Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation

Impairment of synaptic plasticity in the primary somatosensory cortex in a model of diabetic mice

Type 1 and type 2 diabetic patients experience alterations in the Central Nervous System, leading to cognitive deficits. Cognitive deficits have been also observed in animal models of diabetes such as impaired sensory perception, as well as deficits in working and spatial memory functions. It has been suggested that a reduction of insulin-like growth factor-I (IGF-I) and/or insulin levels may induce these neurological disorders. We have studied synaptic plasticity in the primary somatosensory cortex of young streptozotocin (STZ)-diabetic mice. We focused on the influence of reduced IGF-I brain levels on cortical synaptic plasticity. Unit recordings were conducted in layer 2/3 neurons of the primary somatosensory (S1) cortex in both control and STZ-diabetic mice under isoflurane anesthesia. Synaptic plasticity was induced by repetitive whisker stimulation. Results showed that repetitive stimulation of whiskers (8 Hz induction train) elicited a long-term potentiation (LTP) in layer 2/3 neurons of the S1 cortex of control mice. In contrast, the same induction train elicited a long-term depression (LTD) in STZ-diabetic mice that was dependent on NMDA and metabotropic glutamatergic receptors. The reduction of IGF-I brain levels in diabetes could be responsible of synaptic plasticity impairment, as evidenced by improved response facilitation in STZ-diabetic mice following the application of IGF-I. This hypothesis was further supported by immunochemical techniques, which revealed a reduction in IGF-I receptors in the layer 2/3 of the S1 cortex in STZ-diabetic animals. The observed synaptic plasticity impairments in STZ-diabetic animals were accompanied by decreased performance in a whisker discrimination task, along with reductions in IGF-I, GluR1, and NMDA receptors observed in immunochemical studies. In conclusion, impaired synaptic plasticity in the S1 cortex may stem from reduced IGF-I signaling, leading to decreased intracellular signal pathways and thus, glutamatergic receptor numbers in the cellular membrane.

Synaptic Plasticity in the Injured Brain Depends on the Temporal Pattern of Stimulation

Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction; however, little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain. We explored interactions of the temporal pattern and frequency of neurostimulation in the normal cerebral cortex and after mild traumatic brain injury (mTBI), to inform therapies to strengthen or weaken neural circuits in injured brains, as well as to better understand the role of these factors in normal brain plasticity. Whole-cell (WC) patch-clamp recordings of evoked postsynaptic potentials in individual neurons, as well as field potential (FP) recordings, were made from layer 2/3 of visual cortex in response to stimulation of layer 4, in acute slices from control (naive), sham operated, and mTBI rats. We compared synaptic plasticity induced by different stimulation protocols, each consisting of a specific frequency (1 Hz, 10 Hz, or 100 Hz), continuity (continuous or discontinuous), and temporal pattern (perfectly regular, slightly irregular, or highly irregular). At the individual neuron level, dramatic differences in plasticity outcome occurred when the highly irregular stimulation protocol was used at 1 Hz or 10 Hz, producing an overall LTD in controls and shams, but a robust overall LTP after mTBI. Consistent with the individual neuron results, the plasticity outcomes for simultaneous FP recordings were similar, indicative of our results generalizing to a larger scale synaptic network than can be sampled by individual WC recordings alone. In addition to the differences in plasticity outcome between control (naive or sham) and injured brains, the dynamics of the changes in synaptic responses that developed during stimulation were predictive of the final plasticity outcome. Our results demonstrate that the temporal pattern of stimulation plays a role in the polarity and magnitude of synaptic plasticity induced in the cerebral cortex while highlighting differences between normal and injured brain responses. Moreover, these results may be useful for optimization of neurostimulation therapies to treat mTBI and other brain disorders, in addition to providing new insights into downstream plasticity signaling mechanisms in the normal brain.

Adult Neurogenesis Reconciles Flexibility and Stability of Olfactory Perceptual Memory

In brain regions featuring ongoing plasticity, the task of quickly encoding new information without overwriting old memories presents a significant challenge. In the rodent olfactory bulb, which is renowned for substantial structural plasticity driven by adult neurogenesis and persistent turnover of dendritic spines, we show that such plasticity is vital to overcoming this flexibility-stability dilemma. To do so, we develop a computational model for structural plasticity in the olfactory bulb and show that the maturation of adult-born neurons facilitates the abilities to learn quickly and forget slowly. Particularly important to achieve this goal are the transient enhancement of the plasticity, excitability, and susceptibility to apoptosis that characterizes young neurons. The model captures many experimental observations and makes a number of testable predictions. Overall, it identifies memory consolidation as an important role of adult neurogenesis in olfaction and exemplifies how the brain can maintain stable memories despite ongoing extensive plasticity.

The impact of early and late blindness on language and verbal working memory: A brain-constrained neural model

Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce distinct neuroplastic changes in the visual cortex, repurposing it for language and semantic processing. Interestingly, these functional changes provoke a unique cognitive advantage - enhanced verbal working memory, particularly in early blindness. Yet, the underlying neuromechanisms and the impact on language and memory-related circuits remain not fully understood. Here, we applied a brain-constrained neural network mimicking the structural and functional features of the frontotemporal-occipital cortices, to model conceptual acquisition in early and late blindness. The results revealed differential expansion of conceptual-related neural circuits into deprived visual areas depending on the timing of visual loss, which is most prominent in early blindness. This neural recruitment is fundamentally governed by the biological principles of neural circuit expansion and the absence of uncorrelated sensory input. Critically, the degree of these changes is constrained by the availability of neural matter previously allocated to visual experiences, as in the case of late blindness. Moreover, we shed light on the implication of visual deprivation on the neural underpinnings of verbal working memory, revealing longer reverberatory neural activity in 'blind models' as compared to the sighted ones. These findings provide a better understanding of the interplay between visual deprivations, neuroplasticity, language processing and verbal working memory.

Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks

The strengthening and the weakening of synaptic strength in existing Bienenstock-Cooper-Munro (BCM) learning rule are determined by a long-term potentiation (LTP) sliding modification threshold and the afferent synaptic activities. However, synaptic long-term depression (LTD) even affects low-active synapses during the induction of synaptic plasticity, which may lead to information loss. Biological experiments have found another LTD threshold that can induce either potentiation or depression or no change, even at the activated synapses. In addition, existing BCM learning rules can only select a set of fixed rule parameters, which is biologically implausible and practically inflexible to learn the structural information of input signals. In this article, an evolved dual-threshold BCM learning rule is proposed to regulate the reservoir internal connection weights of the echo-state-network (ESN), which can contribute to alleviating information loss and enhancing learning performance by introducing different optimal LTD thresholds for different postsynaptic neurons. Our experimental results show that the evolved dual-threshold BCM learning rule can result in the synergistic learning of different plasticity rules, effectively improving the learning performance of an ESN in comparison with existing neural plasticity learning rules and some state-of-the-art ESN variants on three widely used benchmark tasks and the prediction of an esterification process.

Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data-namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.

Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule

Intelligent behavior depends on the brain's ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

Target cell-specific plasticity rules of NMDA receptor-mediated synaptic transmission in the hippocampus

Long-term potentiation and depression of NMDA receptor-mediated synaptic transmission (NMDAR LTP/LTD) can significantly impact synapse function and information transfer in several brain areas. However, the mechanisms that determine the direction of NMDAR plasticity are poorly understood. Here, using physiologically relevant patterns of presynaptic and postsynaptic burst activities, whole-cell patch clamp recordings, 2-photon laser calcium imaging in acute rat hippocampal slices and immunoelectron microscopy, we tested whether distinct calcium dynamics and group I metabotropic glutamate receptor (I-mGluR) subtypes control the sign of NMDAR plasticity. We found that postsynaptic calcium transients (CaTs) in response to hippocampal MF stimulation were significantly larger during the induction of NMDAR-LTP compared to NMDAR-LTD at the MF-to-CA3 pyramidal cell (MF-CA3) synapse. This difference was abolished by pharmacological blockade of mGluR5 and was significantly reduced by depletion of intracellular calcium stores, whereas blocking mGluR1 had no effect on these CaTs. In addition, we discovered that MF to hilar mossy cell (MF-MC) synapses, which share several structural and functional commonalities with MF-CA3 synapses, also undergoes NMDAR plasticity. To our surprise, however, we found that the postsynaptic distribution of I-mGluR subtypes at these two synapses differ, and the same induction protocol that induces NMDAR-LTD at MF-CA3 synapses, only triggered NMDAR-LTP at MF-MC synapses, despite a comparable calcium dynamics. Thus, postsynaptic calcium dynamics alone cannot predict the sign of NMDAR plasticity, indicating that both postsynaptic calcium rise and the relative contribution of I-mGluR subtypes likely determine the learning rules of NMDAR plasticity.

Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective

The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.

Metabolic Syndrome as a Risk Factor for Alzheimer's Disease: A Focus on Insulin Resistance

Alzheimer's disease (AD) is the main type of dementia and is a disease with a profound socioeconomic burden due to the lack of effective treatment. In addition to genetics and environmental factors, AD is highly associated with metabolic syndrome, defined as the combination of hypertension, hyperlipidemia, obesity and type 2 diabetes mellitus (T2DM). Among these risk factors, the connection between AD and T2DM has been deeply studied. It has been suggested that the mechanism linking both conditions is insulin resistance. Insulin is an important hormone that regulates not only peripheral energy homeostasis but also brain functions, such as cognition. Insulin desensitization, therefore, could impact normal brain function increasing the risk of developing neurodegenerative disorders in later life. Paradoxically, it has been demonstrated that decreased neuronal insulin signalling can also have a protective role in aging and protein-aggregation-associated diseases, as is the case in AD. This controversy is fed by studies focused on neuronal insulin signalling. However, the role of insulin action on other brain cell types, such as astrocytes, is still unexplored. Therefore, it is worthwhile exploring the involvement of the astrocytic insulin receptor in cognition, as well as in the onset and/or development of AD.

Influence of language on perception and concept formation in a brain-constrained deep neural network model

A neurobiologically constrained model of semantic learning in the human brain was used to simulate the acquisition of concrete and abstract concepts, either with or without verbal labels. Concept acquisition and semantic learning were simulated using Hebbian learning mechanisms. We measured the network's category learning performance, defined as the extent to which it successfully (i) grouped partly overlapping perceptual instances into a single (abstract or concrete) conceptual representation, while (ii) still distinguishing representations for distinct concepts. Co-presence of linguistic labels with perceptual instances of a given concept generally improved the network's learning of categories, with a significantly larger beneficial effect for abstract than concrete concepts. These results offer a neurobiological explanation for causal effects of language structure on concept formation and on perceptuo-motor processing of instances of these concepts: supplying a verbal label during concept acquisition improves the cortical mechanisms by which experiences with objects and actions along with the learning of words lead to the formation of neuronal ensembles for specific concepts and meanings. Furthermore, the present results make a novel prediction, namely, that such 'Whorfian' effects should be modulated by the concreteness/abstractness of the semantic categories being acquired, with language labels supporting the learning of abstract concepts more than that of concrete ones. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Non-invasive brain neuromodulation techniques for chronic low back pain

Structural and functional changes of the brain occur in many chronic pain conditions, including chronic low back pain (CLBP), and these brain abnormalities can be reversed by effective treatment. Research on the clinical applications of non-invasive brain neuromodulation (NIBS) techniques for chronic pain is increasing. Unfortunately, little is known about the effectiveness of NIBS on CLBP, which limits its application in clinical pain management. Therefore, we summarized the effectiveness and limitations of NIBS techniques on CLBP management and described the effects and mechanisms of NIBS approaches on CLBP in this review. Overall, NIBS may be effective for the treatment of CLBP. And the analgesic mechanisms of NIBS for CLBP may involve the regulation of pain signal pathway, synaptic plasticity, neuroprotective effect, neuroinflammation modulation, and variations in cerebral blood flow and metabolism. Current NIBS studies for CLBP have limitations, such as small sample size, relative low quality of evidence, and lack of mechanistic studies. Further studies on the effect of NIBS are needed, especially randomized controlled trials with high quality and large sample size.

Structural spine plasticity: Learning and forgetting of odor-specific subnetworks in the olfactory bulb

Learning to discriminate between different sensory stimuli is essential for survival. In rodents, the olfactory bulb, which contributes to odor discrimination via pattern separation, exhibits extensive structural synaptic plasticity involving the formation and removal of synaptic spines, even in adult animals. The network connectivity resulting from this plasticity is still poorly understood. To gain insight into this connectivity we present here a computational model for the structural plasticity of the reciprocal synapses between the dominant population of excitatory principal neurons and inhibitory interneurons. It incorporates the observed modulation of spine stability by odor exposure. The model captures the striking experimental observation that the exposure to odors does not always enhance their discriminability: while training with similar odors enhanced their discriminability, training with dissimilar odors actually reduced the discriminability of the training stimuli. Strikingly, this differential learning does not require the activity-dependence of the spine stability and occurs also in a model with purely random spine dynamics in which the spine density is changed homogeneously, e.g., due to a global signal. However, the experimentally observed odor-specific reduction in the response of principal cells as a result of extended odor exposure and the concurrent disinhibition of a subset of principal cells arise only in the activity-dependent model. Moreover, this model predicts the experimentally testable recovery of odor response through weak but not through strong odor re-exposure and the forgetting of odors via exposure to interfering odors. Combined with the experimental observations, the computational model provides strong support for the prediction that odor exposure leads to the formation of odor-specific subnetworks in the olfactory bulb.

A key feature of the brain is its ability to learn through the plasticity of its network. The olfactory bulb in the olfactory system is a remarkable brain area whose anatomical structure evolves substantially still in adult animals by establishing new synaptic connections and removing existing ones. We present a computational model for this process and employ it to interpret recent experimental results. By comparing the results of our model with those of a random control model we identify various experimental observations that lend strong support to the notion that the network of the olfactory bulb comprises learned, odor-specific subnetworks. Moreover, our model explains the recent observation that the learning of odors does not always improve their discriminability and provides testable predictions for the recovery of odor response after repeated odor exposure and for when the learning of new odors interferes with retaining the memory of familiar odors.

A requirement for astrocyte IP3R2 signaling for whisker experience-dependent depression and homeostatic upregulation in the mouse barrel cortex

Changes to sensory experience result in plasticity of synapses in the cortex. This experience-dependent plasticity (EDP) is a fundamental property of the brain. Yet, while much is known about neuronal roles in EDP, very little is known about the role of astrocytes. To address this issue, we used the well-described mouse whiskers-to-barrel cortex system, which expresses a number of forms of EDP. We found that all-whisker deprivation induced characteristic experience-dependent Hebbian depression (EDHD) followed by homeostatic upregulation in L2/3 barrel cortex of wild type mice. However, these changes were not seen in mutant animals (IP₃R2^–/–) that lack the astrocyte-expressed IP₃ receptor subtype. A separate paradigm, the single-whisker experience, induced potentiation of whisker-induced response in both wild-type (WT) mice and IP₃R2^–/– mice. Recordings in ex vivo barrel cortex slices reflected the in vivo results so that long-term depression (LTD) could not be elicited in slices from IP₃R2^–/– mice, but long-term potentiation (LTP) could. Interestingly, 1 Hz stimulation inducing LTD in WT paradoxically resulted in NMDAR-dependent LTP in slices from IP₃R2^–/– animals. The LTD to LTP switch was mimicked by acute buffering astrocytic [Ca²⁺]_i in WT slices. Both WT LTD and IP₃R2^–/– 1 Hz LTP were mediated by non-ionotropic NMDAR signaling, but only WT LTD was P38 MAPK dependent, indicating an underlying mechanistic switch. These results demonstrate a critical role for astrocytic [Ca²⁺]_i in several EDP mechanisms in neocortex.

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.

Numerical Simulation: Fluctuation in Background Synaptic Activity Regulates Synaptic Plasticity

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain.

Modelling concrete and abstract concepts using brain-constrained deep neural networks

A neurobiologically constrained deep neural network mimicking cortical areas relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically 'ground' concrete and abstract concepts. Grounding sets consisted of three grounding patterns with neurons representing specific perceptual or action-related features; neurons were either unique to one pattern or shared between patterns of the same set. Concrete categories were modelled as pattern triplets overlapping in their 'shared neurons', thus implementing semantic feature sharing of all instances of a category. In contrast, abstract concepts had partially shared feature neurons common to only pairs of category instances, thus, exhibiting family resemblance, but lacking full feature overlap. Stimulation with concrete and abstract conceptual patterns and biologically realistic unsupervised learning caused formation of strongly connected cell assemblies (CAs) specific to individual grounding patterns, whose neurons were spread out across all areas of the deep network. After learning, the shared neurons of the instances of concrete concepts were more prominent in central areas when compared with peripheral sensorimotor ones, whereas for abstract concepts the converse pattern of results was observed, with central areas exhibiting relatively fewer neurons shared between pairs of category members. We interpret these results in light of the current knowledge about the relative difficulty children show when learning abstract words. Implications for future neurocomputational modelling experiments as well as neurobiological theories of semantic representation are discussed.

Calcium-Permeable AMPA Receptors Mediate Timing-Dependent LTP Elicited by Low Repeat Coincident Pre- and Postsynaptic Activity at Schaffer Collateral-CA1 Synapses

High-frequency stimulation induced long-term potentiation (LTP) and low-frequency stimulation induced LTD are considered as cellular models of memory formation. Interestingly, spike timing-dependent plasticity (STDP) can induce equally robust timing-dependent LTP (t-LTP) and t-LTD in response to low frequency repeats of coincident action potential (AP) firing in presynaptic and postsynaptic cells. Commonly, STDP paradigms relying on 25-100 repeats of coincident AP firing are used to elicit t-LTP or t-LTD, but the minimum number of repeats required for successful STDP is barely explored. However, systematic investigation of physiologically relevant low repeat STDP paradigms is of utmost importance to explain learning mechanisms in vivo. Here, we examined low repeat STDP at Schaffer collateral-CA1 synapses by pairing one presynaptic AP with either one postsynaptic AP (1:1 t-LTP), or a burst of 4 APs (1:4 t-LTP) and found 3-6 repeats to be sufficient to elicit t-LTP. 6× 1:1 t-LTP required postsynaptic Ca2+ influx via NMDARs and L-type VGCCs and was mediated by increased presynaptic glutamate release. In contrast, 1:4 t-LTP depended on postsynaptic metabotropic GluRs and ryanodine receptor signaling and was mediated by postsynaptic insertion of AMPA receptors. Unexpectedly, both 6× t-LTP variants were strictly dependent on activation of postsynaptic Ca2+-permeable AMPARs but were differentially regulated by dopamine receptor signaling. Our data show that synaptic changes induced by only 3-6 repeats of mild STDP stimulation occurring in ≤10 s can take place on time scales observed also during single trial learning.

Learning hierarchically-structured concepts

We use a recently developed synchronous Spiking Neural Network (SNN) model to study the problem of learning hierarchically-structured concepts. We introduce an abstract data model that describes simple hierarchical concepts. We define a feed-forward layered SNN model, with learning modeled using Oja's local learning rule, a well known biologically-plausible rule for adjusting synapse weights. We define what it means for such a network to recognize hierarchical concepts; our notion of recognition is robust, in that it tolerates a bounded amount of noise. Then, we present a learning algorithm by which a layered network may learn to recognize hierarchical concepts according to our robust definition. We analyze correctness and performance rigorously; the amount of time required to learn each concept, after learning all of the sub-concepts, is approximately O1ηkℓmaxlog(k)+1ɛ+blog(k), where k is the number of sub-concepts per concept, ℓmax is the maximum hierarchical depth, η is the learning rate, ɛ describes the amount of uncertainty allowed in robust recognition, and b describes the amount of weight decrease for "irrelevant" edges. An interesting feature of this algorithm is that it allows the network to learn sub-concepts in a highly interleaved manner. This algorithm assumes that the concepts are presented in a noise-free way; we also extend these results to accommodate noise in the learning process. Finally, we give a simple lower bound saying that, in order to recognize concepts with hierarchical depth two with noise-tolerance, a neural network should have at least two layers. The results in this paper represent first steps in the theoretical study of hierarchical concepts using SNNs. The cases studied here are basic, but they suggest many directions for extensions to more elaborate and realistic cases.

Biological constraints on neural network models of cognitive function

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling.

Evidence-Based Guidelines and Secondary Meta-Analysis for the Use of Transcranial Direct Current Stimulation in Neurological and Psychiatric Disorders

BACKGROUND

Transcranial direct current stimulation has shown promising clinical results, leading to increased demand for an evidence-based review on its clinical effects.

OBJECTIVE

We convened a team of transcranial direct current stimulation experts to conduct a systematic review of clinical trials with more than 1 session of stimulation testing: pain, Parkinson's disease motor function and cognition, stroke motor function and language, epilepsy, major depressive disorder, obsessive compulsive disorder, Tourette syndrome, schizophrenia, and drug addiction.

METHODS

Experts were asked to conduct this systematic review according to the search methodology from PRISMA guidelines. Recommendations on efficacy were categorized into Levels A (definitely effective), B (probably effective), C (possibly effective), or no recommendation. We assessed risk of bias for all included studies to confirm whether results were driven by potentially biased studies.

RESULTS

Although most of the clinical trials have been designed as proof-of-concept trials, some of the indications analyzed in this review can be considered as definitely effective (Level A), such as depression, and probably effective (Level B), such as neuropathic pain, fibromyalgia, migraine, post-operative patient-controlled analgesia and pain, Parkinson's disease (motor and cognition), stroke (motor), epilepsy, schizophrenia, and alcohol addiction. Assessment of bias showed that most of the studies had low risk of biases, and sensitivity analysis for bias did not change these results. Effect sizes vary from 0.01 to 0.70 and were significant in about 8 conditions, with the largest effect size being in postoperative acute pain and smaller in stroke motor recovery (nonsignificant when combined with robotic therapy).

CONCLUSION

All recommendations listed here are based on current published PubMed-indexed data. Despite high levels of evidence in some conditions, it must be underscored that effect sizes and duration of effects are often limited; thus, real clinical impact needs to be further determined with different study designs.

Calcium Channel-Dependent Induction of Long-Term Synaptic Plasticity at Excitatory Golgi Cell Synapses of Cerebellum

Golgi cells, together with granule cells and mossy fibers, form a neuronal microcircuit regulating information transfer at the cerebellum input stage. Despite theoretical predictions, little was known about long-term synaptic plasticity at Golgi cell synapses. Here, we have used whole-cell patch-clamp recordings and calcium imaging to investigate long-term synaptic plasticity at excitatory synapses impinging on Golgi cells. In acute mouse cerebellar slices, mossy fiber theta-burst stimulation (TBS) could induce either long-term potentiation (LTP) or long-term depression (LTD) at mossy fiber-Golgi cell and granule cell-Golgi cell synapses. This synaptic plasticity showed a peculiar voltage dependence, with LTD or LTP being favored when TBS induction occurred at depolarized or hyperpolarized potentials, respectively. LTP required, in addition to NMDA channels, activation of T-type Ca²⁺ channels, while LTD required uniquely activation of L-type Ca²⁺ channels. Notably, the voltage dependence of plasticity at the mossy fiber-Golgi cell synapses was inverted with respect to pure NMDA receptor-dependent plasticity at the neighboring mossy fiber-granule cell synapse, implying that the mossy fiber presynaptic terminal can activate different induction mechanisms depending on the target cell. In aggregate, this result shows that Golgi cells show cell-specific forms of long-term plasticity at their excitatory synapses, that could play a crucial role in sculpting the response patterns of the cerebellar granular layer.SIGNIFICANCE STATEMENT This article shows for the first time a novel form of Ca²⁺ channel-dependent synaptic plasticity at the excitatory synapses impinging on cerebellar Golgi cells. This plasticity is bidirectional and inverted with respect to NMDA receptor-dependent paradigms, with long-term depression (LTD) and long-term potentiation (LTP) being favored at depolarized and hyperpolarized potentials, respectively. Furthermore, LTP and LTD induction requires differential involvement of T-type and L-type voltage-gated Ca²⁺ channels rather than the NMDA receptors alone. These results, along with recent computational predictions, support the idea that Golgi cell plasticity could play a crucial role in controlling information flow through the granular layer along with cerebellar learning and memory.

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.

Lentiviral Expression of Rabies Virus Glycoprotein in the Rat Hippocampus Strengthens Synaptic Plasticity

Rabies virus (RABV) is a neurotropic virus exclusively infecting neurons in the central nervous system. RABV encodes five proteins. Among them, the viral glycoprotein (RVG) plays a key role in viral entry into neurons and rabies pathogenesis. It was shown that the nature of the C-terminus of the RABV G protein, which possesses a PDZ-binding motif (PBM), modulates the virulence of the RABV strain. The neuronal protein partners recruited by this PBM may alter host cell function. This study was conducted to investigate the effect of RVG on synaptic function in the hippocampal dentate gyrus (DG) of rat. Two μl (10⁸ T.U./ml) of the lentiviral vector containing RVG gene was injected into the DG of rat hippocampus. After 2 weeks, the rat's brain was cross-sectioned and RVG-expressing cells were detected by fluorescent microscopy. Hippocampal synaptic activity of the infected rats was then examined by recording the local field potentials from DG after stimulation of the perforant pathway. Short-term synaptic plasticity was also assessed by double pulse stimulation. Expression of RVG in DG increased long-term potentiation population spikes (LTP-PS), whereas no facilitation of LTP-PS was found in neurons expressing δRVG (deleted PBM). Furthermore, RVG and δRVG strengthened paired-pulse facilitation. Heterosynaptic long-term depression (LTD) in the DG was significantly blocked in RVG-expressing group compared to the control group. This blockade was dependent to PBM motif as rats expressing δRVG in the DG-expressed LTD comparable to the RVG group. Our data demonstrate that RVG expression facilitates both short- and long-term synaptic plasticity in the DG indicating that it may involve both pre- and postsynaptic mechanisms to alter synaptic function. Further studies are needed to elucidate the underlying mechanisms.

Unifying Long-Term Plasticity Rules for Excitatory Synapses by Modeling Dendrites of Cortical Pyramidal Neurons

A large number of experiments have indicated that precise spike times, firing rates, and synapse locations crucially determine the dynamics of long-term plasticity induction in excitatory synapses. However, it remains unknown how plasticity mechanisms of synapses distributed along dendritic trees cooperate to produce the wide spectrum of outcomes for various plasticity protocols. Here, we propose a four-pathway plasticity framework that is well grounded in experimental evidence and apply it to a biophysically realistic cortical pyramidal neuron model. We show in computer simulations that several seemingly contradictory experimental landmark studies are consistent with one unifying set of mechanisms when considering the effects of signal propagation in dendritic trees with respect to synapse location. Our model identifies specific spatiotemporal contributions of dendritic and axo-somatic spikes as well as of subthreshold activation of synaptic clusters, providing a unified parsimonious explanation not only for rate and timing dependence but also for location dependence of synaptic changes.

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A phenomenological synaptic plasticity rule is applied to a pyramidal neuron model

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Model reproduces rate-, timing-, and location-dependent plasticity results

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Active dendrites allow plasticity via dendritic spikes and subthreshold events

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Cooperative plasticity exists across the dendritic tree and within single branches

Induction of LTD-like corticospinal plasticity by low-frequency rTMS depends on pre-stimulus phase of sensorimotor μ-rhythm

Background

Neural oscillations reflect rapidly changing brain excitability states. We have demonstrated previously with EEG-triggered transcranial magnetic stimulation (TMS) of human motor cortex that the positive vs. negative peak of the sensorimotor μ-oscillation reflect corticospinal low-vs. high-excitability states. In vitro experiments showed that induction of long-term depression (LTD) by low-frequency stimulation depends on the postsynaptic excitability state.

Objective/Hypothesis: We tested the hypothesis that induction of LTD-like corticospinal plasticity in humans by 1 Hz repetitive TMS (rTMS) is enhanced when rTMS is synchronized with the low-excitability state, but decreased or even shifted towards long-term (LTP)-like plasticity when synchronized with the high-excitability state.

Methods

We applied real-time EEG-triggered 1-Hz-rTMS (900 pulses) to the hand area of motor cortex in healthy subjects. In a randomized double-blind three-condition crossover design, pulses were synchronized to either the positive or negative peak of the sensorimotor μ-oscillation, or were applied at random phase (control). The amplitude of motor evoked potentials was recorded as an index of corticospinal excitability before and after 1-Hz-rTMS.

Results

1-Hz-rTMS at random phase resulted in a trend towards LTD-like corticospinal plasticity. RTMS in the positive peak condition (i.e., the low-excitability state) induced significant LTD-like plasticity. RTMS in the negative peak condition (i.e., the high-excitability state) showed a trend towards LTP-like plasticity, which was significantly different from the other two conditions.

Conclusion

The level of corticospinal depolarization reflected by phase of the μ-oscillation determines the degree of corticospinal plasticity induced by low-frequency rTMS, a finding that may guide future personalized therapeutic stimulation.

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Positive vs. negative phase of μ-rhythm are states of low vs. high excitability.

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1-Hz-rTMS coupled to positive but not negative phase results in LTD-like plasticity.

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Phase of μ-rhythm determines effect size of 1-Hz-rTMS induced plasticity.

A spike-timing-dependent plasticity rule for dendritic spines

The structural organization of excitatory inputs supporting spike-timing-dependent plasticity (STDP) remains unknown. We performed a spine STDP protocol using two-photon (2P) glutamate uncaging (pre) paired with postsynaptic spikes (post) in layer 5 pyramidal neurons from juvenile mice. Here we report that pre-post pairings that trigger timing-dependent LTP (t-LTP) produce shrinkage of the activated spine neck and increase in synaptic strength; and post-pre pairings that trigger timing-dependent LTD (t-LTD) decrease synaptic strength without affecting spine shape. Furthermore, the induction of t-LTP with 2P glutamate uncaging in clustered spines (<5 μm apart) enhances LTP through a NMDA receptor-mediated spine calcium accumulation and actin polymerization-dependent neck shrinkage, whereas t-LTD was dependent on NMDA receptors and disrupted by the activation of clustered spines but recovered when separated by >40 μm. These results indicate that synaptic cooperativity disrupts t-LTD and extends the temporal window for the induction of t-LTP, leading to STDP only encompassing LTP.

Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo

Two elements of neural information processing have primarily been proposed: firing rate and spike timing of neurons. In the case of synaptic plasticity, although spike-timing-dependent plasticity (STDP) depending on presynaptic and postsynaptic spike times had been considered the most common rule, recent studies have shown the inhibitory nature of the brain in vivo for precise spike timing, which is key to the STDP. Thus, the importance of the firing frequency in synaptic plasticity in vivo has been recognized again. However, little is understood about how the frequency-dependent synaptic plasticity (FDP) is regulated in vivo. Here, we focused on the presynaptic input pattern, the intracellular calcium decay time constants, and the background synaptic activity, which vary depending on neuron types and the anatomical and physiological environment in the brain. By analyzing a calcium-based model, we found that the synaptic weight differs depending on these factors characteristic in vivo, even if neurons receive the same input rate. This finding suggests the involvement of multifaceted factors other than input frequency in FDP and even neural coding in vivo.

Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO3-x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.

SIVA-1 regulates apoptosis and synaptic function by modulating XIAP interaction with the death receptor antagonist FAIM-L

The long isoform of Fas apoptosis inhibitory molecule (FAIM-L) is a neuron-specific death receptor antagonist that modulates apoptotic cell death and mechanisms of neuronal plasticity. FAIM-L exerts its antiapoptotic action by binding to X-linked inhibitor of apoptosis protein (XIAP), an inhibitor of caspases, which are the main effectors of apoptosis. XIAP levels are regulated by the ubiquitin-proteasome pathway. FAIM-L interaction with XIAP prevents the ubiquitination and degradation of the latter, thereby allowing it to inhibit caspase activation. This interaction also modulates non-apoptotic functions of caspases, such as the endocytosis of AMPA receptor (AMPAR) in hippocampal long-term depression (LTD). The molecular mechanism of action exerted by FAIM-L is unclear since the consensus binding motifs are still unknown. Here, we performed a two-hybrid screening to discover novel FAIM-L-interacting proteins. We found a functional interaction of SIVA-1 with FAIM-L. SIVA-1 is a proapoptotic protein that has the capacity to interact with XIAP. We describe how SIVA-1 regulates FAIM-L function by disrupting the interaction of FAIM-L with XIAP, thereby promoting XIAP ubiquitination, caspase-3 activation and neuronal death. Furthermore, we report that SIVA-1 plays a role in receptor internalization in synapses. SIVA-1 is upregulated upon chemical LTD induction, and it modulates AMPAR internalization via non-apoptotic activation of caspases. In summary, our findings uncover SIVA-1 as new functional partner of FAIM-L and demonstrate its role as a regulator of caspase activity in synaptic function.

Multi-level anomalous Hall resistance in a single Hall cross for the applications of neuromorphic device

We demonstrate the process of obtaining memristive multi-states Hall resistance (R_H) change in a single Hall cross (SHC) structure. Otherwise, the working mechanism successfully mimics the behavior of biological neural systems. The motion of domain wall (DW) in the SHC was used to control the ascend (or descend) of the R_H amplitude. The primary synaptic functions such as long-term potentiation (LTP), long-term depression (LTD), and spike-time-dependent plasticity (STDP) could then be emulated by regulating R_H. Applied programmable magnetic field pulses are in varying conditions such as intensity and duration to adjust R_H. These results show that analog readings of DW movement can be closely resembled with the change of synaptic weight and have great potentials for bioinspired neuromorphic computing.

Glutamate treatment mimics LTP- and LTD-like biochemical activity in viable synaptosome preparation

Long-term potentiation (LTP) and long-term depression (LTD) are considered to be the cellular mechanisms behind the increase or decrease of synaptic strength respectively. Electrophysiologically induced LTP/LTD is associated with the activation of glutamate receptors in the synaptic terminals resulting in the initiation of biochemical processes in the postsynaptic terminals and thus propagation of synaptic activity. Isolated nerve endings i.e. synaptosome preparation was used to study here, the biochemical phenotypes of LTP and LTD, and glutamate treatment in varying concentration for different time was used to induce those biochemical phenomena. Treatment with 200 μM glutamate showed increased GluA1 phosphorylation at serine 831 and activation of CaMKIIα by phosphorylation at threonine 286 like LTP, whereas 100 μM glutamate treatment showed decrease in GluA1 phosphorylation level at both pGluA1(S831) and pGluA1(S845), and activation of GSK3β by de-phosphorylating pGSK3β at serine 9 like LTD. The 200 μM glutamate treatment was associated with an increase in the local translation of Arc, BDNF, CaMKIIα and Homer1, whereas 100 μM glutamate treatments resulted in decrease in the level of the said synaptic proteins and the effect was blocked by the proteasomal inhibitor, Lactasystin. Both, the local translation and local degradation was sensitive to the Ca²⁺ chellator, Bapta-AM, indicating that both the phenomena were dependent on the rise in intra-synaptosomal Ca²⁺, like LTP and LTD. Overall the results of the present study suggest that synaptosomal preparations can be a viable alternative to study mechanisms underlying the biochemical activities of LTP/LTD in short term.

Calcium Signaling During Brain Aging and Its Influence on the Hippocampal Synaptic Plasticity

Calcium (Ca²⁺) ions are highly versatile intracellular signaling molecules and are universal second messenger for regulating a variety of cellular and physiological functions including synaptic plasticity. Ca²⁺ homeostasis in the central nervous system endures subtle dysregulation with advancing age. Research has provided abundant evidence that brain aging is associated with altered neuronal Ca²⁺ regulation and synaptic plasticity mechanisms. Much of the work has focused on the hippocampus, a brain region critically involved in learning and memory, which is particularly susceptible to dysfunction during aging. The current chapter takes a specific perspective, assessing various Ca²⁺ sources and the influence of aging on Ca²⁺ sources and synaptic plasticity in the hippocampus. Integrating the knowledge of the complexity of age-related alterations in neuronal Ca²⁺ signaling and synaptic plasticity mechanisms will positively shape the development of highly effective therapeutics to treat brain disorders including cognitive impairment associated with aging and neurodegenerative disease.

Development of Depotentiation in Adult-Born Dentate Granule Cells

Activity-dependent synaptic plasticity, i.e., long-term potentiation (LTP), long-term depression (LTD) and LTP reversal, is generally thought to make up the cellular mechanism underlying learning and memory in the mature brain, in which N-methyl-D-aspartate subtype of glutamate (NMDA) receptors and neurogenesis play important roles. LTP reversal may be the mechanism of forgetting and may mediate many psychiatric disorders, such as schizophrenia, but the specific mechanisms underlying these disorders remain unclear. In addition, LTP reversal during the development of adult-born dentate granule cells (DGCs) remains unknown. We found that the expression of the NMDA receptor subunits NR2A and NR2B displayed dynamic changes during the development of postnatal individuals and the maturation of adult-born neurons and was coupled with the change in LTP reversal. The susceptibility of LTP reversal progressively increases with the rise in the expression of NR2A during the development of postnatal individual and adult-born neurons. In addition, NMDA receptor subunits NR2A, but not NR2B, mediated LTP reversal in the DGCs of the mouse hippocampus.

Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology

Pyramidal cells in layer V of the neocortex are one of the most widely studied neuron types in the mammalian brain. Due to their role as integrators of feedforward and cortical feedback inputs, they are well-positioned to contribute to the symptoms and pathology in mental disorders-such as schizophrenia-that are characterized by a mismatch between the internal perception and external inputs. In this modeling study, we analyze the input/output properties of layer V pyramidal cells and their sensitivity to modeled genetic variants in schizophrenia-associated genes. We show that the excitability of layer V pyramidal cells and the way they integrate inputs in space and time are altered by many types of variants in ion-channel and Ca²⁺ transporter-encoding genes that have been identified as risk genes by recent genome-wide association studies. We also show that the variability in the output patterns of spiking and Ca²⁺ transients in layer V pyramidal cells is altered by these model variants. Importantly, we show that many of the predicted effects are robust to noise and qualitatively similar across different computational models of layer V pyramidal cells. Our modeling framework reveals several aspects of single-neuron excitability that can be linked to known schizophrenia-related phenotypes and existing hypotheses on disease mechanisms. In particular, our models predict that single-cell steady-state firing rate is positively correlated with the coding capacity of the neuron and negatively correlated with the amplitude of a prepulse-mediated adaptation and sensitivity to coincidence of stimuli in the apical dendrite and the perisomatic region of a layer V pyramidal cell. These results help to uncover the voltage-gated ion-channel and Ca²⁺ transporter-associated genetic underpinnings of schizophrenia phenotypes and biomarkers.

Probing Single Synapses via the Photolytic Release of Neurotransmitters

The development of two-photon microscopy has revolutionized our understanding of how synapses are formed and how they transform synaptic inputs in dendritic spines-tiny protrusions that cover the dendrites of pyramidal neurons that receive most excitatory synaptic information in the brain. These discoveries have led us to better comprehend the neuronal computations that take place at the level of dendritic spines as well as within neuronal circuits with unprecedented resolution. Here, we describe a method that uses a two-photon (2P) microscope and 2P uncaging of caged neurotransmitters for the activation of single and multiple spines in the dendrites of cortical pyramidal neurons. In addition, we propose a cost-effective description of the components necessary for the construction of a one laser source-2P microscope capable of nearly simultaneous 2P uncaging of neurotransmitters and 2P calcium imaging of the activated spines and nearby dendrites. We provide a brief overview on how the use of these techniques have helped researchers in the last 15 years unravel the function of spines in: (a) information processing; (b) storage; and (c) integration of excitatory synaptic inputs.

LTD-like plasticity of the human primary motor cortex can be reversed by γ-tACS

BACKGROUND

Cortical oscillatory activities play a role in regulating several brain functions in humans. However, whether motor resonant oscillations (i.e. β and γ) modulate long-term depression (LTD)-like plasticity of the primary motor cortex (M1) is still unclear.

OBJECTIVE

To address this issue, we combined transcranial alternating current stimulation (tACS), a technique able to entrain cortical oscillations, with continuous theta burst stimulation (cTBS), a transcranial magnetic stimulation (TMS) protocol commonly used to induce LTD-like plasticity in M1.

METHODS

Motor evoked potentials (MEPs) elicited by single-pulse TMS, short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) were evaluated before and 5, 15 and 30 min after cTBS alone or cTBS delivered during β-tACS (cTBS-β) or γ-tACS (cTBS-γ). Moreover, we tested the effects of β-tACS (alone) on short-latency afferent inhibition (SAI) and γ-tACS on SICI in order to verify whether tACS-related interneuronal modulation contributes to the effects of tACS-cTBS co-stimulation.

RESULTS

cTBS-γ turned the expected after-effects of cTBS from inhibition to facilitation. By contrast, responses to cTBS-β were similar to those induced by cTBS alone. β- and γ-tACS did not change MEPs evoked by single-pulse TMS. β-tACS reduced SAI and γ-tACS reduced SICI. However, the degree of γ-tACS-induced modulation of SICI did not correlate with the effects of cTBS-γ.

CONCLUSION

γ-tACS reverses cTBS-induced plasticity of the human M1. γ-oscillations may therefore regulate LTD-like plasticity mechanisms.

Metabotropic Glutamate Receptor 5 and 8 Modulate the Ameliorative Effect of Ultramicronized Palmitoylethanolamide on Cognitive Decline Associated with Neuropathic Pain

This study investigated whether metabotropic glutamate receptor (mGluR) 5 and 8 are involved in the effect of ultramicronizedpalmitoylethanolamide (um-PEA) on the cognitive behavior and long term potentiation (LTP) at entorhinal cortex (LEC)-dentate gyrus (DG) pathway in mice rendered neuropathic by the spare nerve injury (SNI). SNI reduced discriminative memory and LTP. Um-PEA treatment started after the development of neuropathic pain had no effects in sham mice, whereas it restored cognitive behavior and LTP in SNI mice. 2-Methyl-6-(phenylethynyl) pyridine (MPEP), a selective mGluR5 antagonist, improved cognition in SNI mice and produced a chemical long term depression of the field excitatory postsynaptic potentials (fEPSPs) in sham and SNI mice. After theta burst stimulation (TBS) MPEP restored LTP in SNI mice. In combination with PEA, MPEP antagonized the PEA effect on discriminative memory and decreased LTP in SNI mice. The (RS)-4-(1-amino-1-carboxyethyl)phthalic acid (MDCPG), a selective mGluR8 antagonist, did not affect discriminative memory, but it induced a chemical LTP and prevented the enhancement of fEPSPs after TBS in SNI mice which were treated or not treated with PEA. The effect of PEA on LTP and cognitive behavior was modulated by mGluR5 and mGluR8. In particular in the SNI conditions, the mGluR5 blockade facilitated memory and LTP, but prevented the beneficial effects of PEA on discriminative memory while the mGluR8 blockade, which was ineffective in itself, prevented the favorable action of the PEA on LTP. Thus, although their opposite roles (excitatory/inhibitory of the two receptor subtypes on the glutamatergic system), they appeared to be required for the neuroprotective effect of PEA in conditions of neuropathic pain.

A model of amygdala function following plastic changes at specific synapses during extinction

The synaptic networks in the amygdala have been the subject of intense interest in recent times, primarily because of the role of this structure in emotion. Fear and its extinction depend on the workings of these networks, with particular interest in extinction because of its potential to ameliorate adverse symptoms associated with post-traumatic stress disorder. Here we place emphasis on the extinction networks revealed by recent techniques, and on the probable plasticity properties of their synaptic connections. We use modules of neurons representing each of the principal components identified as involved in extinction. Each of these modules consists of neural networks, containing specific ratios of excitatory and specialized inhibitory neurons as well as synaptic plasticity mechanisms appropriate for the component of the amygdala they represent. While these models can produce dynamic output, here we concentrate on the equilibrium outputs and do not model the details of the plasticity mechanisms. Pavlovian fear conditioning generates a fear memory in the lateral amygdala module that leads to activation of neurons in the basal nucleus fear module but not in the basal nucleus extinction module. Extinction protocols excite infralimbic medial prefrontal cortex neurons (IL) which in turn excite so-called extinction neurons in the amygdala, leading to the release of endocannabinoids from them and an increase in efficacy of synapses formed by lateral amygdala neurons on them. The model simulations show how such a mechanism could explain experimental observations involving the role of IL as well as endocannabinoids in different temporal phases of extinction.

Synaptic plasticity onto inhibitory neurons as a mechanism for ocular dominance plasticity

Ocular dominance plasticity is a well-documented phenomenon allowing us to study properties of cortical maturation. Understanding this maturation might be an important step towards unravelling how cortical circuits function. However, it is still not fully understood which mechanisms are responsible for the opening and closing of the critical period for ocular dominance and how changes in cortical responsiveness arise after visual deprivation. In this article, we present a theory of ocular dominance plasticity. Following recent experimental work, we propose a framework where a reduction in inhibition is necessary for ocular dominance plasticity in both juvenile and adult animals. In this framework, two ingredients are crucial to observe ocular dominance shifts: a sufficient level of inhibition as well as excitatory-to-inhibitory synaptic plasticity. In our model, the former is responsible for the opening of the critical period, while the latter limits the plasticity in adult animals. Finally, we also provide a possible explanation for the variability in ocular dominance shifts observed in individual neurons and for the counter-intuitive shifts towards the closed eye.

During the development of the brain, visual cortex has a period of increased plasticity. Closing one eye for multiple days during this period can have a profound and life-long impact on neuronal responses. A well-established hypothesis is that the absolute level of inhibition regulates this period. In light of recent experimental results, we suggest an alternative theory. We propose that, in addition to the level of inhibition, synaptic plasticity onto inhibitory neurons is just as crucial. We propose a model which explains many observed phenomena into one single framework. Unlike theories considering only the level of inhibition, we can account for both the onset as well as the closure of this period. Furthermore, we also provide an explanation for the small fraction of neurons that show counter-intuitive behaviour and provide some testable predictions.

Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning

In blind people, the visual cortex takes on higher cognitive functions, including language. Why this functional reorganisation mechanistically emerges at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing features of anatomical structure, neurophysiological function and connectivity of fronto-temporal-occipital areas to simulate word-meaning acquisition in visually deprived and undeprived brains. We observed that, only under visual deprivation, distributed word-related neural circuits 'grew into' the deprived visual areas, which therefore adopted a linguistic-semantic role. Three factors are crucial for explaining this deprivation-related growth: changes in the network's activity balance brought about by the absence of uncorrelated sensory input, the connectivity structure of the network, and Hebbian correlation learning. In addition, the blind model revealed long-lasting spiking neural activity compared to the sighted model during word recognition, which is a neural correlate of enhanced verbal working memory. The present neurocomputational model offers a neurobiological account for neural changes following sensory deprivation, thus closing the gap between cellular-level mechanisms, system-level linguistic and semantic function.

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.

A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity

One of the most controversial debates in cognitive neuroscience concerns the cortical locus of semantic knowledge and processing in the human brain. Experimental data revealed the existence of various cortical regions relevant for meaning processing, ranging from semantic hubs generally involved in semantic processing to modality-preferential sensorimotor areas involved in the processing of specific conceptual categories. Why and how the brain uses such complex organization for conceptualization can be investigated using biologically constrained neurocomputational models. Here, we improve pre-existing neurocomputational models of semantics by incorporating spiking neurons and a rich connectivity structure between the model ‘areas’ to mimic important features of the underlying neural substrate. Semantic learning and symbol grounding in action and perception were simulated by associative learning between co-activated neuron populations in frontal, temporal and occipital areas. As a result of Hebbian learning of the correlation structure of symbol, perception and action information, distributed cell assembly circuits emerged across various cortices of the network. These semantic circuits showed category-specific topographical distributions, reaching into motor and visual areas for action- and visually-related words, respectively. All types of semantic circuits included large numbers of neurons in multimodal connector hub areas, which is explained by cortical connectivity structure and the resultant convergence of phonological and semantic information on these zones. Importantly, these semantic hub areas exhibited some category-specificity, which was less pronounced than that observed in primary and secondary modality-preferential cortices. The present neurocomputational model integrates seemingly divergent experimental results about conceptualization and explains both semantic hubs and category-specific areas as an emergent process causally determined by two major factors: neuroanatomical connectivity structure and correlated neuronal activation during language learning.

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules.

The SERCA2: A Gatekeeper of Neuronal Calcium Homeostasis in the Brain

Calcium (Ca²⁺) ions are prominent cell signaling regulators that carry information for a variety of cellular processes and are critical for neuronal survival and function. Furthermore, Ca²⁺ acts as a prominent second messenger that modulates divergent intracellular cascades in the nerve cells. Therefore, nerve cells have developed intricate Ca²⁺ signaling pathways to couple the Ca²⁺ signal to their biochemical machinery. Notably, intracellular Ca²⁺ homeostasis greatly relies on the rapid redistribution of Ca²⁺ ions into the diverse subcellular organelles which serve as Ca²⁺ stores, including the endoplasmic reticulum (ER). It is well established that Ca²⁺ released into the neuronal cytoplasm is pumped back into the ER by the sarco-/ER Ca²⁺ ATPase 2 (SERCA2), a P-type ion-motive ATPase that resides on the ER membrane. Even though the SERCA2 is constitutively expressed in nerve cells, its precise role in brain physiology and pathophysiology is not well-characterized. Intriguingly, SERCA2-dependent Ca²⁺ dysregulation has been implicated in several disorders that affect cognitive function, including Darier's disease, schizophrenia, Alzheimer's disease, and cerebral ischemia. The current review summarizes knowledge on the expression pattern of the different SERCA2 isoforms in the nervous system, and further discusses evidence of SERCA2 dysregulation in various neuropsychiatric disorders. To the best of our knowledge, this is the first literature review that specifically highlights the critical role of the SERCA2 in the brain. Advancing knowledge on the role of SERCA2 in maintaining neuronal Ca²⁺ homeostasis may ultimately lead to the development of safer and more effective pharmacotherapies to combat debilitating neuropsychiatric disorders.

The human pain system exhibits higher-order plasticity (metaplasticity)

The human pain system can be bidirectionally modulated by high-frequency (HFS; 100 Hz) and low-frequency (LFS; 1 Hz) electrical stimulation of nociceptors leading to long-term potentiation or depression of pain perception (pain-LTP or pain-LTD). Here we show that priming a test site by very low-frequency stimulation (VLFS; 0.05 Hz) prevented pain-LTP probably by elevating the threshold (set point) for pain-LTP induction. Conversely, prior HFS-induced pain-LTP was substantially reversed by subsequent VLFS, suggesting that preceding HFS had primed the human nociceptive system for pain-LTD induction by VLFS. In contrast, the pain elicited by the pain-LTP-precipitating conditioning HFS stimulation remained unaffected. In aggregate these experiments demonstrate that the human pain system expresses two forms of higher-order plasticity (metaplasticity) acting in either direction along the pain-LTD to pain-LTP continuum with similar shifts in thresholds for LTD and LTP as in synaptic plasticity, indicating intriguing new mechanisms for the prevention of pain memory and the erasure of hyperalgesia related to an already established pain memory trace. There were no apparent gender differences in either pain-LTP or metaplasticity of pain-LTP. However, individual subjects appeared to present with an individual balance of pain-LTD to pain-LTP (a pain plasticity "fingerprint").

Amyloid-Beta and Phosphorylated Tau Accumulations Cause Abnormalities at Synapses of Alzheimer's disease Neurons

Amyloid-beta (Aβ) and hyperphosphorylated tau are hallmark lesions of Alzheimer's disease (AD). However, the loss of synapses and dysfunctions of neurotransmission are more directly tied to disease severity. The role of these lesions in the pathoetiological progression of the disease remains contested. Biochemical, cellular, molecular, and pathological studies provided several lines of evidence and improved our understanding of how Aβ and hyperphosphorylated tau accumulation may directly harm synapses and alter neurotransmission. In vitro evidence suggests that Aβ and hyperphosphorylated tau have both direct and indirect cytotoxic effects that affect neurotransmission, axonal transport, signaling cascades, organelle function, and immune response in ways that lead to synaptic loss and dysfunctions in neurotransmitter release. Observations in preclinical models and autopsy studies support these findings, suggesting that while the pathoetiology of positive lesions remains elusive, their removal may reduce disease severity and progression. The purpose of this article is to highlight the need for further investigation of the role of tau in disease progression and its interactions with Aβ and neurotransmitters alike.

The Evolving Understanding of Dopamine Neurons in the Substantia Nigra and Ventral Tegmental Area

In recent years, the population of neurons in the ventral tegmental area (VTA) and substantia nigra (SN) has been examined at multiple levels. The results indicate that the projections, neurochemistry, and receptor and ion channel expression in this cell population vary widely. This review centers on the intrinsic properties and synaptic regulation that control the activity of dopamine neurons. Although all dopamine neurons fire action potentials in a pacemaker pattern in the absence of synaptic input, the intrinsic properties that underlie this activity differ considerably. Likewise, the transition into a burst/pause pattern results from combinations of intrinsic ion conductances, inhibitory and excitatory synaptic inputs that differ among this cell population. Finally, synaptic plasticity is a key regulator of the rate and pattern of activity in different groups of dopamine neurons. Through these fundamental properties, the activity of dopamine neurons is regulated and underlies the wide-ranging functions that have been attributed to dopamine.

Hebbian plasticity requires compensatory processes on multiple timescales

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.