The decade of the dendritic NMDA spike

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Histamine in the neocortex: Towards integrating multiscale effectors

Histamine is a modulatory neurotransmitter, which has received relatively less attention in the central nervous system than other neurotransmitters. The functional role of histamine in the neocortex, the brain region that controls higher-order cognitive functions such as attention, learning and memory, remains largely unknown. This article focuses on the emerging roles and mechanisms of histamine release in the neocortex. We describe gaps in current knowledge and propose the application of interdisciplinary tools to dissect the detailed multiscale functional logic of histaminergic action in the neocortex ranging from sub-cellular, cellular, dendritic and synaptic levels to microcircuits and mesoscale effects.

Causal evidence for cholinergic stabilization of attractor landscape dynamics

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present "DenRAM", the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM's dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM's ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.

Computational Modeling of Extrasynaptic NMDA Receptors: Insights into Dendritic Signal Amplification Mechanisms

Dendritic structures play a pivotal role in the computational processes occurring within neurons. Signal propagation along dendrites relies on both passive conduction and active processes related to voltage-dependent ion channels. Among these channels, extrasynaptic N-methyl-D-aspartate channels (exNMDA) emerge as a significant contributor. Prior studies have mainly concentrated on interactions between synapses and nearby exNMDA (100 nm-10 µm from synapse), activated by presynaptic membrane glutamate. This study concentrates on the correlation between synaptic inputs and distal exNMDA (>100 µm), organized in clusters that function as signal amplifiers. Employing a computational model of a dendrite, we elucidate the mechanism underlying signal amplification in exNMDA clusters. Our findings underscore the pivotal role of the optimal spatial positioning of the NMDA cluster in determining signal amplification efficiency. Additionally, we demonstrate that exNMDA subunits characterized by a large conduction decay constant. Specifically, NR2B subunits exhibit enhanced effectiveness in signal amplification compared to subunits with steeper conduction decay. This investigation extends our understanding of dendritic computational processes by emphasizing the significance of distant exNMDA clusters as potent signal amplifiers. The implications of our computational model shed light on the spatial considerations and subunit characteristics that govern the efficiency of signal amplification in dendritic structures, offering valuable insights for future studies in neurobiology and computational neuroscience.

Iron imbalance in neurodegeneration

Iron is an essential element for the development and functionality of the brain, and anomalies in its distribution and concentration in brain tissue have been found to be associated with the most frequent neurodegenerative diseases. When magnetic resonance techniques allowed iron quantification in vivo, it was confirmed that the alteration of brain iron homeostasis is a common feature of many neurodegenerative diseases. However, whether iron is the main actor in the neurodegenerative process, or its alteration is a consequence of the degenerative process is still an open question. Because the different iron-related pathogenic mechanisms are specific for distinctive diseases, identifying the molecular mechanisms common to the various pathologies could represent a way to clarify this complex topic. Indeed, both iron overload and iron deficiency have profound consequences on cellular functioning, and both contribute to neuronal death processes in different manners, such as promoting oxidative damage, a loss of membrane integrity, a loss of proteostasis, and mitochondrial dysfunction. In this review, with the attempt to elucidate the consequences of iron dyshomeostasis for brain health, we summarize the main pathological molecular mechanisms that couple iron and neuronal death.

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.

Layer 1 neocortex: Gating and integrating multidimensional signals

Layer 1 (L1) of the neocortex acts as a nexus for the collection and processing of widespread information. By integrating ascending inputs with extensive top-down activity, this layer likely provides critical information regulating how the perception of sensory inputs is reconciled with expectation. This is accomplished by sorting, directing, and integrating the complex network of excitatory inputs that converge onto L1. These signals are combined with neuromodulatory afferents and gated by the wealth of inhibitory interneurons that either are embedded within L1 or send axons from other cortical layers. Together, these interactions dynamically calibrate information flow throughout the neocortex. This review will primarily focus on L1 within the primary sensory cortex and will use these insights to understand L1 in other cortical areas.

Physiological features of parvalbumin-expressing GABAergic interneurons contributing to high-frequency oscillations in the cerebral cortex

Parvalbumin-expressing (PV+) inhibitory interneurons drive gamma oscillations (30-80 Hz), which underlie higher cognitive functions. In this review, we discuss two groups/aspects of fundamental properties of PV+ interneurons. In the first group (dubbed Before Axon), we list properties representing optimal synaptic integration in PV+ interneurons designed to support fast oscillations. For example: [i] Information can neither enter nor leave the neocortex without the engagement of fast PV+ -mediated inhibition; [ii] Voltage responses in PV+ interneuron dendrites integrate linearly to reduce impact of the fluctuations in the afferent drive; and [iii] Reversed somatodendritic Rm gradient accelerates the time courses of synaptic potentials arriving at the soma. In the second group (dubbed After Axon), we list morphological and biophysical properties responsible for (a) short synaptic delays, and (b) efficient postsynaptic outcomes. For example: [i] Fast-spiking ability that allows PV+ interneurons to outpace other cortical neurons (pyramidal neurons). [ii] Myelinated axon (which is only found in the PV+ subclass of interneurons) to secure fast-spiking at the initial axon segment; and [iii] Inhibitory autapses - autoinhibition, which assures brief biphasic voltage transients and supports postinhibitory rebounds. Recent advent of scientific tools, such as viral strategies to target PV cells and the ability to monitor PV cells via in vivo imaging during behavior, will aid in defining the role of PV cells in the CNS. Given the link between PV+ interneurons and cognition, in the future, it would be useful to carry out physiological recordings in the PV+ cell type selectively and characterize if and how psychiatric and neurological diseases affect initiation and propagation of electrical signals in this cortical sub-circuit. Voltage imaging may allow fast recordings of electrical signals from many PV+ interneurons simultaneously.

BK channels sustain neuronal Ca2+ oscillations to support hippocampal long-term potentiation and memory formation

Mutations of large conductance Ca2+- and voltage-activated K+ channels (BK) are associated with cognitive impairment. Here we report that CA1 pyramidal neuron-specific conditional BK knock-out (cKO) mice display normal locomotor and anxiety behavior. They do, however, exhibit impaired memory acquisition and retrieval in the Morris Water Maze (MWM) when compared to littermate controls (CTRL). In line with cognitive impairment in vivo, electrical and chemical long-term potentiation (LTP) in cKO brain slices were impaired in vitro. We further used a genetically encoded fluorescent K+ biosensor and a Ca2+-sensitive probe to observe cultured hippocampal neurons during chemical LTP (cLTP) induction. cLTP massively reduced intracellular K+ concentration ([K+]i) while elevating L-Type Ca2+ channel- and NMDA receptor-dependent Ca2+ oscillation frequencies. Both, [K+]i decrease and Ca2+ oscillation frequency increase were absent after pharmacological BK inhibition or in cells lacking BK. Our data suggest that L-Type- and NMDAR-dependent BK-mediated K+ outflow significantly contributes to hippocampal LTP, as well as learning and memory.

Optogenetic stimulation shapes dendritic trees of infragranular cortical pyramidal cells

Spontaneous or experimentally evoked activity can lead to changes in length and/or branching of neocortical pyramidal cell dendrites. For instance, an early postnatal overexpression of certain AMPA or kainate glutamate receptor subunits leads to larger amplitudes of depolarizing events driven by spontaneous activity, and this increases apical dendritic complexity. Whether stimulation frequency has a role is less clear. In this study, we report that the expression of channelrhodopsin2-eYFP was followed by a 5-day optogenetic stimulation from DIV 5-10 or 11-15 in organotypic cultures of rat visual cortex-evoked dendritic remodeling. Stimulation at 0.05 Hz, at a frequency range of spontaneous calcium oscillations known to occur in the early postnatal neocortex in vivo until eye opening, had no effect. Stimulation with 0.5 Hz, a frequency at which the cortex in vivo adopts after eye opening, unexpectedly caused shorter and somewhat less branched apical dendrites of infragranular pyramidal neurons. The outcome resembles the remodeling of corticothalamic and callosal projection neurons of layers VI and V, which in the adult have apical dendrites no longer terminating in layer I. Exposure to 2.5 Hz, a frequency not occurring naturally during the time windows, evoked dendritic damage. The results suggested that optogenetic stimulation at a biologically meaningful frequency for the selected developmental stage can influence dendrite growth, but contrary to expectation, the optogenetic stimulation decreased dendritic growth.

Synapse-specific diversity of distinct postsynaptic GluN2 subtypes defines transmission strength in spinal lamina I

The unitary postsynaptic response to presynaptic quantal glutamate release is the fundamental basis of excitatory information transfer between neurons. The view, however, of individual glutamatergic synaptic connections in a population as homogenous, fixed-strength units of neural communication is becoming increasingly scrutinized. Here, we used minimal stimulation of individual glutamatergic afferent axons to evoke single synapse resolution postsynaptic responses from central sensory lamina I neurons in an ex vivo adult rat spinal slice preparation. We detected unitary events exhibiting a NMDA receptor component with distinct kinetic properties across synapses conferred by specific GluN2 subunit composition, indicative of GluN2 subtype-based postsynaptic heterogeneity. GluN2A, 2A and 2B, or 2B and 2D synaptic predominance functioned on distinct lamina I neuron types to narrowly, intermediately, or widely tune, respectively, the duration of evoked unitary depolarization events from resting membrane potential, which enabled individual synapses to grade differentially depolarizing steps during temporally patterned afferent input. Our results lead to a model wherein a core locus of proteomic complexity prevails at this central glutamatergic sensory synapse that involves distinct GluN2 subtype configurations. These findings have major implications for subthreshold integrative capacity and transmission strength in spinal lamina I and other CNS regions.

Glutamate spillover drives robust all-or-none dendritic plateau potentials-an in silico investigation using models of striatal projection neurons

Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg²⁺ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg²⁺ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various recall strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning.

Growing dendrites enhance a neuron's computational power and memory capacity

Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, homeostatic, Hebbian developmental theory. Here we investigate the neurocomputational advantages and limits on this novel algorithm that combines dendritogenesis with supervised adaptive synaptogenesis. Neurons created with this algorithm have enhanced memory capacity, can avoid catastrophic interference (forgetting), and have the ability to unmix mixture distributions. In particular, individual dendrites develop within each class, in an unsupervised manner, to become feature-clusters that correspond to the mixing elements of class-conditional mixture distribution. Error-free classification is demonstrated with input perturbations up to 40%. Although discriminative problems are used to understand the capabilities of the stochastic algorithm and the neuronal connectivity it produces, the algorithm is in the generative class, it thus seems ideal for decisions that require generalization, i.e., extrapolation beyond previous learning.

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

INTRODUCTION

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

METHODS

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

RESULTS

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

DISCUSSION

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Essentially all data processed by mammals and many other living organisms is sequential. This holds true for all types of sensory input data as well as motor output activity. Being able to form memories of such sequential data, to predict future sequence elements, and to replay learned sequences is a necessary prerequisite for survival. It has been hypothesized that sequence learning, prediction and replay constitute the fundamental computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) constitutes an abstract powerful algorithm implementing this form of computation and has been proposed to serve as a model of neocortical processing. In this study, we are reformulating this algorithm in terms of known biological ingredients and mechanisms to foster the verifiability of the HTM hypothesis based on electrophysiological and behavioral data. The proposed model learns continuously in an unsupervised manner by biologically plausible, local plasticity mechanisms, and successfully predicts and replays complex sequences. Apart from establishing contact to biology, the study sheds light on the mechanisms determining at what speed we can process sequences and provides an explanation of fast sequence replay observed in the hippocampus and in the neocortex.

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.

Do Biological Constraints Impair Dendritic Computation?

Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these biological constraints. Here we simulate models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows: first, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results in both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.

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.

Common synaptic phenotypes arising from diverse mutations in the human NMDA receptor subunit GluN2A

Dominant mutations in the human gene GRIN2A, encoding NMDA receptor (NMDAR) subunit GluN2A, make a significant and growing contribution to the catalogue of published single-gene epilepsies. Understanding the disease mechanism in these epilepsy patients is complicated by the surprising diversity of effects that the mutations have on NMDARs. Here we have examined the cell-autonomous effect of five GluN2A mutations, 3 loss-of-function and 2 gain-of-function, on evoked NMDAR-mediated synaptic currents (NMDA-EPSCs) in CA1 pyramidal neurons in cultured hippocampal slices. Despite the mutants differing in their functional incorporation at synapses, prolonged NMDA-EPSC current decays (with only marginal changes in charge transfer) were a common effect for both gain- and loss-of-function mutants. Modelling NMDA-EPSCs with mutant properties in a CA1 neuron revealed that the effect of GRIN2A mutations can lead to abnormal temporal integration and spine calcium dynamics during trains of concerted synaptic activity. Investigations beyond establishing the molecular defects of GluN2A mutants are much needed to understand their impact on synaptic transmission.

The cell-autonomous effect of five severe loss- or gain-of-function GluN2A (NMDA receptor) mutations is assessed on evoked NMDAR-mediated synaptic currents in CA1 pyramidal neurons in cultured mouse hippocampal slices. Data and modelling suggest that mutant-like NMDA-EPSCs can lead to abnormal temporal summation and spine calcium dynamics.

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.

Input rate encoding and gain control in dendrites of neocortical pyramidal neurons

Elucidating how neurons encode network activity is essential to understanding how the brain processes information. Neocortical pyramidal cells receive excitatory input onto spines distributed along dendritic branches. Local dendritic branch nonlinearities can boost the response to spatially clustered and synchronous input, but how this translates into the integration of patterns of ongoing activity remains unclear. To examine dendritic integration under naturalistic stimulus regimes, we use two-photon glutamate uncaging to repeatedly activate multiple dendritic spines at random intervals. In the proximal dendrites of two populations of layer 5 pyramidal neurons in the mouse motor cortex, spatially restricted synchrony is not a prerequisite for dendritic boosting. Branches encode afferent inputs with distinct rate sensitivities depending upon cell and branch type. Thus, inputs distributed along a dendritic branch can recruit supralinear boosting and the window of this nonlinearity may provide a mechanism by which dendrites can preferentially amplify slow-frequency network oscillations.

Optimizing a Neuron for Reliable Dendritic Subunit Pooling

In certain biologically relevant computing scenarios, a neuron "pools" the outputs of multiple independent functional subunits, firing if any one of them crosses threshold. Recent studies suggest that active dendrites could provide the thresholding mechanism, so that both the thresholding and pooling operations could take place within a single neuron. A pooling neuron faces a difficult task, however. Dendrites can produce highly variable responses depending on the density and spatial patterning of their synaptic inputs, and bona fide dendritic firing may be very rare, making it difficult for a neuron to reliably detect when one of its many dendrites has "gone suprathreshold". Our goal has been to identify biological adaptations that optimize a neuron's performance at the binary subunit pooling (BSP) task. Katz et al. (2009) pointed to the importance of spine density gradients in shaping dendritic responses. In a similar vein, we used a compartmental model to study how a neuron's performance at the BSP task is affected by different spine density layouts and other biological variables. We found BSP performance was optimized when dendrites have (1) a decreasing spine density gradient (true for many types of pyramidal neurons); (2) low-to-medium resistance spine necks; (3) strong NMDA currents; (4) fast spiking Na+ channels; and (5) powerful hyperpolarizing inhibition. Our findings provide a normative account that links several neuronal properties within the context of a behaviorally relevant task, and thus provide new insights into nature's subtle strategies for optimizing the computing capabilities of neural tissue.

Plasticity of olfactory bulb inputs mediated by dendritic NMDA-spikes in rodent piriform cortex

The piriform cortex (PCx) is essential for learning of odor information. The current view postulates that odor learning in the PCx is mainly due to plasticity in intracortical (IC) synapses, while odor information from the olfactory bulb carried via the lateral olfactory tract (LOT) is ‘hardwired.’ Here, we revisit this notion by studying location- and pathway-dependent plasticity rules. We find that in contrast to the prevailing view, synaptic and optogenetically activated LOT synapses undergo strong and robust long-term potentiation (LTP) mediated by only a few local NMDA-spikes delivered at theta frequency, while global spike timing-dependent plasticity (STDP) protocols failed to induce LTP in these distal synapses. In contrast, IC synapses in apical and basal dendrites undergo plasticity with both NMDA-spikes and STDP protocols but to a smaller extent compared with LOT synapses. These results are consistent with a self-potentiating mechanism of odor information via NMDA-spikes that can form branch-specific memory traces of odors that can further associate with contextual IC information via STDP mechanisms to provide cognitive and emotional value to odors.

Dendritic Branch-constrained N-Methyl-d-Aspartate Receptor-mediated Spikes Drive Synaptic Plasticity in Hippocampal CA3 Pyramidal Cells

N-methyl-d-aspartate receptor-mediated ( spikes can be causally linked to the induction of synaptic long-term potentiation (LTP) in hippocampal and cortical pyramidal cells. However, it is unclear if they regulate plasticity at a local or global scale in the dendritic tree. Here, we used dendritic patch-clamp recordings and calcium imaging to investigate the integrative properties of single dendrites of hippocampal CA3 cells. We show that local hyperpolarization of a single dendritic segment prevents NMDA spikes, their associated calcium transients, as well as LTP in a branch-specific manner. This result provides direct, causal evidence that the single dendritic branch can operate as a functional unit in regulating CA3 pyramidal cell plasticity.

Noise-induced properties of active dendrites

Dendrites play an essential role in the integration of highly fluctuating input in vivo into neurons across all nervous systems. Yet, they are often studied under conditions where inputs to dendrites are sparse. The dynamic properties of active dendrites facing in vivo-like fluctuating input thus remain elusive. In this paper, we uncover dynamics in a canonical model of a dendritic compartment with active calcium channels, receiving in vivo-like fluctuating input. In a single-compartment model of the active dendrite with fast calcium activation, we show noise-induced nonmonotonic behavior in the relationship of the membrane potential output, and mean input emerges. In contrast, noise can induce bistability in the input-output relation in the system with slowly activating calcium channels. Both phenomena are absent in a noiseless condition. Furthermore, we show that timescales of the emerging stochastic bistable dynamics extend far beyond a deterministic system due to stochastic switching between the solutions. A numerical simulation of a multicompartment model neuron shows that in the presence of in vivo-like synaptic input, the bistability uncovered in our analysis persists. Our results reveal that realistic synaptic input contributes to sustained dendritic nonlinearities, and synaptic noise is a significant component of dendritic input integration.

An increase in dendritic plateau potentials is associated with experience-dependent cortical map reorganization

Here we describe a mechanism for cortical map plasticity. Classically, representational map changes are thought to be driven by changes within cortico-cortical circuits, e.g., Hebbian plasticity of synaptic circuits that lost vs. maintained an excitatory drive from the first-order thalamus, possibly steered by neuromodulatory forces from deep brain regions. Our work provides evidence for an additional gating mechanism, provided by plateau potentials, which are driven by higher-order thalamic feedback. Higher-order thalamic neurons are characterized by broad receptive fields, and the plateau potentials that they evoke strongly facilitate long-term potentiation and elicit spikes. We show that these features combined constitute a powerful driving force for the fusion or expansion of sensory representations within cortical maps.

The organization of sensory maps in the cerebral cortex depends on experience, which drives homeostatic and long-term synaptic plasticity of cortico-cortical circuits. In the mouse primary somatosensory cortex (S1) afferents from the higher-order, posterior medial thalamic nucleus (POm) gate synaptic plasticity in layer (L) 2/3 pyramidal neurons via disinhibition and the production of dendritic plateau potentials. Here we address whether these thalamocortically mediated responses play a role in whisker map plasticity in S1. We find that trimming all but two whiskers causes a partial fusion of the representations of the two spared whiskers, concomitantly with an increase in the occurrence of POm-driven N-methyl-D-aspartate receptor-dependent plateau potentials. Blocking the plateau potentials restores the archetypical organization of the sensory map. Our results reveal a mechanism for experience-dependent cortical map plasticity in which higher-order thalamocortically mediated plateau potentials facilitate the fusion of normally segregated cortical representations.

Multibranch Formal Neuron: An Internally Nonlinear Learning Unit

The transformation of synaptic input into action potential in nerve cells is strongly influenced by the morphology of the dendritic arbor as well as the synaptic efficacy map. The multiplicity of dendritic branches strikingly enables a single cell to act as a highly nonlinear processing element. Studies have also found functional synaptic clustering whereby synapses that encode a common sensory feature are spatially clustered together on the branches. Motivated by these findings, here we introduce a multibranch formal model of the neuron that can integrate synaptic inputs nonlinearly through collective action of its dendritic branches and yields synaptic clustering. An analysis in support of its use as a computational building block is offered. Also offered is an accompanying gradient descent-based learning algorithm. The model unit spans a wide spectrum of nonlinearities, including the parity problem, and can outperform the multilayer perceptron in generalizing to unseen data. The occurrence of synaptic clustering boosts the generalization efficiency of the unit, which may also be the answer for the puzzling ubiquity of synaptic clustering in the real neurons. Our theoretical analysis is backed up by simulations. The study could pave the way to new artificial neural networks.

Neurophysiological basis of the N400 deflection, from Mismatch Negativity to Semantic Prediction Potentials and late positive components

The first theoretical model on the neurophysiological basis of the N400: the deflection reflects layer I dendritic plateaus on a preparatory state of synaptic integration that precedes layer V somatic burst firing for conscious identification of the higher-order features of the stimulus (a late positive shift). Plateaus ensue from apical disinhibition by vasoactive intestinal polypeptide-positive interneurons (VIPs) through suppression of Martinotti cells, opening the gates for glutamatergic feedback to trigger dendritic regenerative potentials. Cholinergic transients contribute to these dynamics directly, holding a central role in the N400 deflection. The stereotypical timing of the (frontal) glutamatergic feedback and the accompanying cholinergic transients account for the enigmatic "invariability" of the peak latency in the face of a gamut of different stimuli and paradigms. The theoretical postulations presented here may bring about unprecedented level of detail for the N400 deflection to be used in the study of schizophrenia, Alzheimer's disease and other higher-order pathologies. The substrates of a late positive component, the Mismatch Negativity and the Semantic Prediction Potentials are also surveyed.

Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity

As our understanding of the thalamocortical system deepens, the questions we face become more complex. Their investigation requires the adoption of novel experimental approaches complemented with increasingly sophisticated computational modeling. In this review, we take stock of current data and knowledge about the circuitry of the somatosensory thalamocortical loop in rodents, discussing common principles across modalities and species whenever appropriate. We review the different levels of organization, including the cells, synapses, neuroanatomy, and network connectivity. We provide a complete overview of this system that should be accessible for newcomers to this field while nevertheless being comprehensive enough to serve as a reference for seasoned neuroscientists and computational modelers studying the thalamocortical system. We further highlight key gaps in data and knowledge that constitute pressing targets for future experimental work. Filling these gaps would provide invaluable information for systematically unveiling how this system supports behavioral and cognitive processes.

The Theta Rhythm of the Hippocampus: From Neuronal and Circuit Mechanisms to Behavior

This review focuses on the neuronal and circuit mechanisms involved in the generation of the theta (θ) rhythm and of its participation in behavior. Data have accumulated indicating that θ arises from interactions between medial septum-diagonal band of Broca (MS-DbB) and intra-hippocampal circuits. The intrinsic properties of MS-DbB and hippocampal neurons have also been shown to play a key role in θ generation. A growing number of studies suggest that θ may represent a timing mechanism to temporally organize movement sequences, memory encoding, or planned trajectories for spatial navigation. To accomplish those functions, θ and gamma (γ) oscillations interact during the awake state and REM sleep, which are considered to be critical for learning and memory processes. Further, we discuss that the loss of this interaction is at the base of various neurophatological conditions.

Freeze-Frame Imaging of Dendritic Calcium Signals With TubuTag

The extensive dendritic arbor of neurons is thought to be actively involved in the processing of information. Dendrites contain a rich diversity of ligand- and voltage-activated ion channels as well as metabotropic receptors. In addition, they are capable of releasing calcium from intracellular stores. Under specific conditions, large neurons produce calcium spikes that are locally restricted to a dendritic section. To investigate calcium signaling in dendrites, we introduce TubuTag, a genetically encoded ratiometric calcium sensor anchored to the cytoskeleton. TubuTag integrates cytoplasmic calcium signals by irreversible photoconversion from green to red fluorescence when illuminated with violet light. We used a custom two-photon microscope with a large field of view to image pyramidal neurons in CA1 at subcellular resolution. Photoconversion was strongest in the most distal parts of the apical dendrite, suggesting a gradient in the amplitude of dendritic calcium signals. As the read-out of fluorescence can be performed several hours after photoconversion, TubuTag will help investigating dendritic signal integration and calcium homeostasis in large populations of neurons.

NMDA Receptors Enhance the Fidelity of Synaptic Integration

Excitatory synaptic transmission in many neurons is mediated by two coexpressed ionotropic glutamate receptor subtypes, AMPA and NMDA receptors, that differ in kinetics, ion selectivity, and voltage-sensitivity. AMPA receptors have fast kinetics and are voltage-insensitive, while NMDA receptors have slower kinetics and increased conductance at depolarized membrane potentials. Here, we report that the voltage dependency and kinetics of NMDA receptors act synergistically to stabilize synaptic integration of EPSPs across spatial and voltage domains. Simulations of synaptic integration in simplified and morphologically realistic dendritic trees revealed that the combined presence of AMPA and NMDA conductances reduce the variability of somatic responses to spatiotemporal patterns of excitatory synaptic input presented at different initial membrane potentials and/or in different dendritic domains. This moderating effect of the NMDA conductance on synaptic integration was robust across a wide range of AMPA-to-NMDA ratios, and results from synergistic interaction of NMDA kinetics (which reduces variability across membrane potential) and voltage dependence (which favors stabilization across dendritic location). When combined with AMPA conductance, the NMDA conductance compensates for voltage-dependent and impedance-dependent changes in synaptic driving force, and distance-dependent attenuation of synaptic potentials arriving at the axon, to increase the fidelity of synaptic integration and EPSP-spike coupling across both neuron state (i.e., initial membrane potential) and dendritic location of synaptic input. Thus, synaptic NMDA receptors convey advantages for synaptic integration that are independent of, but fully compatible with, their importance for coincidence detection and synaptic plasticity.

The integration of Gaussian noise by long-range amygdala inputs in frontal circuit promotes fear learning in mice

Survival depends on the ability of animals to select the appropriate behavior in response to threat and safety sensory cues. However, the synaptic and circuit mechanisms by which the brain learns to encode accurate predictors of threat and safety remain largely unexplored. Here, we show that frontal association cortex (FrA) pyramidal neurons of mice integrate auditory cues and basolateral amygdala (BLA) inputs non-linearly in a NMDAR-dependent manner. We found that the response of FrA pyramidal neurons was more pronounced to Gaussian noise than to pure frequency tones, and that the activation of BLA-to-FrA axons was the strongest in between conditioning pairings. Blocking BLA-to-FrA signaling specifically at the time of presentation of Gaussian noise (but not 8 kHz tone) between conditioning trials impaired the formation of auditory fear memories. Taken together, our data reveal a circuit mechanism that facilitates the formation of fear traces in the FrA, thus providing a new framework for probing discriminative learning and related disorders.

GluN2B but Not GluN2A for Basal Dendritic Growth of Cortical Pyramidal Neurons

NMDA receptors are important players for neuronal differentiation. We previously reported that antagonizing NMDA receptors with APV blocked the growth-promoting effects evoked by the overexpression of specific calcium-permeable or flip-spliced AMPA receptor subunits and of type I transmembrane AMPA receptor regulatory proteins which both exclusively modify apical dendritic length and branching of cortical pyramidal neurons. These findings led us to characterize the role of GluN2B and GluN2A for dendritogenesis using organotypic cultures of rat visual cortex. Antagonizing GluN2B with ifenprodil and Ro25-6981 strongly impaired basal dendritic growth of supra- and infragranular pyramidal cells at DIV 5–10, but no longer at DIV 15–20. Growth recovered after washout, and protein blots revealed an increase of synaptic GluN2B-containing receptors as indicated by a enhanced phosphorylation of the tyrosine 1472 residue. Antagonizing GluN2A with TCN201 and NVP-AAM077 was ineffective at both ages. Dendrite growth of non-pyramidal interneurons was not altered. We attempted to overexpress GluN2A and GluN2B. However, although the constructs delivered currents in HEK cells, there were neither effects on dendrite morphology nor an enhanced sensitivity to NMDA. Further, co-expressing GluN1-1a and GluN2B did not alter dendritic growth. Visualization of overexpressed, tagged GluN2 proteins was successful after immunofluorescence for the tag which delivered rather weak staining in HEK cells as well as in neurons. This suggested that the level of overexpression is too weak to modify dendrite growth. In summary, endogenous GluN2B, but not GluN2A is important for pyramidal cell basal dendritic growth during an early postnatal time window.

Power-efficient neural network with artificial dendrites

In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy efficiency and the ability to handle complex tasks. Here, by developing artificial dendrites, we experimentally demonstrate a complete neural network fully integrated with synapses, dendrites and soma, implemented using scalable memristor devices. We perform a digit recognition task and simulate a multilayer network using experimentally derived device characteristics. The power consumption is more than three orders of magnitude lower than that of a central processing unit and 70 times lower than that of a typical application-specific integrated circuit chip. This network, equipped with functional dendrites, shows the potential of substantial overall performance improvement, for example by extracting critical information from a noisy background with significantly reduced power consumption and enhanced accuracy.

Dendritic integration in olfactory bulb granule cells upon simultaneous multispine activation: Low thresholds for nonlocal spiking activity

The inhibitory axonless olfactory bulb granule cells form reciprocal dendrodendritic synapses with mitral and tufted cells via large spines, mediating recurrent and lateral inhibition. As a case in point for dendritic transmitter release, rat granule cell dendrites are highly excitable, featuring local Na+ spine spikes and global Ca2+- and Na+-spikes. To investigate the transition from local to global signaling, we performed holographic, simultaneous 2-photon uncaging of glutamate at up to 12 granule cell spines, along with whole-cell recording and dendritic 2-photon Ca2+ imaging in acute juvenile rat brain slices. Coactivation of less than 10 reciprocal spines was sufficient to generate diverse regenerative signals that included regional dendritic Ca2+-spikes and dendritic Na+-spikes (D-spikes). Global Na+-spikes could be triggered in one third of granule cells. Individual spines and dendritic segments sensed the respective signal transitions as increments in Ca2+ entry. Dendritic integration as monitored by the somatic membrane potential was mostly linear until a threshold number of spines was activated, at which often D-spikes along with supralinear summation set in. As to the mechanisms supporting active integration, NMDA receptors (NMDARs) strongly contributed to all aspects of supralinearity, followed by dendritic voltage-gated Na+- and Ca2+-channels, whereas local Na+ spine spikes, as well as morphological variables, barely mattered. Because of the low numbers of coactive spines required to trigger dendritic Ca2+ signals and thus possibly lateral release of GABA onto mitral and tufted cells, we predict that thresholds for granule cell-mediated bulbar lateral inhibition are low. Moreover, D-spikes could provide a plausible substrate for granule cell-mediated gamma oscillations.

Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

The connectivity structure of neuronal networks in cortex is highly dynamic. This ongoing cortical rewiring is assumed to serve important functions for learning and memory. We analyze in this article a model for the self-organization of synaptic inputs onto dendritic branches of pyramidal cells. The model combines a generic stochastic rewiring principle with a simple synaptic plasticity rule that depends on local dendritic activity. In computer simulations, we find that this synaptic rewiring model leads to synaptic clustering, that is, temporally correlated inputs become locally clustered on dendritic branches. This empirical finding is backed up by a theoretical analysis which shows that rewiring in our model favors network configurations with synaptic clustering. We propose that synaptic clustering plays an important role in the organization of computation and memory in cortical circuits: we find that synaptic clustering through the proposed rewiring mechanism can serve as a mechanism to protect memories from subsequent modifications on a medium time scale. Rewiring of synaptic connections onto specific dendritic branches may thus counteract the general problem of catastrophic forgetting in neural networks.

An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex

The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits ("macrocolumns"), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

Experience and sleep-dependent synaptic plasticity: from structure to activity

Synaptic plasticity is important for learning and memory. With increasing evidence linking sleep states to changes in synaptic strength, an emerging view is that sleep promotes learning and memory by facilitating experience-induced synaptic plasticity. In this review, we summarize the recent progress on the function of sleep in regulating cortical synaptic plasticity. Specifically, we outline the electroencephalogram signatures of sleep states (e.g. slow-wave sleep, rapid eye movement sleep, spindles), sleep state-dependent changes in gene and synaptic protein expression, synaptic morphology, and neuronal and network activity. We highlight studies showing that post-experience sleep potentiates experience-induced synaptic changes and discuss the potential mechanisms that may link sleep-related brain activity to synaptic structural remodelling. We conclude that both synapse formation or strengthening and elimination or weakening occur across sleep. This sleep-dependent synaptic plasticity plays an important role in neuronal circuit refinement during development and after learning, while sleep disorders may contribute to or exacerbate the development of common neurological diseases. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Functional clustering of dendritic activity during decision-making

The active properties of dendrites can support local nonlinear operations, but previous imaging and electrophysiological measurements have produced conflicting views regarding the prevalence and selectivity of local nonlinearities in vivo. We imaged calcium signals in pyramidal cell dendrites in the motor cortex of mice performing a tactile decision task. A custom microscope allowed us to image the soma and up to 300 μm of contiguous dendrite at 15 Hz, while resolving individual spines. New analysis methods were used to estimate the frequency and spatial scales of activity in dendritic branches and spines. The majority of dendritic calcium transients were coincident with global events. However, task-associated calcium signals in dendrites and spines were compartmentalized by dendritic branching and clustered within branches over approximately 10 μm. Diverse behavior-related signals were intermingled and distributed throughout the dendritic arbor, potentially supporting a large learning capacity in individual neurons.

From cells to sensations: A window to the physics of mind

Principles of methods for studying particles and fields that cannot be sensed by third-person observers by routine methods can be used to understand the physics of first-person properties of mind. Accordingly, whenever a system exhibits disparate features at multiple levels, unique combination of constraints offered by them direct us towards a solution that will be the first principle of that system. Using this method, it was possible to arrive at a third-person observable solution-point of brain-mind interface. Examination of this location identified a set of unique features that can allow an associatively learned (cue) stimulus to spark hallucinations that form units of first-person internal (inner) sensations reminiscent of stimuli from the associatively learned second item in timescales of milliseconds. It allows us to peep into a virtual space of mind where different modifications and integrations of units of internal sensations generate their different net conformations ranging from perception to an inner sense of hidden relationships that form a hypothesis. Since sparking of inner sensations of the late arriving (when far away) or non-arriving (when hidden) features of items started providing survival advantage, the focus of evolution might have been to optimize this property. Hence, the circuity that generates it can be considered as the primary circuitry of the system. The solution provides several testable predictions. By taking readers through the process of deriving the solution and by explaining how it interconnects disparate findings, it is hoped that the factors determining the physics of mind will become evident.

Can NMDA Spikes Dictate Computations of Local Networks and Behavior?

Intelligence is the ability to learn appropriate responses to stimuli and the capacity to master new skills. Synaptic integration at the dendritic level is thought to be essential for this ability through linear and non-linear processing, by allowing neurons to be tuned to relevant information and to maximize adaptive behavior. Showing that dendrites are able to generate local computations that influence how animals perceive the world, form a new memory or learn a new skill was a break-through in neuroscience, since in the past they were seen as passive elements of the neurons, just funneling information to the soma. Here, we provide an overview of the role of dendritic integration in improving the neuronal network and behavioral performance. We focus on how NMDA spikes are generated and their role in neuronal computation for optimal behavioral output based on recent in vivo studies on rodents.