The role of dendritic inhibition in shaping the plasticity of excitatory synapses

A generalized mathematical framework for the calcium control hypothesis describes weight-dependent synaptic plasticity

The brain modifies synaptic strengths to store new information via long-term potentiation (LTP) and long-term depression (LTD). Evidence has mounted that long-term synaptic plasticity is controlled via concentrations of calcium ([Ca2+]) in postsynaptic dendritic spines. Several mathematical models describe this phenomenon, including those of Shouval, Bear, and Cooper (SBC) (Shouval et al., 2002, 2010) and Graupner and Brunel (GB) (Graupner & Brunel, 2012). Here we suggest a generalized version of the SBC and GB models, the fixed point - learning rate (FPLR) framework, where the synaptic [Ca2+] specifies a fixed point toward which the synaptic weight approaches asymptotically at a [Ca2+]-dependent rate. The FPLR framework offers a straightforward phenomenological interpretation of calcium-based plasticity: the calcium concentration tells the synaptic weight where it is going and how quickly it goes there. The FPLR framework can flexibly incorporate various experimental findings, including the existence of multiple regions of [Ca2+] where no plasticity occurs, or plasticity observed experimentally in cerebellar Purkinje cells, where the directionality of calcium-based synaptic changes is reversed relative to cortical and hippocampal neurons. We also suggest a modeling approach that captures the dependency of late-phase plasticity stabilization on protein synthesis. We demonstrate that due to the asymptotic nature of synaptic changes in the FPLR rule, the plastic changes induced by frequency- and spike-timing-dependent plasticity protocols are weight-dependent. Finally, we show how the FPLR framework can explain the weight-dependence observed in behavioral time scale plasticity (BTSP).

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis

Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how these theoretically-derived rules relate to biological mechanisms of plasticity in the brain, or how these different rules might be mechanistically implemented in different contexts and brain regions. This study shows that the calcium control hypothesis, which relates synaptic plasticity in the brain to the calcium concentration ([Ca2+]) in dendritic spines, can produce a diverse array of learning rules. We propose a simple, perceptron-like neuron model, the calcitron, that has four sources of [Ca2+]: local (following the activation of an excitatory synapse and confined to that synapse), heterosynaptic (resulting from the activity of other synapses), postsynaptic spike-dependent, and supervisor-dependent. We demonstrate that by modulating the plasticity thresholds and calcium influx from each calcium source, we can reproduce a wide range of learning and plasticity protocols, such as Hebbian and anti-Hebbian learning, frequency-dependent plasticity, and unsupervised recognition of frequently repeating input patterns. Moreover, by devising simple neural circuits to provide supervisory signals, we show how the calcitron can implement homeostatic plasticity, perceptron learning, and BTSP-inspired one-shot learning. Our study bridges the gap between theoretical learning algorithms and their biological counterparts, not only replicating established learning paradigms but also introducing novel rules.

miRNA-mediated control of gephyrin synthesis drives sustained inhibitory synaptic plasticity

Activity-dependent protein synthesis is crucial for long-lasting forms of synaptic plasticity. However, our understanding of translational mechanisms controlling GABAergic synapses is limited. One distinct form of inhibitory long-term potentiation (iLTP) enhances postsynaptic clusters of GABAARs and the primary inhibitory scaffold, gephyrin, to promote sustained synaptic strengthening. While we previously found that persistent iLTP requires mRNA translation, the mechanisms controlling plasticity-induced gephyrin translation remain unknown. We identify miR153 as a novel regulator of Gphn mRNA translation which controls gephyrin protein levels and synaptic clustering, ultimately impacting inhibitory synaptic structure and function. iLTP induction downregulates miR153, reversing its translational suppression of Gphn mRNA and promoting de novo gephyrin protein synthesis and synaptic clustering during iLTP. Finally, we find that reduced miR153 expression during iLTP is driven by an excitation-transcription coupling pathway involving calcineurin, NFAT and HDACs, which also controls the miRNA-dependent upregulation of GABAARs. Together, we delineate a miRNA-dependent post-transcriptional mechanism that controls the expression of the key synaptic scaffold, gephyrin, and may converge with parallel miRNA pathways to coordinate gene upregulation to maintain inhibitory synaptic plasticity.

miRNA-mediated control of gephyrin synthesis drives sustained inhibitory synaptic plasticity

Activity-dependent protein synthesis is crucial for many long-lasting forms of synaptic plasticity. However, our understanding of the translational mechanisms controlling inhibitory synapses is limited. One distinct form of inhibitory long-term potentiation (iLTP) enhances postsynaptic clusters of GABAARs and the primary inhibitory scaffold, gephyrin, to promote sustained synaptic strengthening. While we previously found that persistent iLTP requires mRNA translation, the precise mechanisms controlling gephyrin translation during this process remain unknown. Here, we identify miR153 as a novel regulator of Gphn mRNA translation which controls gephyrin protein levels and synaptic clustering, ultimately impacting GABAergic synaptic structure and function. We find that iLTP induction downregulates miR153, reversing its translational suppression of Gphn mRNA and allowing for increased de novo gephyrin protein synthesis and synaptic clustering during iLTP. Finally, we find that reduced miR153 expression during iLTP is driven by an excitation-transcription coupling pathway involving calcineurin, NFAT and HDACs, which also controls the miRNA-dependent upregulation of GABAARs. Overall, this work delineates a miRNA-dependent post-transcriptional mechanism that controls the expression of the key synaptic scaffold, gephyrin, and may converge with parallel miRNA pathways to coordinate gene upregulation to maintain inhibitory synaptic plasticity.

The dendritic engram

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

Asymmetric Voltage Attenuation in Dendrites Can Enable Hierarchical Heterosynaptic Plasticity

Long-term synaptic plasticity is mediated via cytosolic calcium concentrations ([Ca2+]). Using a synaptic model that implements calcium-based long-term plasticity via two sources of Ca2+ - NMDA receptors and voltage-gated calcium channels (VGCCs) - we show in dendritic cable simulations that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at nonactivated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry can produce a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at the active synapses, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity.

Differential effects of group III metabotropic glutamate receptors on spontaneous inhibitory synaptic currents in spine-innervating double bouquet and parvalbumin-expressing dendrite-targeting GABAergic interneurons in human neocortex

Diverse neocortical GABAergic neurons specialize in synaptic targeting and their effects are modulated by presynaptic metabotropic glutamate receptors (mGluRs) suppressing neurotransmitter release in rodents, but their effects in human neocortex are unknown. We tested whether activation of group III mGluRs by L-AP4 changes GABAA receptor-mediated spontaneous inhibitory postsynaptic currents (sIPSCs) in 2 distinct dendritic spine-innervating GABAergic interneurons recorded in vitro in human neocortex. Calbindin-positive double bouquet cells (DBCs) had columnar "horsetail" axons descending through layers II-V innervating dendritic spines (48%) and shafts, but not somata of pyramidal and nonpyramidal neurons. Parvalbumin-expressing dendrite-targeting cell (PV-DTC) axons extended in all directions innervating dendritic spines (22%), shafts (65%), and somata (13%). As measured, 20% of GABAergic neuropil synapses innervate spines, hence DBCs, but not PV-DTCs, preferentially select spine targets. Group III mGluR activation paradoxically increased the frequency of sIPSCs in DBCs (to median 137% of baseline) but suppressed it in PV-DTCs (median 92%), leaving the amplitude unchanged. The facilitation of sIPSCs in DBCs may result from their unique GABAergic input being disinhibited via network effect. We conclude that dendritic spines receive specialized, diverse GABAergic inputs, and group III mGluRs differentially regulate GABAergic synaptic transmission to distinct GABAergic cell types in human cortex.

SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory

Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is instrumental to advanced cognitive intelligence. However, it is hard to endow a simple neuron model with working memory, and to understand the biological mechanisms that have resulted in such a powerful ability at the neuronal level. This article presents a novel self-adaptive multicompartment spiking neuron model, referred to as SAM, for spike-based learning with working memory. SAM integrates four major biological principles including sparse coding, dendritic non-linearity, intrinsic self-adaptive dynamics, and spike-driven learning. We first describe SAM's design and explore the impacts of critical parameters on its biological dynamics. We then use SAM to build spiking networks to accomplish several different tasks including supervised learning of the MNIST dataset using sequential spatiotemporal encoding, noisy spike pattern classification, sparse coding during pattern classification, spatiotemporal feature detection, meta-learning with working memory applied to a navigation task and the MNIST classification task, and working memory for spatiotemporal learning. Our experimental results highlight the energy efficiency and robustness of SAM in these wide range of challenging tasks. The effects of SAM model variations on its working memory are also explored, hoping to offer insight into the biological mechanisms underlying working memory in the brain. The SAM model is the first attempt to integrate the capabilities of spike-driven learning and working memory in a unified single neuron with multiple timescale dynamics. The competitive performance of SAM could potentially contribute to the development of efficient adaptive neuromorphic computing systems for various applications from robotics to edge computing.

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron's dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

Single cortical neurons as deep artificial neural networks

Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical models of cortical neurons at millisecond (spiking) resolution. A temporally convolutional DNN with five to eight layers was required to capture the I/O mapping of a realistic model of a layer 5 cortical pyramidal cell (L5PC). This DNN generalized well when presented with inputs widely outside the training distribution. When NMDA receptors were removed, a much simpler network (fully connected neural network with one hidden layer) was sufficient to fit the model. Analysis of the DNNs' weight matrices revealed that synaptic integration in dendritic branches could be conceptualized as pattern matching from a set of spatiotemporal templates. This study provides a unified characterization of the computational complexity of single neurons and suggests that cortical networks therefore have a unique architecture, potentially supporting their computational power.

NDNF interneurons in layer 1 gain-modulate whole cortical columns according to an animal's behavioral state

Processing of sensory information in neural circuits is modulated by an animal's behavioral state, but the underlying cellular mechanisms are not well understood. Focusing on the mouse visual cortex, here we analyze the role of GABAergic interneurons that are located in layer 1 and express Ndnf (L1 NDNF INs) in the state-dependent control over sensory processing. We find that the ongoing and sensory-evoked activity of L1 NDNF INs is strongly enhanced when an animal is aroused and that L1 NDNF INs gain-modulate local excitatory neurons selectively during high-arousal states by inhibiting their apical dendrites while disinhibiting their somata via Parvalbumin-expressing interneurons. Because active NDNF INs are evenly spread in L1 and can affect excitatory neurons across all cortical layers, this indicates that the state-dependent activation of L1 NDNF INs and the subsequent shift of inhibition in excitatory neurons toward their apical dendrites gain-modulate sensory processing in whole cortical columns.

Sensing and processing whisker deflections in rodents

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

Low-threshold spiking interneurons perform feedback inhibition in the lateral amygdala

Amygdala plays crucial roles in emotional learning. The lateral amygdala (LA) is the input station of the amygdala, where learning related plasticity occurs. The LA is cortical like in nature in terms of its cellular make up, composed of a majority of principal cells and a minority of interneurons with distinct subtypes defined by morphology, intrinsic electrophysiological properties and neurochemical expression profile. The specific functions served by LA interneuron subtypes remain elusive. This study aimed to elucidate the interneuron subtype mediating feedback inhibition. Electrophysiological evidence involving antidromic activation of recurrent LA circuitry via basolateral amygdala stimulation and paired recordings implicate low-threshold spiking interneurons in feedback inhibition. Recordings in somatostatin-cre animals crossed with tdtomato mice have revealed remarkable similarities between a subset of SOM+ interneurons and LTS interneurons. This study concludes that LTS interneurons, most of which are putatively SOM+, mediate feedback inhibition in the LA. Parallels with cortical areas and potential implications for information processing and plasticity are discussed.

Synaptic Clustering and Memory Formation

In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed "synapse clustering" and has been shown to emerge in many different memory-related paradigms, as well as in in vitro studies. The clustering of synapses may emerge from synapses receiving similar input, or via many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.

Striatal Low-Threshold Spiking Interneurons Regulate Goal-Directed Learning

The dorsomedial striatum (DMS) is critically involved in motor control and reward processing, but the specific neural circuit mediators are poorly understood. Recent evidence highlights the extensive connectivity of low-threshold spiking interneurons (LTSIs) within local striatal circuitry; however, the in vivo function of LTSIs remains largely unexplored. We employed fiber photometry to assess LTSI calcium activity in a range of DMS-mediated behaviors, uncovering specific reward-related activity that is down-modulated during goal-directed learning. Using two mechanistically distinct manipulations, we demonstrated that this down-modulation of LTSI activity is critical for acquisition of novel contingencies, but not for their modification. In contrast, continued LTSI activation slowed instrumental learning. Similar manipulations of fast-spiking interneurons did not reproduce these effects, implying a specific function of LTSIs. Finally, we revealed a role for the γ-aminobutyric acid (GABA)ergic functions of LTSIs in learning. Together, our data provide new insights into this striatal interneuron subclass as important gatekeepers of goal-directed learning.

Homeostatic synaptic plasticity as a metaplasticity mechanism - a molecular and cellular perspective

The molecular mechanisms underlying various types of synaptic plasticity are historically regarded as separate processes involved in independent cellular events. However, recent progress in our molecular understanding of Hebbian and homeostatic synaptic plasticity supports the observation that these two types of plasticity share common cellular events, and are often altered together in neurological diseases. Here, we discuss the emerging concept of homeostatic synaptic plasticity as a metaplasticity mechanism with a focus on cellular signaling processes that enable a direct interaction between Hebbian and homeostatic plasticity. We also identify distinct and shared molecular players involved in these cellular processes that may be explored experimentally in future studies to test the hypothesis that homeostatic synaptic plasticity serves as a metaplasticity mechanism to integrate changes in neuronal activity and support optimal Hebbian learning.

From membrane receptors to protein synthesis and actin cytoskeleton: Mechanisms underlying long lasting forms of synaptic plasticity

Synaptic plasticity, the activity dependent change in synaptic strength, forms the molecular foundation of learning and memory. Synaptic plasticity includes structural changes, with spines changing their size to accomodate insertion and removal of postynaptic receptors, which are correlated with functional changes. Of particular relevance for memory storage are the long lasting forms of synaptic plasticity which are protein synthesis dependent. Due to the importance of spine structural plasticity and protein synthesis, this review focuses on the signaling pathways that connect synaptic stimulation with regulation of protein synthesis and remodeling of the actin cytoskeleton. We also review computational models that implement novel aspects of molecular signaling in synaptic plasticity, such as the role of neuromodulators and spatial microdomains, as well as highlight the need for computational models that connect activation of memory kinases with spine actin dynamics.

Inhibition enhances spatially-specific calcium encoding of synaptic input patterns in a biologically constrained model

Synaptic plasticity, which underlies learning and memory, depends on calcium elevation in neurons, but the precise relationship between calcium and spatiotemporal patterns of synaptic inputs is unclear. Here, we develop a biologically realistic computational model of striatal spiny projection neurons with sophisticated calcium dynamics, based on data from rodents of both sexes, to investigate how spatiotemporally clustered and distributed excitatory and inhibitory inputs affect spine calcium. We demonstrate that coordinated excitatory synaptic inputs evoke enhanced calcium elevation specific to stimulated spines, with lower but physiologically relevant calcium elevation in nearby non-stimulated spines. Results further show a novel and important function of inhibition-to enhance the difference in calcium between stimulated and non-stimulated spines. These findings suggest that spine calcium dynamics encode synaptic input patterns and may serve as a signal for both stimulus-specific potentiation and heterosynaptic depression, maintaining balanced activity in a dendritic branch while inducing pattern-specific plasticity.

Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models

In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb's postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected to depend on the spatiotemporal dynamics of the underlying signaling pathways. However in most cases, due to technical limitations experiments grant only indirect access to these pathways. Computational models carefully constrained by experiments, allow for a better qualitative understanding of the molecular basis of STDP and its regulation by neuromodulators. Recently, computational models of calcium dynamics and signaling pathway molecules have started to explore STDP emergence in ex and in vivo-like conditions. These models are expected to reproduce better at least part of the complex modulation of STDP as an emergent property of the underlying molecular pathways. Elucidation of the mechanisms underlying STDP modulation and its consequences on network dynamics is of critical importance and will allow better understanding of the major mechanisms of memory storage and recall both in health and disease.

Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons

The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current. When impinging on its early phase, individual inhibitory synapses strongly, but transiently, dampen the NMDA spike; later inhibition prematurely terminates it. A single inhibitory synapse reduces the NMDA-mediated Ca²⁺ current, a key player in plasticity, by up to 45%. NMDA spikes in distal dendritic branches/spines are longer-lasting and more resilient to inhibition, enhancing synaptic plasticity at these branches. We conclude that NMDA spikes are highly sensitive to dendritic inhibition; sparse weak inhibition can finely tune synaptic plasticity both locally at the dendritic branch level and globally at the level of the neuron's output.

Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks

Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSP's planar four-color graph coloring, maximum independent set, and sudoku on this substrate and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of nonsaturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by nonlinear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation and offer insight into the computational role of dual inhibitory mechanisms in neural circuits.

Functional implications of inhibitory synapse placement on signal processing in pyramidal neuron dendrites

A rich literature describes inhibitory innervation of pyramidal neurons in terms of the distinct inhibitory cell types that target the soma, axon initial segment, or dendritic arbor. Less attention has been devoted to how localization of inhibition to specific parts of the pyramidal dendritic arbor influences dendritic signal detection and integration. The effect of inhibitory inputs can vary based on their placement on dendritic spines versus shaft, their distance from the soma, and the branch order of the dendrite they inhabit. Inhibitory synapses are also structurally dynamic, and the implications of these dynamics depend on their dendritic location. Here we consider the heterogeneous roles of inhibitory synapses as defined by their strategic placement on the pyramidal cell dendritic arbor.

Detailed Dendritic Excitatory/Inhibitory Balance through Heterosynaptic Spike-Timing-Dependent Plasticity

The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. Furthermore, heterosynaptic STDP explains how the maturation of inhibitory neurons modulates the selectivity of excitatory neurons for binocular matching in the critical period plasticity. The model also provides an alternative explanation for the potential mechanism underlying the somatic detailed balance that is commonly associated with inhibitory STDP. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and the resultant dendritic computation.SIGNIFICANCE STATEMENT Recent experimental studies reveal that relative differences in spike timings experienced among neighboring glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in the efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Through computational modeling at the parameter regime where previous experimental results are well reproduced, we show that heterosynaptic plasticity serves to finely balance excitatory and inhibitory inputs on the dendrite. Our results suggest a principle of GABA-driven neural circuit formation.

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.

Morphological Neuron Classification Using Machine Learning

Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.

A dendritic disinhibitory circuit mechanism for pathway-specific gating

While reading a book in a noisy café, how does your brain ‘gate in' visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. The branch-specific disinhibition can be achieved despite dense interneuronal connectivity, even with random connections. Moreover, clustering of input pathways on dendrites can naturally emerge through synaptic plasticity regulated by dendritic inhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. The model suggests that cognitive flexibility engages top-down signalling of behavioural rule or context that targets specific classes of inhibitory neurons.

Cortical circuits receive simultaneous inputs from multiple pathways and are able to flexibly select the appropriate inputs for processing. Here the authors propose a network model in which dendritic branch-specific disinhibition established through synaptic plasticity achieves pathway-specific gating.

Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons

Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning. To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. The position of inhibitory synapses on the dendritic tree determines the spatial extent of the effect and allows a pathway-specific regulation of plasticity. With appropriate timing, EPSPs can still trigger somatic action potentials, although backpropagating signals are abolished. An annihilation of bAPs requires precisely timed inhibition, while the timing constraints are less stringent for distal calcium spikes. We further show that a wide-spread motif of local circuits—feedforward inhibition—is well suited to provide the temporal precision needed for the control of bAPs. Altogether, our model provides experimentally testable predictions and demonstrates that the inhibitory switch of plasticity can be a robust and attractive mechanism, hence assigning an additional function to the inhibitory elements of neuronal microcircuits beyond modulation of excitability.

We must constantly learn in order to meet the demands of a dynamically changing environment. The basis of learning is believed to be synaptic plasticity, i.e. the potential of neuronal connections to change. Depending on context, however, it may be either useful to learn and modify connections or, alternatively, to keep an established network structure stable to maintain what has been already learned (also referred to as the plasticity-stability dilemma). The ability to switch synaptic plasticity on and off in a flexible way hence constitutes an attractive feature of neuronal processing. Here, we analyze a cellular mechanism based on the inhibition-mediated gating of coincidence signals required for Hebbian forms of excitatory synaptic plasticity. While experimental evidence in support of individual steps involved in this mechanism is accumulating, it is as of now unclear whether this mechanism can indeed operate robustly under physiologically realistic parameters of pyramidal cells, in particular, without impairing information flow in these cells altogether. Computational modeling allows us to demonstrate that this is indeed possible if inhibition is well timed (on the order of 1 ms). Moreover, we show that a specific design of the local circuit can ensure the necessary timing.

Synapse-specific expression of calcium-permeable AMPA receptors in neocortical layer 5

Key points

In the hippocampus, calcium‐permeable AMPA receptors have been found in a restricted subset of neuronal types that inhibit other neurons, although their localization in the neocortex is less well understood.

In the present study, we looked for calcium‐permeable AMPA receptors in two distinct populations of neocortical inhibitory neurons: basket cells and Martinotti cells. We found them in the former but not in the latter. Furthermore, in basket cells, these receptors were associated with particularly fast responses.

Computer modelling predicted (and experiments verified) that fast calcium‐permeable AMPA receptors enable basket cells to respond rapidly, such that they promptly inhibit neighbouring cells and shut down activity.

The results obtained in the present study help our understanding of pathologies such as stroke and epilepsy that have been associated with disordered regulation of calcium‐permeable AMPA receptors.

Abstract

AMPA‐type glutamate receptors (AMPARs) lacking an edited GluA2 subunit are calcium‐permeable (CP) and contribute to synaptic plasticity in several hippocampal interneuron types, although their precise role in the neocortex is not well described. We explored the presence of CP‐AMPARs at pyramidal cell (PC) inputs to Martinotti cells (MCs) and basket cells (BCs) in layer 5 of the developing mouse visual cortex (postnatal days 12–21). GluA2 immunolabelling was stronger in MCs than in BCs. A differential presence of CP‐AMPARs at PC‐BC and PC‐MC synapses was confirmed electrophysiologically, based on measures of spermine‐dependent rectification and CP‐AMPAR blockade by 1‐naphtyl acetyl spermine using recordings from synaptically connected cell pairs, NPEC‐AMPA uncaging and miniature current recordings. In addition, CP‐AMPAR expression in BCs was correlated with rapidly decaying synaptic currents. Computer modelling predicted that this reduces spike latencies and sharpens suprathreshold responses in BCs, which we verified experimentally using the dynamic clamp technique. Thus, the synapse‐specific expression of CP‐AMPARs may critically influence both plasticity and information processing in neocortical microcircuits.

In the hippocampus, calcium‐permeable AMPA receptors have been found in a restricted subset of neuronal types that inhibit other neurons, although their localization in the neocortex is less well understood.

In the present study, we looked for calcium‐permeable AMPA receptors in two distinct populations of neocortical inhibitory neurons: basket cells and Martinotti cells. We found them in the former but not in the latter. Furthermore, in basket cells, these receptors were associated with particularly fast responses.

Computer modelling predicted (and experiments verified) that fast calcium‐permeable AMPA receptors enable basket cells to respond rapidly, such that they promptly inhibit neighbouring cells and shut down activity.

The results obtained in the present study help our understanding of pathologies such as stroke and epilepsy that have been associated with disordered regulation of calcium‐permeable AMPA receptors.

Ketamine Strengthens CRF-Activated Amygdala Inputs to Basal Dendrites in mPFC Layer V Pyramidal Cells in the Prelimbic but not Infralimbic Subregion, A Key Suppressor of Stress Responses

A single sub-anesthetic dose of ketamine, a short-acting NMDA receptor blocker, induces a rapid and prolonged antidepressant effect in treatment-resistant major depression. In animal models, ketamine (24 h) reverses depression-like behaviors and associated deficits in excitatory postsynaptic currents (EPSCs) generated in apical dendritic spines of layer V pyramidal cells of medial prefrontal cortex (mPFC). However, little is known about the effects of ketamine on basal dendrites. The basal dendrites of layer V cells receive an excitatory input from pyramidal cells of the basolateral amygdala (BLA), neurons that are activated by the stress hormone CRF. Here we found that CRF induces EPSCs in PFC layer V cells and that ketamine enhanced this effect through the mammalian target of rapamycin complex 1 synaptogenic pathway; the CRF-induced EPSCs required an intact BLA input and were generated primarily in basal dendrites. In contrast to its detrimental effects on apical dendritic structure and function, chronic stress did not induce a loss of CRF-induced EPSCs in basal dendrites, thereby creating a relative imbalance in favor of amygdala inputs. The effects of ketamine were complex: ketamine enhanced apical EPSC responses in all mPFC subregions, anterior cingulate (AC), prelimbic (PL), and infralimbic (IL) but enhanced CRF-induced EPSCs only in AC and PL-responses were unchanged in IL, a critical area for suppression of stress responses. We propose that by restoring the strength of apical inputs relative to basal amygdala inputs, especially in IL, ketamine would ameliorate the hypothesized disproportional negative influence of the amygdala in chronic stress and major depression.

Synaptic clustering within dendrites: an emerging theory of memory formation

It is generally accepted that complex memories are stored in distributed representations throughout the brain, however the mechanisms underlying these representations are not understood. Here, we review recent findings regarding the subcellular mechanisms implicated in memory formation, which provide evidence for a dendrite-centered theory of memory. Plasticity-related phenomena which affect synaptic properties, such as synaptic tagging and capture, synaptic clustering, branch strength potentiation and spinogenesis provide the foundation for a model of memory storage that relies heavily on processes operating at the dendrite level. The emerging picture suggests that clusters of functionally related synapses may serve as key computational and memory storage units in the brain. We discuss both experimental evidence and theoretical models that support this hypothesis and explore its advantages for neuronal function.