapses. I→I synapses play a key disinhibitory role in cortical circuits. Because they typically target other INs, vasoactive intestinal peptide (VIP) INs are often featured in I→I→E disinhibition, which upregulates activity in nearby excitatory neurons. VIP IN dysregulation may thus lead to neuropathologies such as epilepsy. In spite of the important activity regulatory role of VIP INs, their long-term plasticity has not been described. Therefore, we characterized the phenomenology of spike-timing-dependent plasticity (STDP) at inputs and outputs of genetically defined VIP INs. Using a combination of whole-cell recording, 2-photon microscopy, and optogenetics, we explored I→I STDP at layer 2/3 (L2/3) VIP IN outputs onto L5 Martinotti cells (MCs) and basket cells (BCs). We found that VIP IN→MC synapses underwent causal long-term depression (LTD) that was presynaptically expressed. VIP IN→BC connections, however, did not undergo any detectable plasticity. Conversely, using extracellular stimulation, we explored E→I STDP at inputs to VIP INs which revealed long-term potentiation (LTP) for both causal and acausal timings. Taken together, our results demonstrate that VIP INs possess synapse-type-specific learning rules at their inputs and outputs. This suggests the possibility of harnessing VIP IN long-term plasticity to control activity-related neuropathologies such as epilepsy.

Cellular mechanisms of cooperative context-sensitive predictive inference

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.

Modular horizontal network within mouse primary visual cortex

Interactions between feedback connections from higher cortical areas and local horizontal connections within primary visual cortex (V1) were shown to play a role in contextual processing in different behavioral states. Layer 1 (L1) is an important part of the underlying network. This cell-sparse layer is a target of feedback and local inputs, and nexus for contacts onto apical dendrites of projection neurons in the layers below. Importantly, L1 is a site for coupling inputs from the outside world with internal information. To determine whether all of these circuit elements overlap in L1, we labeled the horizontal network within mouse V1 with anterograde and retrograde viral tracers. We found two types of local horizontal connections: short ones that were tangentially limited to the representation of the point image, and long ones which reached beyond the receptive field center, deep into its surround. The long connections were patchy and terminated preferentially in M2 muscarinic acetylcholine receptor-negative (M2-) interpatches. Anterogradely labeled inputs overlapped in M2-interpatches with apical dendrites of retrogradely labeled L2/3 and L5 cells, forming module-selective loops between topographically distant locations. Previous work showed that L1 of M2-interpatches receive inputs from the lateral posterior thalamic nucleus (LP) and from a feedback network from areas of the medial dorsal stream, including the secondary motor cortex. Together, these findings suggest that interactions in M2-interpatches play a role in processing visual inputs produced by object-and self-motion.

Layer 6b controls brain state via apical dendrites and the higher-order thalamocortical system

The deepest layer of the cortex (layer 6b [L6b]) contains relatively few neurons, but it is the only cortical layer responsive to the potent wake-promoting neuropeptide orexin/hypocretin. Can these few neurons significantly influence brain state? Here, we show that L6b-photoactivation causes a surprisingly robust enhancement of attention-associated high-gamma oscillations and population spiking while abolishing slow waves in sleep-deprived mice. To explain this powerful impact on brain state, we investigated L6b's synaptic output using optogenetics, electrophysiology, and monoCaTChR ex vivo. We found powerful output in the higher-order thalamus and apical dendrites of L5 pyramidal neurons, via L1a and L5a, as well as in superior colliculus and L6 interneurons. L6b subpopulations with distinct morphologies and short- and long-term plasticities project to these diverse targets. The L1a-targeting subpopulation triggered powerful NMDA-receptor-dependent spikes that elicited burst firing in L5. We conclude that orexin/hypocretin-activated cortical neurons form a multifaceted, fine-tuned circuit for the sustained control of the higher-order thalamocortical system.

An operating principle of the cerebral cortex, and a cellular mechanism for attentional trial-and-error pattern learning and useful classification extraction

A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate "guess" neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention toward important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization cross-induction, enabling minicolumn functions such as the creation of powerful hierarchical "hyperneurons" and the internal representation of the external world. We suggest building blocks to extend the microcircuit theory to network-level processing, which, interestingly, yields variants resembling the artificial neural networks currently in use. On a more speculative note, we conjecture that principles of intelligence in universes governed by certain types of physical laws might resemble ours.

Why birds are smart

Many cognitive neuroscientists believe that both a large brain and an isocortex are crucial for complex cognition. Yet corvids and parrots possess non-cortical brains of just 1-25 g, and these birds exhibit cognitive abilities comparable with those of great apes such as chimpanzees, which have brains of about 400 g. This opinion explores how this cognitive equivalence is possible. We propose four features that may be required for complex cognition: a large number of associative pallial neurons, a prefrontal cortex (PFC)-like area, a dense dopaminergic innervation of association areas, and dynamic neurophysiological fundaments for working memory. These four neural features have convergently evolved and may therefore represent 'hard to replace' mechanisms enabling complex cognition.

Disinhibition by VIP interneurons is orthogonal to cross-modal attentional modulation in primary visual cortex

Attentional modulation of sensory processing is a key feature of cognition; however, its neural circuit basis is poorly understood. A candidate mechanism is the disinhibition of pyramidal cells through vasoactive intestinal peptide (VIP) and somatostatin (SOM)-positive interneurons. However, the interaction of attentional modulation and VIP-SOM disinhibition has never been directly tested. We used all-optical methods to bi-directionally manipulate VIP interneuron activity as mice performed a cross-modal attention-switching task. We measured the activities of VIP, SOM, and parvalbumin (PV)-positive interneurons and pyramidal neurons identified in the same tissue and found that although activity in all cell classes was modulated by both attention and VIP manipulation, their effects were orthogonal. Attention and VIP-SOM disinhibition relied on distinct patterns of changes in activity and reorganization of interactions between inhibitory and excitatory cells. Circuit modeling revealed a precise network architecture consistent with multiplexing strong yet non-interacting modulations in the same neural population.

Cortical depth profiles in primary visual cortex for illusory and imaginary experiences

Visual illusions and mental imagery are non-physical sensory experiences that involve cortical feedback processing in the primary visual cortex. Using laminar functional magnetic resonance imaging (fMRI) in two studies, we investigate if information about these internal experiences is visible in the activation patterns of different layers of primary visual cortex (V1). We find that imagery content is decodable mainly from deep layers of V1, whereas seemingly 'real' illusory content is decodable mainly from superficial layers. Furthermore, illusory content shares information with perceptual content, whilst imagery content does not generalise to illusory or perceptual information. Together, our results suggest that illusions and imagery, which differ immensely in their subjective experiences, also involve partially distinct early visual microcircuits. However, overlapping microcircuit recruitment might emerge based on the nuanced nature of subjective conscious experience.

The locus coeruleus as a global model failure system

Predictive processing models posit that brains constantly attempt to predict their sensory inputs. Prediction errors signal when these predictions are incorrect and are thought to be instructive signals that drive corrective plasticity. Recent findings support the idea that the locus coeruleus (LC) - a brain-wide neuromodulatory system - signals several types of prediction error. I discuss how these findings support models proposing that the LC signals global model failures: instances where predictions about the world are strongly violated. Focusing on the cortex, I explore the utility of this signal in learning rate control, how the LC circuit may compute the signal, and how this view may aid our understanding of neurodivergence.

Context association in pyramidal neurons through local synaptic plasticity in apical dendrites

The unique characteristics of neocortical pyramidal neurons are thought to be crucial for many aspects of information processing and learning in the brain. Experimental data suggests that their segregation into two distinct compartments, the basal dendrites close to the soma and the apical dendrites branching out from the thick apical dendritic tuft, plays an essential role in cortical organization. A recent hypothesis states that layer 5 pyramidal cells associate top-down contextual information arriving at their apical tuft with features of the sensory input that predominantly arrives at their basal dendrites. It has however remained unclear whether such context association could be established by synaptic plasticity processes. In this work, we formalize the objective of such context association learning through a mathematical loss function and derive a plasticity rule for apical synapses that optimizes this loss. The resulting plasticity rule utilizes information that is available either locally at the synapse, through branch-local NMDA spikes, or through global Ca2+events, both of which have been observed experimentally in layer 5 pyramidal cells. We show in computer simulations that the plasticity rule enables pyramidal cells to associate top-down contextual input patterns with high somatic activity. Furthermore, it enables networks of pyramidal neuron models to perform context-dependent tasks and enables continual learning by allocating new dendritic branches to novel contexts.

Responses to Pattern-Violating Visual Stimuli Evolve Differently Over Days in Somata and Distal Apical Dendrites

Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In line with this conjecture, different responses to pattern-matching vs pattern-violating visual stimuli have been observed in both spiking and somatic calcium imaging data. However, it remains unknown whether these pattern-violation signals are different between the distal apical dendrites, which are heavily targeted by top-down signals, and the somata, where bottom-up information is primarily integrated. Furthermore, it is unknown how responses to pattern-violating stimuli evolve over time as an animal gains more experience with them. Here, we address these unanswered questions by analyzing responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons tracked over multiple days in primary visual cortex of awake, behaving female and male mice. We use sequences of Gabor patches with patterns in their orientations to create pattern-matching and pattern-violating stimuli, and two-photon calcium imaging to record neuronal responses. Many neurons in both layers show large differences between their responses to pattern-matching and pattern-violating stimuli. Interestingly, these responses evolve in opposite directions in the somata and distal apical dendrites, with somata becoming less sensitive to pattern-violating stimuli and distal apical dendrites more sensitive. These differences between the somata and distal apical dendrites may be important for hierarchical computation of sensory predictions and learning, since these two compartments tend to receive bottom-up and top-down information, respectively.

Synapse-specific burst coding sustained by local axonal translation

Neurotransmission in the brain is unreliable, suggesting that high-frequency spike bursts rather than individual spikes carry the neural code. For instance, cortical pyramidal neurons rely on bursts in memory formation. Protein synthesis is another key factor in long-term synaptic plasticity and learning but is widely considered unnecessary for synaptic transmission. Here, however, we show that burst neurotransmission at synapses between neocortical layer 5 pyramidal cells depends on axonal protein synthesis linked to presynaptic NMDA receptors and mTOR. We localized protein synthesis to axons with laser axotomy and puromycylation live imaging. We whole-cell recorded connected neurons to reveal how translation sustained readily releasable vesicle pool size and replenishment rate. We live imaged axons and found sparsely docked RNA granules, suggesting synapse-specific regulation. In agreement, translation boosted neurotransmission onto excitatory but not inhibitory basket or Martinotti cells. Local axonal mRNA translation is thus a hitherto unappreciated principle for sustaining burst coding at specific synapse types.

Short-term Hebbian learning can implement transformer-like attention

Transformers have revolutionized machine learning models of language and vision, but their connection with neuroscience remains tenuous. Built from attention layers, they require a mass comparison of queries and keys that is difficult to perform using traditional neural circuits. Here, we show that neurons can implement attention-like computations using short-term, Hebbian synaptic potentiation. We call our mechanism the match-and-control principle and it proposes that when activity in an axon is synchronous, or matched, with the somatic activity of a neuron that it synapses onto, the synapse can be briefly strongly potentiated, allowing the axon to take over, or control, the activity of the downstream neuron for a short time. In our scheme, the keys and queries are represented as spike trains and comparisons between the two are performed in individual spines allowing for hundreds of key comparisons per query and roughly as many keys and queries as there are neurons in the network.

Modeling apical and basal tree contribution to orientation selectivity in a mouse primary visual cortex layer 2/3 pyramidal cell

Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical and possibly functional sub-units. To elucidate the contribution of each tree to the encoding of visual stimuli at the somatic level, we modeled the response pattern of a mouse L2/3 V1 pyramidal neuron to orientation tuned synaptic input. Towards this goal, we used a morphologically detailed computational model of a single cell that replicates electrophysiological and two-photon imaging data. Our simulations predict a synergistic effect of apical and basal trees on somatic action potential generation: basal tree activity, in the form of either depolarization or dendritic spiking, is necessary for producing somatic activity, despite the fact that most somatic spikes are heavily driven by apical dendritic spikes. This model provides evidence for synergistic computations taking place in the basal and apical trees of the L2/3 V1 neuron along with mechanistic explanations for tree-specific contributions and emphasizes the potential role of predictive and attentional feedback input in these cells.

Gene expression in the primate orbitofrontal cortex related to anxious temperament

Anxiety disorders are among the most prevalent psychiatric disorders, causing significant suffering and disability. Relative to other psychiatric disorders, anxiety disorders tend to emerge early in life, supporting the importance of developmental mechanisms in their emergence and maintenance. Behavioral inhibition (BI) is a temperament that emerges early in life and, when stable and extreme, is linked to an increased risk for the later development of anxiety disorders and other stress-related psychopathology. Understanding the neural systems and molecular mechanisms underlying this dispositional risk could provide insight into treatment targets for anxiety disorders. Nonhuman primates (NHPs) have an anxiety-related temperament, called anxious temperament (AT), that is remarkably similar to BI in humans, facilitating the design of highly translational models for studying the early risk for stress-related psychopathology. Because of the recent evolutionary divergence between humans and NHPs, many of the anxiety-related brain regions that contribute to psychopathology are highly similar in terms of their structure and function, particularly with respect to the prefrontal cortex. The orbitofrontal cortex plays a critical role in the flexible encoding and regulation of threat responses, in part through connections with subcortical structures like the amygdala. Here, we explore individual differences in the transcriptional profile of cells within the region, using laser capture microdissection and single nuclear sequencing, providing insight into the molecules underlying individual differences in AT-related function of the pOFC, with a particular focus on previously implicated cellular systems, including neurotrophins and glucocorticoid signaling.

Beyond spiking networks: The computational advantages of dendritic amplification and input segregation

The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve state-of-the-art performance in machine learning. Recent works have proposed that input segregation (neurons receive sensory information and higher-order feedback in segregated compartments), and nonlinear dendritic computation would support error backpropagation in biological neurons. However, these approaches require propagating errors with a fine spatiotemporal structure to all the neurons, which is unlikely to be feasible in a biological network. To relax this assumption, we suggest that bursts and dendritic input segregation provide a natural support for target-based learning, which propagates targets rather than errors. A coincidence mechanism between the basal and the apical compartments allows for generating high-frequency bursts of spikes. This architecture supports a burst-dependent learning rule, based on the comparison between the target bursting activity triggered by the teaching signal and the one caused by the recurrent connections, providing support for target-based learning. We show that this framework can be used to efficiently solve spatiotemporal tasks, such as context-dependent store and recall of three-dimensional trajectories, and navigation tasks. Finally, we suggest that this neuronal architecture naturally allows for orchestrating "hierarchical imitation learning", enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks. We show a possible implementation of this in a two-level network, where the high network produces the contextual signal for the low network.

How deep is the brain? The shallow brain hypothesis

Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.