Synaptic plasticity in dendrites: complications and coping strategies

Sensory encoding and memory in the mushroom body: signals, noise, and variability

To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.

A biochemical description of postsynaptic plasticity-with timescales ranging from milliseconds to seconds

Synaptic plasticity [long-term potentiation/depression (LTP/D)], is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally-spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time scale synaptic plasticity (BTSP), on time scales of seconds. BTSP is a candidate for a mechanism underlying rapid learning of spatial location by place cells. Here, a computational model of the induction of E-LTP/D at a spine head of a synapse of a hippocampal pyramidal neuron is developed. The single-compartment model represents two interacting biochemical pathways for the activation (phosphorylation) of the kinase (CaMKII) with a phosphatase, with ion inflow through channels (NMDAR, CaV1,Na). The biochemical reactions are represented by a deterministic system of differential equations, with a detailed description of the activation of CaMKII that includes the opening of the compact state of CaMKII. This single model captures realistic responses (temporal profiles with the differing timescales) of STDP and BTSP and their asymmetries. The simulations distinguish several mechanisms underlying STDP vs. BTSP, including i) the flow of [Formula: see text] through NMDAR vs. CaV1 channels, and ii) the origin of several time scales in the activation of CaMKII. The model also realizes a priming mechanism for E-LTP that is induced by [Formula: see text] flow through CaV1.3 channels. Once in the spine head, this small additional [Formula: see text] opens the compact state of CaMKII, placing CaMKII ready for subsequent induction of LTP.

Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Foundation's (NSF) Emerging Frontiers and Multidisciplinary Activities (EFMA) office of the Directorate for Engineering (ENG) launched an Emerging Frontiers in Research and Innovation (EFRI) topic for the fiscal years FY22 and FY23 entitled "Brain-inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence" (BRAID) [...].

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.

Commonly Overlooked Factors in Biocompatibility Studies of Neural Implants

Biocompatibility of cutting-edge neural implants, surgical tools and techniques, and therapeutic technologies is a challenging concept that can be easily misjudged. For example, neural interfaces are routinely gauged on how effectively they determine active neurons near their recording sites. Tissue integration and toxicity of neural interfaces are frequently assessed histologically in animal models to determine tissue morphological and cellular changes in response to surgical implantation and chronic presence. A disconnect between histological and efficacious biocompatibility exists, however, as neuronal numbers frequently observed near electrodes do not match recorded neuronal spiking activity. The downstream effects of the myriad surgical and experimental factors involved in such studies are rarely examined when deciding whether a technology or surgical process is biocompatible. Such surgical factors as anesthesia, temperature excursions, bleed incidence, mechanical forces generated, and metabolic conditions are known to have strong systemic and thus local cellular and extracellular consequences. Many tissue markers are extremely sensitive to the physiological state of cells and tissues, thus significantly impacting histological accuracy. This review aims to shed light on commonly overlooked factors that can have a strong impact on the assessment of neural biocompatibility and to address the mismatch between results stemming from functional and histological methods.

Precise Spiking Motifs in Neurobiological and Neuromorphic Data

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other, can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption-a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

A computational model to explore how temporal stimulation patterns affect synapse plasticity

Plasticity-related proteins (PRPs), which are synthesized in a synapse activation-dependent manner, are shared by multiple synapses to a limited spatial extent for a specific period. In addition, stimulated synapses can utilize shared PRPs through synaptic tagging and capture (STC). In particular, the phenomenon by which short-lived early long-term potentiation is transformed into long-lived late long-term potentiation using shared PRPs is called “late-associativity,” which is the underlying principle of “cluster plasticity.” We hypothesized that the competitive capture of PRPs by multiple synapses modulates late-associativity and affects the fate of each synapse in terms of whether it is integrated into a synapse cluster. We tested our hypothesis by developing a computational model to simulate STC, late-associativity, and the competitive capture of PRPs. The experimental results obtained using the model revealed that the number of competing synapses, timing of stimulation to each synapse, and basal PRP level in the dendritic compartment altered the effective temporal window of STC and influenced the conditions under which late-associativity occurs. Furthermore, it is suggested that the competitive capture of PRPs results in the selection of synapses to be integrated into a synapse cluster via late-associativity.

Microanatomical study of pyramidal neurons in the contralesional somatosensory cortex after experimental ischemic stroke

At present, many studies support the notion that after stroke, remote regions connected to the infarcted area are also affected and may contribute to functional outcome. In the present study, we have analyzed possible microanatomical alterations in pyramidal neurons from the contralesional hemisphere after induced stroke. We performed intracellular injections of Lucifer yellow in pyramidal neurons from layer III in the somatosensory cortex of the contralesional hemisphere in an ischemic stroke mouse model. A detailed 3-dimensional analysis of the neuronal complexity and morphological alterations of dendritic spines was then performed. Our results demonstrate that pyramidal neurons from layer III in the somatosensory cortex of the contralesional hemisphere show selective changes in their dendritic arbors, namely, less dendritic complexity of the apical dendritic arbor-but no changes in the basal dendritic arbor. In addition, we found differences in spine morphology in both apical and basal dendrites comparing the contralesional hemisphere with the lesional hemisphere. Our results show that pyramidal neurons of remote areas connected to the infarct zone exhibit a series of selective changes in neuronal complexity and morphological distribution of dendritic spines, supporting the hypothesis that remote regions connected to the peri-infarcted area are also affected after stroke.

Dopamine D4 Receptor Is a Regulator of Morphine-Induced Plasticity in the Rat Dorsal Striatum

Long-term exposition to morphine elicits structural and synaptic plasticity in reward-related regions of the brain, playing a critical role in addiction. However, morphine-induced neuroadaptations in the dorsal striatum have been poorly studied despite its key function in drug-related habit learning. Here, we show that prolonged treatment with morphine triggered the retraction of the dendritic arbor and the loss of dendritic spines in the dorsal striatal projection neurons (MSNs). In an attempt to extend previous findings, we also explored whether the dopamine D4 receptor (D4R) could modulate striatal morphine-induced plasticity. The combined treatment of morphine with the D4R agonist PD168,077 produced an expansion of the MSNs dendritic arbors and restored dendritic spine density. At the electrophysiological level, PD168,077 in combination with morphine altered the electrical properties of the MSNs and decreased their excitability. Finally, results from the sustantia nigra showed that PD168,077 counteracted morphine-induced upregulation of μ opioid receptors (MOR) in striatonigral projections and downregulation of G protein-gated inward rectifier K+ channels (GIRK1 and GIRK2) in dopaminergic cells. The present results highlight the key function of D4R modulating morphine-induced plasticity in the dorsal striatum. Thus, D4R could represent a valuable pharmacological target for the safety use of morphine in pain management.

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.

Abnormal dendritic morphology in the cerebellum of cyclooxygenase-2- knockin mice

Prostaglandin E2 (PGE2) is a bioactive signalling molecule metabolized from the phospholipid membranes by the enzymatic activity of cycloxygenase-2 (COX-2). In the developing brain, COX-2 constitutively regulates the production of PGE2, which is important in neuronal development. However, abnormal COX-2/PGE2 signalling has been linked to neurodevelopmental disorders including autism spectrum disorders (ASDs). We have previously demonstrated that COX-2^- -KI mice show autism-related behaviours including social deficits, repetitive behaviours and anxious behaviours. COX-2-deficient mice also have deficits in pathways involved in synaptic transmission and dendritic spine formation. In this study, we use a Golgi-COX staining method to examine sex-dependent differences in dendritic and dendritic spine morphology in neurons of COX-2^- -KI mice cerebellum compared with wild-type (WT) matched controls at postnatal day 25 (P25). We show that COX-2^- -KI mice have increased dendritic arborization closer to the cell soma and increased dendritic looping. We also observed a sex-dependent effect of the COX-2^- -KI on dendritic thickness, dendritic spine density, dendritic spine morphology, and the expression of β-actin and the actin-binding protein spinophilin. Our findings show that changes in COX-2/PGE2 signalling lead to impaired morphology of dendrites and dendritic spines in a sex-dependant manner and may contribute the pathology of the cerebellum seen in individuals with ASD. This study provides further evidence that the COX-2^- -KI mouse model can be used to study a subset of ASD pathologies.

Drawing inspiration from biological dendrites to empower artificial neural networks

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.

Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for confining CF growth in organic memristors is demonstrated by exploiting the unique functionality of crosslinkable polymers. This can be achieved by predefining the localized ion-migration path (LIP) in crosslinkable polymers. In the proposed organic memristor, metal cations are locally transported along the LIP. Thus, CF growth is achieved only in a confined region. A flexible memristor with an LIP exhibits a vastly improved reliability and uniformity, and it is capable of operating with high mechanical and electrical endurance. Moreover, neuromorphic arrays based on the proposed memristor exhibit 96.3% learning accuracy, which is comparable to the ideal software baseline. The proposed concept of predefining the LIP in organic memristors is expected to provide novel platforms for the advance of flexible electronics and to realize a variety of practical neural networks for artificial intelligence applications.

Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

Adenosine Receptor-Mediated Developmental Loss of Spike Timing-Dependent Depression in the Hippocampus

Critical periods of synaptic plasticity facilitate the reordering and refining of neural connections during development, allowing the definitive synaptic circuits responsible for correct adult physiology to be established. Presynaptic spike timing-dependent long-term depression (t-LTD) exists in the hippocampus, which depends on the activation of NMDARs and that probably fulfills a role in synaptic refinement. This t-LTD is present until the third postnatal week in mice, disappearing in the fourth week of postnatal development. We were interested in the mechanisms underlying this maturation related loss of t-LTD and we found that at CA3–CA1 synapses, presynaptic NMDA receptors (pre-NMDARs) are tonically active between P13 and P21, mediating an increase in glutamate release during this critical period of plasticity. Conversely, at the end of this critical period (P22–P30) and coinciding with the loss of t-LTD, these pre-NMDARs are no longer tonically active. Using immunogold electron microscopy, we demonstrated the existence of pre-NMDARs at Schaffer collateral synaptic boutons, where a decrease in the number of pre-NMDARs during development coincides with the loss of both tonic pre-NMDAR activation and t-LTD. Interestingly, this t-LTD can be completely recovered by antagonizing adenosine type 1 receptors (A₁R), which also recovers the tonic activation of pre-NMDARs at P22–P30. By contrast, the induction of t-LTD was prevented at P13–P21 by an agonist of A₁R, as was tonic pre-NMDAR activation. Furthermore, we found that the adenosine that mediated the loss of t-LTD during the fourth week of development is supplied by astrocytes. These results provide direct evidence for the mechanism that closes the window of plasticity associated with t-LTD, revealing novel events probably involved in synaptic remodeling during development.

A mechanism for learning with sleep spindles

Spindles are ubiquitous oscillations during non-rapid eye movement (NREM) sleep. A growing body of evidence points to a possible link with learning and memory, and the underlying mechanisms are now starting to be unveiled. Specifically, spindles are associated with increased dendritic activity and high intracellular calcium levels, a situation favourable to plasticity, as well as with control of spiking output by feed-forward inhibition. During spindles, thalamocortical networks become unresponsive to inputs, thus potentially preventing interference between memory-related internal information processing and extrinsic signals. At the system level, spindles are co-modulated with other major NREM oscillations, including hippocampal sharp wave-ripples (SWRs) and neocortical slow waves, both previously shown to be associated with learning and memory. The sequential occurrence of reactivation at the time of SWRs followed by neuronal plasticity-promoting spindles is a possible mechanism to explain NREM sleep-dependent consolidation of memories. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two "noisy" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

Functional Architecture and Encoding of Tactile Sensorimotor Behavior in Rat Posterior Parietal Cortex

The posterior parietal cortex (PPC) in rodents is reciprocally connected to primary somatosensory and vibrissal motor cortices. The PPC neuronal circuitry could thus encode and potentially integrate incoming somatosensory information and whisker motor output. However, the information encoded across PPC layers during refined sensorimotor behavior remains largely unknown. To uncover the sensorimotor features represented in PPC during voluntary whisking and object touch, we performed loose-patch single-unit recordings and extracellular recordings of ensemble activity, covering all layers of PPC in anesthetized and awake, behaving male rats. First, using single-cell receptive field mapping, we revealed the presence of coarse somatotopy along the mediolateral axis in PPC. Second, we found that spiking activity was modulated during exploratory whisking in layers 2-4 and layer 6, but not in layer 5 of awake, behaving rats. Population spiking activity preceded actual movement, and whisker trajectory endpoints could be decoded by population spiking, suggesting that PPC is involved in movement planning. Finally, population spiking activity further increased in response to active whisker touch but only in PPC layers 2-4. Thus, we find layer-specific processing, which emphasizes the computational role of PPC during whisker sensorimotor behavior.SIGNIFICANCE STATEMENT The posterior parietal cortex (PPC) is thought to merge information on motor output and sensory input to orchestrate interaction with the environment, but the function of different PPC microcircuit components is poorly understood. We recorded neuronal activity in rat PPC during sensorimotor behavior involving motor and sensory pathways. We uncovered that PPC layers have dedicated function: motor and sensory information is merged in layers 2-4; layer 6 predominantly represents motor information. Collectively, PPC activity predicts future motor output, thus entailing a motor plan. Our results are important for understanding how PPC computationally processes motor output and sensory input. This understanding may facilitate decoding of brain activity when using brain-machine interfaces to overcome loss of function after, for instance, spinal cord injury.

Differential role of pre- and postsynaptic neurons in the activity-dependent control of synaptic strengths across dendrites

Neurons receive a large number of active synaptic inputs from their many presynaptic partners across their dendritic tree. However, little is known about how the strengths of individual synapses are controlled in balance with other synapses to effectively encode information while maintaining network homeostasis. This is in part due to the difficulty in assessing the activity of individual synapses with identified afferent and efferent connections for a synapse population in the brain. Here, to gain insights into the basic cellular rules that drive the activity-dependent spatial distribution of pre- and postsynaptic strengths across incoming axons and dendrites, we combine patch-clamp recordings with live-cell imaging of hippocampal pyramidal neurons in dissociated cultures and organotypic slices. Under basal conditions, both pre- and postsynaptic strengths cluster on single dendritic branches according to the identity of the presynaptic neurons, thus highlighting the ability of single dendritic branches to exhibit input specificity. Stimulating a single presynaptic neuron induces input-specific and dendritic branchwise spatial clustering of presynaptic strengths, which accompanies a widespread multiplicative scaling of postsynaptic strengths in dissociated cultures and heterosynaptic plasticity at distant synapses in organotypic slices. Our study provides evidence for a potential homeostatic mechanism by which the rapid changes in global or distant postsynaptic strengths compensate for input-specific presynaptic plasticity.

Patterns of Dendritic Basal Field Orientation of Pyramidal Neurons in the Rat Somatosensory Cortex

The study of neuronal dendritic orientation is of interest because it is related to how neurons grow dendrites to establish the synaptic input that neurons receive. The dendritic orientations of neurons in the nervous system vary, ranging from rather heterogeneously distributed (asymmetric) to homogeneously distributed (symmetric) dendritic arbors. Here, we analyze the dendritic orientation of the basal dendrites of intracellularly labeled pyramidal neurons from horizontal sections of Layers II–VI of the hindlimb somatosensory (S1HL) cortex of 14-d-old (P14) rats. We used circular statistics and proposed two new graphical descriptive representations of the neuron. We found that the dendritic pattern of most neurons was asymmetric. Furthermore, we found that there is a mixture of different types of orientations within any given group of neurons in any cortical layer. In addition, we investigated whether dendritic orientation was related to the physical location within the brain with respect to the anterior, dorsal, posterior and ventral directions. Generally, there was a preference towards the anterior orientation. A comparison between layers revealed that the preference for the anterior orientation was more pronounced in neurons located in Layers II, III, IV, and Va than for the neurons located in Layers Vb and VI. The dorsal orientation was the least preferred orientation in all layers, except for Layers IV and Va, where the ventral orientation had the lowest preference. Therefore, the orientation of basal dendritic arbors of pyramidal cells is variable and asymmetric, although a majority has a single orientation with a preference for the anterior direction in P14 rats.

Neural coding: A single neuron's perspective

What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neuronal populations are the neural basis of sensory representations. In the somatosensory cortex, however, spiking of individual (or pairs of) cortical neurons is only minimally informative about the world. Recent studies showed that one solution neurons implement to counteract this information loss is adapting their rate of information transfer to the ongoing synaptic activity by changing the membrane potential at which spike is generated. Here we first introduce the principles of information flow from the sensory periphery to the primary sensory cortex in a model sensory (whisker) system, and subsequently discuss how the adaptive spike threshold gates the intracellular information transfer from the somatic post-synaptic potential to action potentials, controlling the information content of communication across somatosensory cortical neurons.

Human Cortical Pyramidal Neurons: From Spines to Spikes via Models

We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na⁺ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA-conductances per synaptic contact (0.88 and 1.31 nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers, and the assumption of five contacts per synaptic connection. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 ± 4.6 mV), spine base (9.7 ± 5.0 mV), and soma (0.3 ± 0.1 mV), and for the spine neck resistance (50–80 MΩ). Matching the shape and firing pattern of experimental somatic Na⁺-spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 ± 28 simultaneously activated HL2/L3-HL2/L3 synapses are required for generating (with 50% probability) a somatic Na⁺ spike. Dendritic NMDA spikes were triggered in the model when 20 ± 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA-spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat somatosensory cortex. These multi-sites non-linear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.

Modelling plasticity in dendrites: from single cells to networks

One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.

Regulation of Rho GTPase proteins during spine structural plasticity for the control of local dendritic plasticity

While it is generally appreciated that learning involves the structural rearrangement of neuronal circuits, the underlying orchestration of molecular events that drives these changes is not as well understood. Recent studies on the spatiotemporal organization of synaptic signaling events have provided new insights into the biochemical underpinnings of various expressions of structural neuronal plasticity, as well as the functional consequences that emerge because of the particular behavior of the molecules involved. In particular, activity patterns of and interplay among a class of morphogenic signaling proteins, the Rho GTPases, and their downstream signals, are found to be critical for linking neuronal activity with various forms of neuronal plasticity. We review recent findings on this topic and discuss their physiological implications.