Widespread and Highly Correlated Somato-dendritic Activity in Cortical Layer 5 Neurons

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

Histological evidence suggests that the estrous cycle exerts a powerful influence on CA1 neurons in the mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice across multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with spine density peaking during proestrus when estradiol levels are highest. These morphological changes coincided with greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed greater place field stability during proestrus, evident at both the single-cell and population levels. These findings demonstrate that the estrous cycle drives large-scale structural and functional plasticity in hippocampal neurons essential for learning and memory.

Tuft dendrites in frontal motor cortex enable flexible learning

Flexible learning relies on integrating sensory and contextual information to adjust behavioral output in different environments. The anterolateral motor cortex (ALM) is a frontal area critical for action selection in rodents. Here we show that inputs critical to decision-making converge on the apical tuft dendrites of L5b pyramidal neurons in ALM. We therefore investigated the role of these dendrites in a rule-switching paradigm. Activation of dendrite-inhibiting layer 1 interneurons impaired relearning, without affecting previously learned behavior. Remarkably, this inhibition profoundly suppressed calcium activity selectively in dendritic shafts but not spines while reducing burst firing. Moreover, excitatory synaptic inputs to tuft dendrites exhibited rule-dependent clustering. We conclude that active dendritic integration is a key computational component of flexible learning.

Higher-order thalamic input to cortex selectively conveys state information

Communication among neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for corticocortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of neurons and axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice has a unique impact on the posterior medial visual cortex (PM). Whereas corticocortical projections from lower cortical areas convey robust visual information to PM, higher-order thalamocortical projections convey information about global arousal state. Together, these findings suggest a key role for the higher-order thalamus in providing contextual signals that may flexibly modulate cortical sensory processing.

Dendritic excitations govern back-propagation via a spike-rate accelerometer

Dendrites on neurons support electrical excitations, but the computational significance of these events is not well understood. We developed molecular, optical, and computational tools for all-optical electrophysiology in dendrites. We mapped sub-millisecond voltage dynamics throughout the dendritic trees of CA1 pyramidal neurons under diverse optogenetic and synaptic stimulus patterns, in acute brain slices. Our data show history-dependent spike back-propagation in distal dendrites, driven by locally generated Na+ spikes (dSpikes). Dendritic depolarization created a transient window for dSpike propagation, opened by A-type KV channel inactivation, and closed by slow NaV inactivation. Collisions of dSpikes with synaptic inputs triggered calcium channel and N-methyl-D-aspartate receptor (NMDAR)-dependent dendritic plateau potentials and accompanying complex spikes at the soma. This hierarchical ion channel network acts as a spike-rate accelerometer, providing an intuitive picture connecting dendritic biophysics to associative plasticity rules.

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.

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.

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.

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.

Dynamic lateralization in contralateral-projecting corticospinal neurons during motor learning

Understanding the adaptability of the motor cortex in response to bilateral motor tasks is crucial for advancing our knowledge of neural plasticity and motor learning. Here we aim to investigate the dynamic lateralization of contralateral-projecting corticospinal neurons (cpCSNs) during such tasks. Utilizing in vivo two-photon calcium imaging, we observe cpCSNs in mice performing a "left-right" lever-press task. Our findings reveal heterogeneous populational dynamics in cpCSNs: a marked decrease in activity during ipsilateral motor learning, in contrast to maintained activity during contralateral motor learning. Notably, individual cpCSNs show dynamic shifts in engagement with ipsilateral and contralateral movements, displaying an evolving pattern of activation over successive days. It suggests that cpCSNs exhibit adaptive changes in activation patterns in response to ipsilateral and contralateral movements, highlighting a flexible reorganization during motor learning This reconfiguration underscores the dynamic nature of cortical lateralization in motor learning and offers insights for neuromotor rehabilitation.

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 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.

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.

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

Histological evidence suggests that the estrous cycle exerts a powerful effect on CA1 neurons in mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice over multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with heightened density during periods of greater estradiol (proestrus). These morphological changes were accompanied by greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed enhanced place field stability during proestrus, evident at the single-cell and population level. These results establish the estrous cycle as a driver of large-scale structural and functional plasticity in hippocampal circuits essential for learning and memory.

Population coding of time-varying sounds in the nonlemniscal inferior colliculus

The inferior colliculus (IC) of the midbrain is important for complex sound processing, such as discriminating conspecific vocalizations and human speech. The IC's nonlemniscal, dorsal "shell" region is likely important for this process, as neurons in these layers project to higher-order thalamic nuclei that subsequently funnel acoustic signals to the amygdala and nonprimary auditory cortices, forebrain circuits important for vocalization coding in a variety of mammals, including humans. However, the extent to which shell IC neurons transmit acoustic features necessary to discern vocalizations is less clear, owing to the technical difficulty of recording from neurons in the IC's superficial layers via traditional approaches. Here, we use two-photon Ca2+ imaging in mice of either sex to test how shell IC neuron populations encode the rate and depth of amplitude modulation, important sound cues for speech perception. Most shell IC neurons were broadly tuned, with a low neurometric discrimination of amplitude modulation rate; only a subset was highly selective to specific modulation rates. Nevertheless, neural network classifier trained on fluorescence data from shell IC neuron populations accurately classified amplitude modulation rate, and decoding accuracy was only marginally reduced when highly tuned neurons were omitted from training data. Rather, classifier accuracy increased monotonically with the modulation depth of the training data, such that classifiers trained on full-depth modulated sounds had median decoding errors of ∼0.2 octaves. Thus, shell IC neurons may transmit time-varying signals via a population code, with perhaps limited reliance on the discriminative capacity of any individual neuron.NEW & NOTEWORTHY The IC's shell layers originate a "nonlemniscal" pathway important for perceiving vocalization sounds. However, prior studies suggest that individual shell IC neurons are broadly tuned and have high response thresholds, implying a limited reliability of efferent signals. Using Ca2+ imaging, we show that amplitude modulation is accurately represented in the population activity of shell IC neurons. Thus, downstream targets can read out sounds' temporal envelopes from distributed rate codes transmitted by populations of broadly tuned neurons.

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.

Auditory Corticofugal Neurons Transmit Auditory and Non-auditory Information During Behavior

Layer 5 pyramidal neurons of sensory cortices project "corticofugal" axons to myriad sub-cortical targets, thereby broadcasting high-level signals important for perception and learning. Recent studies suggest dendritic Ca2+ spikes as key biophysical mechanisms supporting corticofugal neuron function: these long-lasting events drive burst firing, thereby initiating uniquely powerful signals to modulate sub-cortical representations and trigger learning-related plasticity. However, the behavioral relevance of corticofugal dendritic spikes is poorly understood. We shed light on this issue using 2-photon Ca2+ imaging of auditory corticofugal dendrites as mice of either sex engage in a GO/NO-GO sound-discrimination task. Unexpectedly, only a minority of dendritic spikes were triggered by behaviorally relevant sounds under our conditions. Task related dendritic activity instead mostly followed sound cue termination and co-occurred with mice's instrumental licking during the answer period of behavioral trials, irrespective of reward consumption. Temporally selective, optogenetic silencing of corticofugal neurons during the trial answer period impaired auditory discrimination learning. Thus, auditory corticofugal systems' contribution to learning and plasticity may be partially nonsensory in nature.

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.

Transthalamic input to higher-order cortex selectively conveys state information

Communication among different neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for cortico-cortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of cortical neurons and projection axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice conveys a specialized stream of information to higher-order visual cortex. Whereas corticocortical projections from lower cortical areas convey robust visual information, higher-order thalamocortical projections convey strong behavioral state information. Together, these findings suggest a key role for higher-order thalamus in providing contextual signals that flexibly modulate sensory processing in higher-order cortex.

Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

The inferior colliculus (IC) of the midbrain is important for complex sound processing, such as discriminating conspecific vocalizations and human speech. The IC's non-lemniscal, dorsal "shell" region is likely important for this process, as neurons in these layers project to higher-order thalamic nuclei that subsequently funnel acoustic signals to the amygdala and non-primary auditory cortices; forebrain circuits important for vocalization coding in a variety of mammals, including humans. However, the extent to which shell IC neurons transmit acoustic features necessary to discern vocalizations is less clear, owing to the technical difficulty of recording from neurons in the IC's superficial layers via traditional approaches. Here we use 2-photon Ca2+ imaging in mice of either sex to test how shell IC neuron populations encode the rate and depth of amplitude modulation, important sound cues for speech perception. Most shell IC neurons were broadly tuned, with a low neurometric discrimination of amplitude modulation rate; only a subset were highly selective to specific modulation rates. Nevertheless, neural network classifier trained on fluorescence data from shell IC neuron populations accurately classified amplitude modulation rate, and decoding accuracy was only marginally reduced when highly tuned neurons were omitted from training data. Rather, classifier accuracy increased monotonically with the modulation depth of the training data, such that classifiers trained on full-depth modulated sounds had median decoding errors of ~0.2 octaves. Thus, shell IC neurons may transmit time-varying signals via a population code, with perhaps limited reliance on the discriminative capacity of any individual neuron.

The dendritic engram

Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions.

Voltage dynamics of dendritic integration and back-propagation in vivo

Neurons integrate synaptic inputs within their dendrites and produce spiking outputs, which then propagate down the axon and back into the dendrites where they contribute to plasticity. Mapping the voltage dynamics in dendritic arbors of live animals is crucial for understanding neuronal computation and plasticity rules. Here we combine patterned channelrhodopsin activation with dual-plane structured illumination voltage imaging, for simultaneous perturbation and monitoring of dendritic and somatic voltage in Layer 2/3 pyramidal neurons in anesthetized and awake mice. We examined the integration of synaptic inputs and compared the dynamics of optogenetically evoked, spontaneous, and sensory-evoked back-propagating action potentials (bAPs). Our measurements revealed a broadly shared membrane voltage throughout the dendritic arbor, and few signatures of electrical compartmentalization among synaptic inputs. However, we observed spike rate acceleration-dependent propagation of bAPs into distal dendrites. We propose that this dendritic filtering of bAPs may play a critical role in activity-dependent plasticity.

Cortico-cortical feedback engages active dendrites in visual cortex

Sensory processing in the neocortex requires both feedforward and feedback information flow between cortical areas¹. In feedback processing, higher-level representations provide contextual information to lower levels, and facilitate perceptual functions such as contour integration and figure-ground segmentation^2,3. However, we have limited understanding of the circuit and cellular mechanisms that mediate feedback influence. Here we use long-range all-optical connectivity mapping in mice to show that feedback influence from the lateromedial higher visual area (LM) to the primary visual cortex (V1) is spatially organized. When the source and target of feedback represent the same area of visual space, feedback is relatively suppressive. By contrast, when the source is offset from the target in visual space, feedback is relatively facilitating. Two-photon calcium imaging data show that this facilitating feedback is nonlinearly integrated in the apical tuft dendrites of V1 pyramidal neurons: retinotopically offset (surround) visual stimuli drive local dendritic calcium signals indicative of regenerative events, and two-photon optogenetic activation of LM neurons projecting to identified feedback-recipient spines in V1 can drive similar branch-specific local calcium signals. Our results show how neocortical feedback connectivity and nonlinear dendritic integration can together form a substrate to support both predictive and cooperative contextual interactions.

Topologically induced suppression of explosive synchronization

Nowadays, explosive synchronization is a well-documented phenomenon consisting in a first-order transition that may coexist with classical synchronization. Typically, explosive synchronization occurs when the network structure is represented by the classical graph Laplacian, and the node frequency and its degree are correlated. Here, we answer the question on whether this phenomenon can be observed in networks when the oscillators are coupled via degree-biased Laplacian operators. We not only observe that this is the case but also that this new representation naturally controls the transition from explosive to standard synchronization in a network. We prove analytically that explosive synchronization emerges when using this theoretical setting in star-like networks. As soon as this star-like network is topologically converted into a network containing cycles, the explosive synchronization gives rise to classical synchronization. Finally, we hypothesize that this mechanism may play a role in switching from normal to explosive states in the brain, where explosive synchronization has been proposed to be related to some pathologies like epilepsy and fibromyalgia.

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

Anatomically segregated apical and basal dendrites of pyramidal neurons receive functionally distinct inputs, but it is unknown if this results in compartment-level functional diversity during behavior. Here we imaged calcium signals from apical dendrites, soma, and basal dendrites of pyramidal neurons in area CA3 of mouse hippocampus during head-fixed navigation. To examine dendritic population activity, we developed computational tools to identify dendritic regions of interest and extract accurate fluorescence traces. We identified robust spatial tuning in apical and basal dendrites, similar to soma, though basal dendrites had reduced activity rates and place field widths. Across days, apical dendrites were more stable than soma or basal dendrites, resulting in better decoding of the animal's position. These population-level dendritic differences may reflect functionally distinct input streams leading to different dendritic computations in CA3. These tools will facilitate future studies of signal transformations between cellular compartments and their relation to behavior.

HMGB1 mediates synaptic loss and cognitive impairment in an animal model of sepsis-associated encephalopathy

BACKGROUND

Microglial activation-mediated neuroinflammation is one of the essential pathogenic mechanisms of sepsis-associated encephalopathy (SAE). Mounting evidence suggests that high mobility group box-1 protein (HMGB1) plays a pivotal role in neuroinflammation and SAE, yet the mechanism by which HMGB1 induces cognitive impairment in SAE remains unclear. Therefore, this study aimed to investigate the mechanism of HMGB1 underlying cognitive impairment in SAE.

METHODS

An SAE model was established by cecal ligation and puncture (CLP); animals in the sham group underwent cecum exposure alone without ligation and perforation. Mice in the inflachromene (ICM) group were continuously injected with ICM intraperitoneally at a daily dose of 10 mg/kg for 9 days starting 1 h before the CLP operation. The open field, novel object recognition, and Y maze tests were performed on days 14-18 after surgery to assess locomotor activity and cognitive function. HMGB1 secretion, the state of microglia, and neuronal activity were measured by immunofluorescence. Golgi staining was performed to detect changes in neuronal morphology and dendritic spine density. In vitro electrophysiology was performed to detect changes in long-term potentiation (LTP) in the CA1 of the hippocampus. In vivo electrophysiology was performed to detect the changes in neural oscillation of the hippocampus.

RESULTS

CLP-induced cognitive impairment was accompanied by increased HMGB1 secretion and microglial activation. The phagocytic capacity of microglia was enhanced, resulting in aberrant pruning of excitatory synapses in the hippocampus. The loss of excitatory synapses reduced neuronal activity, impaired LTP, and decreased theta oscillation in the hippocampus. Inhibiting HMGB1 secretion by ICM treatment reversed these changes.

CONCLUSIONS

HMGB1 induces microglial activation, aberrant synaptic pruning, and neuron dysfunction in an animal model of SAE, leading to cognitive impairment. These results suggest that HMGB1 might be a target for SAE treatment.

Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

Endogenous Modulators of NMDA Receptor Control Dendritic Field Expansion of Cortical Neurons

Impairments of N-methyl-D-aspartate receptor (NMDAR) activity have been implicated in several neuropsychiatric disorders, with pharmacological inhibition of NMDAR-mediated currents and associated neurobehavioral changes considered as a model of schizophrenia. We analyzed the effects of brief and long-term exposure of rat cortical cultures to the most prevalent endogenous modulators of NMDAR (kynurenic acid, pregnenolone sulfate, spermidine, and zinc) on neuronal viability, stimulation-induced release of glutamate, and dendritic morphology with synaptic density. Both, glutamate release and neuronal viability studies revealed no difference between the test and control groups. No differences were also observed in the number of dendritic branching and length, or density of synaptic connections and neuronal soma size. Comparison of the extent of dendritic projections and branching patterns, however, revealed enhanced distal arborization with the expansion of the dendritic area under prolonged treatment of cultures with physiological concentrations of NMDAR modulators, with differences reaching significance in spermidine and pregnenolone sulfate tests. Measurements of the density of glutamatergic synapses showed consistency across all neuronal groups, except those treated with pregnenolone sulfate, which showed a reduction of PSD-95-positive elements. Overall, our data suggest that constitutive glutamatergic activity mediated by NMDAR controls the dendritic field expansion and can influence the integrative properties of cortical neurons.

Daily two-photon neuronal population imaging with targeted single-cell electrophysiology and subcellular imaging in auditory cortex of behaving mice

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca²⁺ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca²⁺ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

Light-evoked dendritic spikes in sustained but not transient rabbit retinal ganglion cells

Dendritic computations have a central role in neuronal function, but it is unknown how cell-class heterogeneity of dendritic electrical excitability shapes physiologically engaged neuronal and circuit computations. To address this, we examined dendritic integration in closely related classes of retinal ganglion cells (GCs) using simultaneous somato-dendritic electrical recording techniques in a functionally intact circuit. Simultaneous recordings revealed sustained OFF-GCs generated powerful dendritic spikes in response to visual input that drove action potential firing. In contrast, the dendrites of transient OFF-GCs were passive and did not generate dendritic spikes. Dendritic spike generation allowed sustained, but not transient, OFF-GCs to signal into action potential output the local motion of visual stimuli to produce a continuous wave of action potential firing in adjacent cells as images moved across the retina. Conversely, this representation was highly fragmented in transient OFF-GCs. Thus, a heterogeneity of dendritic excitability defines the computations executed by classes of GCs.

Assessing Local and Branch-specific Activity in Dendrites

Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.

Optimizing interneuron circuits for compartment-specific feedback inhibition

Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. Using simulations and mathematical analyses, we show that the resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models.

The brain contains billions of nerve cells—neurons—that can be classified into different types depending on their shape, connectivity and activity. A particularly diverse group of neurons is that of inhibitory neurons, named after their suppressive effect on neural activity. Presumably, their diverse properties allow inhibitory neurons to fulfil different functions, but what these functions are is currently unclear. In this paper, we investigated if a particular function can explain the existence and properties of the two most common inhibitory cell classes: The need to regulate activity in different physical parts (compartments) of the neurons they target. We investigated this function in a computer model, using optimization to find the neuron properties best-suited for compartment-specific inhibition. Our key result is that after the optimization, model neurons largely fell into two classes that resembled the two types of biological neurons. In particular, the optimized neurons were connected to only one compartment of other neurons. This suggests that the diversity of inhibitory neurons is well suited for compartment-specific inhibition. In the future, our approach of optimizing neural properties might be used to investigate other functions (or dysfunctions) of neuron diversity.

Compartmentalized dendritic plasticity during associative learning

Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits. Activity-dependent plasticity of synaptic input is a major underlying cellular process. Although we have a detailed understanding of synaptic and dendritic plasticity in vitro, little is known about the functional and plastic properties of active dendrites in behaving animals. Using deep brain two-photon Ca²⁺ imaging, we investigated how sensory responses in amygdala principal neurons develop upon classical fear conditioning, a form of associative learning. Fear conditioning induced differential plasticity in dendrites and somas regulated by compartment-specific inhibition. Our results indicate that learning-induced plasticity can be uncoupled between soma and dendrites, reflecting distinct synaptic and microcircuit-level mechanisms that increase the computational capacity of amygdala circuits.

Dynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior

Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N-methyl-d-aspartate (NMDA)-spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

Dendritic branch structure compartmentalizes voltage-dependent calcium influx in cortical layer 2/3 pyramidal cells

Back-propagating action potentials (bAPs) regulate synaptic plasticity by evoking voltage-dependent calcium influx throughout dendrites. Attenuation of bAP amplitude in distal dendritic compartments alters plasticity in a location-specific manner by reducing bAP-dependent calcium influx. However, it is not known if neurons exhibit branch-specific variability in bAP-dependent calcium signals, independent of distance-dependent attenuation. Here, we reveal that bAPs fail to evoke calcium influx through voltage-gated calcium channels (VGCCs) in a specific population of dendritic branches in mouse cortical layer 2/3 pyramidal cells, despite evoking substantial VGCC-mediated calcium influx in sister branches. These branches contain VGCCs and successfully propagate bAPs in the absence of synaptic input; nevertheless, they fail to exhibit bAP-evoked calcium influx due to a branch-specific reduction in bAP amplitude. We demonstrate that these branches have more elaborate branch structure compared to sister branches, which causes a local reduction in electrotonic impedance and bAP amplitude. Finally, we show that bAPs still amplify synaptically-mediated calcium influx in these branches because of differences in the voltage-dependence and kinetics of VGCCs and NMDA-type glutamate receptors. Branch-specific compartmentalization of bAP-dependent calcium signals may provide a mechanism for neurons to diversify synaptic tuning across the dendritic tree.

Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning

The hippocampus plays a critical role in memory consolidation, mediated by coordinated network activity during sharp-wave ripple (SWR) events. Despite the link between SWRs and hippocampal plasticity, little is known about how network state affects information processing in dendrites, the primary sites of synaptic input integration and plasticity. Here, we monitored somatic and basal dendritic activity in CA1 pyramidal cells in behaving mice using longitudinal two-photon calcium imaging integrated with simultaneous local field potential recordings. We found immobility was associated with an increase in dendritic activity concentrated during SWRs. Coincident dendritic and somatic activity during SWRs predicted increased coupling during subsequent exploration of a novel environment. In contrast, somatic-dendritic coupling and SWR recruitment varied with cells' tuning distance to reward location during a goal-learning task. Our results connect SWRs with the stabilization of information processing within CA1 neurons and suggest that these mechanisms may be dynamically biased by behavioral demands.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.

Homeostatic regulation through strengthening of neuronal network-correlated synaptic inputs

Homeostatic regulation is essential for stable neuronal function. Several synaptic mechanisms of homeostatic plasticity have been described, but the functional properties of synapses involved in homeostasis are unknown. We used longitudinal two-photon functional imaging of dendritic spine calcium signals in visual and retrosplenial cortices of awake adult mice to quantify the sensory deprivation-induced changes in the responses of functionally identified spines. We found that spines whose activity selectively correlated with intrinsic network activity underwent tumor necrosis factor alpha (TNF-α)-dependent homeostatic increases in their response amplitudes, but spines identified as responsive to sensory stimulation did not. We observed an increase in the global sensory-evoked responses following sensory deprivation, despite the fact that the identified sensory inputs did not strengthen. Instead, global sensory-evoked responses correlated with the strength of network-correlated inputs. Our results suggest that homeostatic regulation of global responses is mediated through changes to intrinsic network-correlated inputs rather than changes to identified sensory inputs thought to drive sensory processing.

Double-μPeriscope, a tool for multilayer optical recordings, optogenetic stimulations or both

Intelligent behavior and cognitive functions in mammals depend on cortical microcircuits made up of a variety of excitatory and inhibitory cells that form a forest-like complex across six layers. Mechanistic understanding of cortical microcircuits requires both manipulation and monitoring of multiple layers and interactions between them. However, existing techniques are limited as to simultaneous monitoring and stimulation at different depths without damaging a large volume of cortical tissue. Here, we present a relatively simple and versatile method for delivering light to any two cortical layers simultaneously. The method uses a tiny optical probe consisting of two microprisms mounted on a single shaft. We demonstrate the versatility of the probe in three sets of experiments: first, two distinct cortical layers were optogenetically and independently manipulated; second, one layer was stimulated while the activity of another layer was monitored; third, the activity of thalamic axons distributed in two distinct cortical layers was simultaneously monitored in awake mice. Its simple-design, versatility, small-size, and low-cost allow the probe to be applied widely to address important biological questions.

Corticospinal populations broadcast complex motor signals to coordinated spinal and striatal circuits

Many models of motor control emphasize the role of sensorimotor cortex in movement, principally through the projections that corticospinal neurons (CSNs) make to the spinal cord. Additionally, CSNs possess expansive supraspinal axon collaterals, the functional organization of which is largely unknown. Using anatomical and electrophysiological circuit-mapping techniques in the mouse, we reveal dorsolateral striatum as the preeminent target of CSN collateral innervation. We found that this innervation is biased so that CSNs targeting different striatal pathways show biased targeting of spinal cord circuits. Contrary to more conventional perspectives, CSNs encode not only individual movements, but also information related to the onset and offset of motor sequences. Furthermore, similar activity patterns are broadcast by CSN populations targeting different striatal circuits. Our results reveal a logic of coordinated connectivity between forebrain and spinal circuits, where separate CSN modules broadcast similarly complex information to downstream circuits, suggesting that differences in postsynaptic connectivity dictate motor specificity.

Active dendrites enable strong but sparse inputs to determine orientation selectivity

The dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.

Local and Global Dynamics of Dendritic Activity in the Pyramidal Neuron

There has been increasing interest in the measurement and comparison of activity across compartments of the pyramidal neuron. Dendritic activity can occur both locally, on a single dendritic segment, or globally, involving multiple compartments of the single neuron. Little is known about how these dendritic dynamics shape and contribute to information processing and behavior. Although it has been difficult to characterize local and global activity in vivo due to the technical challenge of simultaneously recording from the entire dendritic arbor and soma, the rise of calcium imaging has driven the increased feasibility and interest of these experiments. However, the distinction between local and global activity made by calcium imaging requires careful consideration. In this review we describe local and global activity, discuss the difficulties and caveats of this distinction, and present the evidence of local and global activity in information processing and behavior.

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.

Rethinking Single Neuron Electrical Compartmentalization: Dendritic Contributions to Network Computation In Vivo

Decades of experimental and theoretical work support a now well-established theory that active dendritic processing contributes to the computational power of individual neurons. This theory is based on the high degree of electrical compartmentalization observed in the dendrites of single neurons in ex vivo preparations. Compartmentalization allows dendrites to conduct semi-independent operations on their inputs before final integration and output at the axon, producing a "network-in-a-neuron." However, recent in vivo functional imaging experiments in mouse cortex have reported surprisingly little evidence for strong dendritic compartmentalization. In this review, we contextualize these new findings and discuss their impact on the future of the field. Specifically, we consider how highly coordinated, and thus less compartmentalized, activity in soma and dendrites can contribute to cortical computations including nonlinear mixed selectivity, prediction/expectation, multiplexing, and credit assignment.

QLCA and Entangled States as Single-Neuron Activity Generators

Each neuron in the central nervous system has many dendrites, which provide input information through impulses. Assuming that a neuron's decision to continue or stop firing is made by rules applied to the dendrites' inputs, we associate neuron activity with a quantum like-cellular automaton (QLCA) concepts. Following a previous study that related the CA description with entangled states, we provide a quantum-like description of neuron activity. After reviewing and presenting the entanglement concept expressed by QLCA terminology, we propose a model that relates quantum-like measurement to consciousness. Then, we present a toy model that reviews the QLCA theory, which is adapted to our terminology. The study also focuses on implementing QLCA formalism to describe a single neuron activity.

A cortico-collicular pathway for motor planning in a memory-dependent perceptual decision task

Survival in a dynamic environment requires animals to plan future actions based on past sensory evidence, known as motor planning. However, the neuronal circuits underlying this crucial brain function remain elusive. Here, we employ projection-specific imaging and perturbation methods to investigate the direct pathway linking two key nodes in the motor planning network, the secondary motor cortex (M2) and the midbrain superior colliculus (SC), in mice performing a memory-dependent perceptual decision task. We find dynamic coding of choice information in SC-projecting M2 neurons during motor planning and execution, and disruption of this information by inhibiting M2 terminals in SC selectively impaired decision maintenance. Furthermore, we show that while both excitatory and inhibitory SC neurons receive synaptic inputs from M2, these SC subpopulations display differential temporal patterns in choice coding during behavior. Our results reveal the dynamic recruitment of the premotor-collicular pathway as a circuit mechanism for motor planning.

Duan, Pan et al. find that the premotor cortex cooperates with the midbrain superior colliculus via direct projections to implement decision maintenance. These results reveal mechanisms of cortico-collicular interaction during cognition and action in a pathway- and cell-type-specific manner.

Temporally multiplexed dual-plane imaging of neural activity with four-dimensional precision

Spatiotemporal patterns of neural activity generate brain functions, such as perception, memory, and behavior. Four-dimensional (4-D: x, y, z, t) analyses of such neural activity will facilitate understanding of brain functions. However, conventional two-photon microscope systems observe single-plane brain tissue alone at a time with cellular resolution. It faces a trade-off between the spatial resolution in the x-, y-, and z-axes and the temporal resolution by a limited point-by-point scan speed. To overcome this trade-off in 4-D imaging, we developed a holographic two-photon microscope for dual-plane imaging. A spatial light modulator (SLM) provided an additional focal plane at a different depth. Temporal multiplexing of split lasers with an optical chopper allowed fast imaging of two different focal planes. We simultaneously recorded the activities of neurons on layers 2/3 and 5 of the cerebral cortex in awake mice in vivo. The present study demonstrated the proof-of-concept of dual-plane two-photon imaging of neural circuits by using the temporally multiplexed SLM-based microscope. The temporally multiplexed holographic microscope, combined with in vivo labeling with genetically encoded probes, enabled 4-D imaging and analysis of neural activities at cellular resolution and physiological timescales. Large-scale 4-D imaging and analysis will facilitate studies of not only the nervous system but also of various biological systems.

Perirhinal input to neocortical layer 1 controls learning

Hippocampal output influences memory formation in the neocortex, but this process is poorly understood because the precise anatomical location and the underlying cellular mechanisms remain elusive. Here, we show that perirhinal input, predominantly to sensory cortical layer 1 (L1), controls hippocampal-dependent associative learning in rodents. This process was marked by the emergence of distinct firing responses in defined subpopulations of layer 5 (L5) pyramidal neurons whose tuft dendrites receive perirhinal inputs in L1. Learning correlated with burst firing and the enhancement of dendritic excitability, and it was suppressed by disruption of dendritic activity. Furthermore, bursts, but not regular spike trains, were sufficient to retrieve learned behavior. We conclude that hippocampal information arriving at L5 tuft dendrites in neocortical L1 mediates memory formation in the neocortex.