A physiological basis for a theory of synapse modification

Epilepsy in Association with Giant Skull Base Tumors: A Retrospective Case Series of Giant Skull Base Tumors in the Anterior and Middle Fossa

Objective  This study aims to investigate the association between giant anterior and middle fossa skull base tumors and epilepsy, and implications for clinical management. Methods  A retrospective analysis was conducted on a cohort of patients diagnosed with anterior skull base tumors between 2016 and 2023. Radiologic data were used to identify tumors with major diameter larger than 5 cm involving skull base with intracranial involvement. Relevant demographic information, tumor characteristics, seizure type, seizure frequency, and treatment outcomes were analyzed. Results  Among the 236 patients diagnosed and operated with skull base tumors by senior author, 7.63% ( n  = 18) had giant skull base tumors of anterior and middle fossa, and 2.96% ( n  = 7) presented with concurrent epilepsy. Overall, in giant anterior and middle fossa skull base tumors, epilepsy was present in 38% of cases. The average age at operation was 47.56 ± 16.96, with 44.4% of cases being male and 55.6% of the cases being female. The majority of these cases exhibited focal epilepsy (71%), characterized by seizures originating from the proximity of the tumor location suggesting a potential correlation between tumor location and seizure generation. Of the remaining, 29% were generalized seizures. Tumors of the anterior fossa included 11 meningiomas, 3 pituitary adenomas, 1 chondrosarcoma, 1 hemangiopericytoma, 1 schwannoma, and 1 adenoid cyst carcinoma; half of which ( n  = 9) were of low grade. Conclusion  Our findings provide evidence of low frequency of epilepsy in skull base tumors in general, with an association among giant anterior and middle fossa skull base tumor and epilepsy.

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

Developmental Plasticity-Inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks

Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) and spiking neural networks (SNNs) draw little inspiration from brain's developmental plasticity mechanisms, thus limiting their ability to learn efficiently, rapidly, and accurately. This paper proposed a developmental plasticity-inspired adaptive pruning (DPAP) method, with inspiration from the adaptive developmental pruning of dendritic spines, synapses, and neurons according to the "use it or lose it, gradually decay" principle. The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with additional adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining. Extensive comparative experiments show consistent and remarkable performance and speed boost with the extremely compressed networks on a diverse set of benchmark tasks for deep ANNs and SNNs, especially the spatio-temporal joint pruning of SNNs in neuromorphic datasets. This work explores how developmental plasticity enables complex deep networks to gradually evolve into brain-like efficient and compact structures, eventually achieving state-of-the-art (SOTA) performance for biologically realistic SNNs.

Homeostatic regulation of brain activity: from endogenous mechanisms to homeostatic nanomachines

After the initial concepts of the constancy of the internal milieu or homeostasis, put forward by Claude Bernard and Walter Cannon, homeostasis emerged as a mechanism to control oscillations of biologically meaningful variables within narrow physiological ranges. This is a primary need in the central nervous system that is continuously subjected to a multitude of stimuli from the internal and external environments that affect its function and structure, allowing to adapt the individual to the ever-changing daily conditions. Preserving physiological levels of activity despite disturbances that could either depress neural computation or excessively stimulate neural activity is fundamental, and failure of these homeostatic mechanisms can lead to brain diseases. In this review, we cover the role and main mechanisms of homeostatic plasticity involving the regulation of excitability and synaptic strength from the single neuron to the network level. We analyze the relationships between homeostatic and Hebbian plasticity and the conditions under which the preservation of the excitatory/inhibitory balance fails, triggering epileptogenesis and eventually epilepsy. Several therapeutic strategies to cure epilepsy have been designed to strengthen homeostasis when endogenous homeostatic plasticity mechanisms have become insufficient or ineffective to contrast hyperactivity. We describe "on demand" gene therapy strategies, including optogenetics, chemogenetics, and chemo-optogenetics, and particularly focus on new closed loop sensor-actuator strategies mimicking homeostatic plasticity that can be endogenously expressed to strengthen the homeostatic defenses against brain diseases.

Transcranial magnetic stimulation for methamphetamine use disorder: A scoping review within the neurocircuitry model of addiction

The use of methamphetamine in the United States is increasing, contributing now to the "fourth wave" in the national opioid epidemic crisis. People who suffer from methamphetamine use disorder (MUD) have a higher risk of death. No pharmacological interventions are approved by the FDA and psychosocial interventions are only moderately effective. Transcranial Magnetic Stimulation (TMS) is a relatively novel FDA-cleared intervention for the treatment of Major Depressive Disorder (MDD) and other neuropsychiatric conditions. Several lines of research suggest that TMS could be useful for the treatment of addictive disorders, including MUD. We will review those published clinical trials that show potential effects on craving reduction of TMS when applied over the dorsolateral prefrontal cortex (DLPFC) also highlighting some limitations that affect their generalizability and applicability. We propose the use of the Koob and Volkow's neurocircuitry model of addiction as a frame to explain the brain effects of TMS in patients with MUD. We will finally discuss new venues that could lead to a more individualized and effective treatment of this complex disorder including the use of neuroimaging, the exploration of different areas of the brain such as the frontopolar cortex or the salience network and the use of biomarkers.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

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.

Long-term memory, synaptic plasticity and dopamine in rodent medial prefrontal cortex: Role in executive functions

Mnemonic functions, supporting rodent behavior in complex tasks, include both long-term and (short-term) working memory components. While working memory is thought to rely on persistent activity states in an active neural network, long-term memory and synaptic plasticity contribute to the formation of the underlying synaptic structure, determining the range of possible states. Whereas, the implication of working memory in executive functions, mediated by the prefrontal cortex (PFC) in primates and rodents, has been extensively studied, the contribution of long-term memory component to these tasks received little attention. This review summarizes available experimental data and theoretical work concerning cellular mechanisms of synaptic plasticity in the medial region of rodent PFC and the link between plasticity, memory and behavior in PFC-dependent tasks. A special attention is devoted to unique properties of dopaminergic modulation of prefrontal synaptic plasticity and its contribution to executive functions.

Metaplasticity framework for cross-modal synaptic plasticity in adults

Sensory loss leads to widespread adaptation of neural circuits to mediate cross-modal plasticity, which allows the organism to better utilize the remaining senses to guide behavior. While cross-modal interactions are often thought to engage multisensory areas, cross-modal plasticity is often prominently observed at the level of the primary sensory cortices. One dramatic example is from functional imaging studies in humans where cross-modal recruitment of the deprived primary sensory cortex has been observed during the processing of the spared senses. In addition, loss of a sensory modality can lead to enhancement and refinement of the spared senses, some of which have been attributed to compensatory plasticity of the spared sensory cortices. Cross-modal plasticity is not restricted to early sensory loss but is also observed in adults, which suggests that it engages or enables plasticity mechanisms available in the adult cortical circuit. Because adult cross-modal plasticity is observed without gross anatomical connectivity changes, it is thought to occur mainly through functional plasticity of pre-existing circuits. The underlying cellular and molecular mechanisms involve activity-dependent homeostatic and Hebbian mechanisms. A particularly attractive mechanism is the sliding threshold metaplasticity model because it innately allows neurons to dynamically optimize their feature selectivity. In this mini review, I will summarize the cellular and molecular mechanisms that mediate cross-modal plasticity in the adult primary sensory cortices and evaluate the metaplasticity model as an effective framework to understand the underlying mechanisms.

Metabotropic glutamate receptors and cognition: From underlying plasticity and neuroprotection to cognitive disorders and therapeutic targets

Metabotropic glutamate (mGlu) receptors are G protein-coupled receptors that play pivotal roles in mediating the activity of neurons and other cell types within the brain, communication between cell types, synaptic plasticity, and gene expression. As such, these receptors play an important role in a number of cognitive processes. In this chapter, we discuss the role of mGlu receptors in various forms of cognition and their underlying physiology, with an emphasis on cognitive dysfunction. Specifically, we highlight evidence that links mGlu physiology to cognitive dysfunction across brain disorders including Parkinson's disease, Alzheimer's disease, Fragile X syndrome, post-traumatic stress disorder, and schizophrenia. We also provide recent evidence demonstrating that mGlu receptors may elicit neuroprotective effects in particular disease states. Lastly, we discuss how mGlu receptors can be targeted utilizing positive and negative allosteric modulators as well as subtype specific agonists and antagonist to restore cognitive function across these disorders.

Biomolecular Markers of Brain Aging

Characterized by the gradual loss of physiological integrity, impaired function, and increased susceptibility to death, aging is considered the primary risk factor for major human diseases, such as cancer, diabetes, cardiovascular disorders, and neurodegenerative diseases. The time-dependent accumulation of cellular damage is widely considered the general cause of aging. While the mechanism of normal aging is still unresolved, researchers have identified different markers of aging, including genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. Theories of aging can be divided into two categories: (1) aging is a genetically programmed process, and (2) aging is a random process caused by gradual damage to the organism over time as a result of its vital activities. Aging affects the entire human body, and aging of the brain is undoubtedly different from all other organs, as neurons are highly differentiated postmitotic cells, and the lifespan of most neurons in the postnatal period is equal to the lifespan of the brain. In this chapter, we discuss the conserved mechanisms of aging that may underlie the changes observed in the aging brain, with a focus on mitochondrial function and oxidative stress, autophagy and protein turnover, insulin/IGF signaling, target of rapamycin (TOR) signaling, and sirtuin function.

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

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

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses

Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remarkable energy efficiency. Memristor is considered as one of the most promising candidates for the electronic synapse of the neuromorphic computing system due to its scalability, power efficiency and capability to simulate biological behaviors. Several memristor-based hardware demonstrations have been explored to achieve the capacity of unsupervised learning with the spike-rate-dependent plasticity (SRDP) learning rule. However, the learning capacity is limited and few of the memristor-based hardware demonstrations have explored the online unsupervised learning at the network level with an SRDP algorithm. Here, we construct a memristor-based hardware system and demonstrate the online unsupervised learning of SRDP networks. The neuromorphic system consists of multiple memristor arrays as the synapse and the discrete CMOS circuit unit as the neuron. Unsupervised learning and online weight update of 10 MNIST handwritten digits are realized by the constructed SRDP networks, and the recognition accuracy is above 90% with 20% device variation. This work paves the way towards the realization of large-scale and efficient networks for more complex tasks.

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. The porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag⁺ ion and electron diffusion pathways. Ag⁺ ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ∼95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

Role of NMDA Receptors in Adult Neurogenesis and Normal Development of the Dentate Gyrus

The NMDA receptors are a type of glutamate receptors, which is involved in neuronal function, plasticity and development in the mammalian brain. However, how the NMDA receptors contribute to adult neurogenesis and development of the dentate gyrus is unclear. In this study, we investigate this question by examining a region-specific knock-out mouse line that lacks the NR1 gene, which encodes the essential subunit of the NMDA receptors, in granule cells of the dentate gyrus (DG-NR1KO mice). We found that the survival of newly-generated granule cells, cell proliferation and the size of the granule cell layer are significantly reduced in the dorsal dentate gyrus of adult DG-NR1KO mice. Our results also show a significant reduction in the number of immature neurons and in the volume of the granule cell layer, starting from three weeks of postnatal age. DG-NR1KO mice also showed impairment in the expression of an immediate early gene, Arc, and behavior during the novelty-suppressed feeding and open field test. These results suggest that the NMDA receptors in granule cells have a role in adult neurogenesis in the adult brain and contributes to the normal development of the dentate gyrus.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

Cortical and Subcortical Circuits for Cross-Modal Plasticity Induced by Loss of Vision

Cortical areas are highly interconnected both via cortical and subcortical pathways, and primary sensory cortices are not isolated from this general structure. In primary sensory cortical areas, these pre-existing functional connections serve to provide contextual information for sensory processing and can mediate adaptation when a sensory modality is lost. Cross-modal plasticity in broad terms refers to widespread plasticity across the brain in response to losing a sensory modality, and largely involves two distinct changes: cross-modal recruitment and compensatory plasticity. The former involves recruitment of the deprived sensory area, which includes the deprived primary sensory cortex, for processing the remaining senses. Compensatory plasticity refers to plasticity in the remaining sensory areas, including the spared primary sensory cortices, to enhance the processing of its own sensory inputs. Here, we will summarize potential cellular plasticity mechanisms involved in cross-modal recruitment and compensatory plasticity, and review cortical and subcortical circuits to the primary sensory cortices which can mediate cross-modal plasticity upon loss of vision.

Naturalistic Spike Trains Drive State-Dependent Homeostatic Plasticity in Superficial Layers of Visual Cortex

The history of neural activity determines the synaptic plasticity mechanisms employed in the brain. Previous studies report a rapid reduction in the strength of excitatory synapses onto layer 2/3 (L2/3) pyramidal neurons of the primary visual cortex (V1) following two days of dark exposure and subsequent re-exposure to light. The abrupt increase in visually driven activity is predicted to drive homeostatic plasticity, however, the parameters of neural activity that trigger these changes are unknown. To determine this, we first recorded spike trains in vivo from V1 layer 4 (L4) of dark exposed (DE) mice of both sexes that were re-exposed to light through homogeneous or patterned visual stimulation. We found that delivering the spike patterns recorded in vivo to L4 of V1 slices was sufficient to reduce the amplitude of miniature excitatory postsynaptic currents (mEPSCs) of V1 L2/3 neurons in DE mice, but not in slices obtained from normal reared (NR) controls. Unexpectedly, the same stimulation pattern produced an up-regulation of mEPSC amplitudes in V1 L2/3 neurons from mice that received 2 h of light re-exposure (LE). A Poisson spike train exhibiting the same average frequency as the patterns recorded in vivo was equally effective at depressing mEPSC amplitudes in L2/3 neurons in V1 slices prepared from DE mice. Collectively, our results suggest that the history of visual experience modifies the responses of V1 neurons to stimulation and that rapid homeostatic depression of excitatory synapses can be driven by non-patterned input activity.

Model of theta frequency perturbations and contextual fear memory

Theta oscillations in the hippocampal local field potential (LFP) appear during translational movement and arousal, modulate the activity of principal cells, and are associated with spatial cognition and episodic memory function. All known anxiolytics slightly but consistently reduce hippocampal theta frequency. However, whether this electrophysiological effect is mechanistically related to the decreased behavioral expression of anxiety is currently unclear. Here, we propose that a reduction in theta frequency affects synaptic plasticity and mnemonic function and that this can explain the reduction in anxiety behavior. We test this hypothesis in a biophysical model of contextual fear conditioning. First, we confirm that our model reproduces previous empirical results regarding the dependence of synaptic plasticity on presynaptic firing rate. Next, we investigate how theta frequency during contextual conditioning impacts learning. These simulations demonstrate that learned associations between threat and context are attenuated when learning takes place under reduced theta frequency. Additionally, our simulations demonstrate that learned associations result in increased theta activity in the amygdala, consistent with empirical data. In summary, we propose a mechanism that can account for the behavioral effect of anxiolytics by impairing the integration of threat attributes of an environment into the cognitive map due to reduced synaptic potentiation.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

A unified computational model for cortical post-synaptic plasticity

Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.

The normal human hippocampus and that of Alzheimer’s patients contain filopodia like those of octopus neuropils and nerve growth cones

New synapses are generally formed in long-term learning [See (3) for references], but it is believed that short-term learning involves only alterations in synaptic efficacy with activity (1). We have studied this problem extensively in Octopus vulgaris (2-5). Electron Microscopic (EM) studies of serial sections reveal that filopodia like those in nerve growth cones occur in its touch learning neuropils (2). They increase in numbers transiently during learning and some form new synapses, while others retract. Calcium influx into neurites during activity triggers actin polymerization and related filopodial extension. This, by interpolation of new membranes, results in separation of interactive membranes, formerly in contact. An alteration in the total electrochemical interactions of the membranes results that alters the summation of interactions between all the membranes and changes the overall integrated output. This can be defined as learning and one of us has advanced a filopodial theory of learning based on these ideas (5). Accordingly, there is a localized reactivation of growth processes normally only operating during development to bring about structural alterations beginning with short-term learning. If correct, leaning should be blocked by cytochalasins B (CB) and D (CD) that interfere with actin polymerization and it should be stimulated by nerve growth factor (NGF). We have good evidence that CB and CD block touch learning reversibly in Octopus (2-5) and recently that NGF stimulates touch learning.

NMDARs Translate Sequential Temporal Information into Spatial Maps

Spatial representations of the sensory world are important for brain function. Timing is an essential component of sensory information. Many brain circuits transform the temporal sequence of input activity into spatial maps; however, the mechanisms underlying this transformation are unclear. Different N-methyl-D-aspartate receptor (NMDAR) response magnitudes result in synaptic potentiation or depression. We asked whether NMDAR response magnitude also affects the transformation of temporal information into directional spatial maps. We quantified retinotectal axon branch dynamics in Xenopus optic tectum in response to temporal sequences of visual stimulation. Reducing NMDAR responses by 50% inverts the spatial distribution of branch dynamics along the rostrocaudal axis in response to temporal patterns of input, suggesting that the magnitude of NMDAR signaling encodes the temporal sequence of inputs and translates the temporal code into a directional spatial map using structural plasticity-based branch dynamics. We discuss how this NMDAR-dependent decoding mechanism retrieves spatial information from sequential afferent activity.

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NMDAR response magnitude encodes the temporal sequence of inputs

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NMDAR mechanism decodes spatial information from sequential input activity

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NMDAR attenuation inverts the temporal to spatial transformation

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NMDAR activity alters the spatial distribution of dynamic and stable branches

Early Loss of Vision Results in Extensive Reorganization of Plasticity-Related Receptors and Alterations in Hippocampal Function That Extend Through Adulthood

Although by adulthood cortical structures and their capacity for processing sensory information have become established and stabilized, under conditions of cortical injury, or sensory deprivation, rapid reorganization occurs. Little is known as to the impact of this kind of adaptation on cellular processes related to memory encoding. However, imaging studies in humans suggest that following loss or impairment of a sensory modality, not only cortical but also subcortical structures begin to reorganize. It is likely that these processes are supported by neurotransmitter receptors that enable synaptic and cortical plasticity. Here, we explored to what extent the expression of plasticity-related proteins (GABA-A, GABA-B, GluN1, GluN2A, GluN2B) is altered following early vision loss, and whether this impacts on hippocampal function. We observed that in the period of 2-4 months postnatally in CBA/J-mice that experience hereditary postnatal retinal degeneration, systematic changes of GABA-receptor and NMDA-receptor subunit expression occurred that emerged first in the hippocampus and developed later in the cortex, compared to control mice that had normal vision. Changes were accompanied by significant impairments in hippocampal long-term potentiation and hippocampus-dependent learning. These data indicate that during cortical adaptation to early loss of vision, hippocampal information processing is compromised, and this status impacts on the acquisition of spatial representations.

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.

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence.

Mechanisms of Homeostatic Synaptic Plasticity in vivo

Synapses undergo rapid activity-dependent plasticity to store information, which when left uncompensated can lead to destabilization of neural function. It has been well documented that homeostatic changes, which operate at a slower time scale, are required to maintain stability of neural networks. While there are many mechanisms that can endow homeostatic control, sliding threshold and synaptic scaling are unique in that they operate by providing homeostatic control of synaptic strength. The former mechanism operates by adjusting the threshold for synaptic plasticity, while the latter mechanism directly alters the gain of synapses. Both modes of homeostatic synaptic plasticity have been studied across various preparations from reduced in vitro systems, such as neuronal cultures, to in vivo intact circuitry. While most of the cellular and molecular mechanisms of homeostatic synaptic plasticity have been worked out using reduced preparations, there are unique challenges present in intact circuitry in vivo, which deserve further consideration. For example, in an intact circuit, neurons receive distinct set of inputs across their dendritic tree which carry unique information. Homeostatic synaptic plasticity in vivo needs to operate without compromising processing of these distinct set of inputs to preserve information processing while maintaining network stability. In this mini review, we will summarize unique features of in vivo homeostatic synaptic plasticity, and discuss how sliding threshold and synaptic scaling may act across different activity regimes to provide homeostasis.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Effects of uninterrupted sinusoidal LF-EMF stimulation on LTP induced by different combinations of TBS/HFS at the Schaffer collateral-CA1 of synapses

Long-term potentiation (LTP) is an important aspect of synaptic plasticity and is one of the main mechanisms involved in memory. Low-frequency electromagnetic fields (LF-EMFs) such as transcranial magnetic stimulation are emerging neuromodulation tools for the regulation of LTP. However, whether LF-EMFs have different effects on different types of LTP has not yet been verified. Herein, we studied the regulatory effects of 15 Hz/2 mT sinusoidal magnetic field as pre-magnetic stimulation on several types of LTP, which were induced by theta-burst(TBS) or high-frequency stimulation (HFS) or some combination of them, and applied N-methyl-D-aspartate receptor(NMDAR) antagonists to observe the relationship between the regulation of LTP by LF-EMFs and NMDAR in the Schaffer collateral pathway of rat brain slices in vitro. The results presented in this paper are the performance of TBS and HFS was not exactly the same and the recovery speed of TBS-LTP was faster than HFS-LTP after receiving the regulation of LF-EMFs; moreover, the LTP level was affected by the order of combination and the effect of pre-magnetic stimulation could maintain the entire process of the combined induction experiment, while NMDAR antagonists could not completely offset the influence of LF-EMFs. The memory patterns are diverse, and this study has shown LF-EMFs can regulate LTP such as TBS-LTP and HFS-LTP and can continuously affect multiple LTP induction processes. However, different memory processes may have different performance in the face of LF-EMFs regulation. In terms of the mechanism of LF-EMFs-induced LTP regulation, NMDARs may be involved in the process of LF-EMF regulation of LTP, but are not the only factor.

Input-Specific Metaplasticity in the Visual Cortex Requires Homer1a-Mediated mGluR5 Signaling

Effective sensory processing depends on sensory experience-dependent metaplasticity, which allows homeostatic maintenance of neural network activity and preserves feature selectivity. Following a strong increase in sensory drive, plasticity mechanisms that decrease the strength of excitatory synapses are preferentially engaged to maintain stability in neural networks. Such adaptation has been demonstrated in various model systems, including mouse primary visual cortex (V1), where excitatory synapses on layer 2/3 (L2/3) neurons undergo rapid reduction in strength when visually deprived mice are reexposed to light. Here, we report that this form of plasticity is specific to intracortical inputs to V1 L2/3 neurons and depends on the activity of NMDA receptors (NMDARs) and group I metabotropic glutamate receptor 5 (mGluR5). Furthermore, we found that expression of the immediate early gene (IEG) Homer1a (H1a) and its subsequent interaction with mGluR5s are necessary for this input-specific metaplasticity.

Novel Players in the Aging Synapse: Impact on Cognition

While neuronal loss has long been considered as the main contributor to age-related cognitive decline, these alterations are currently attributed to gradual synaptic dysfunction driven by calcium dyshomeostasis and alterations in ionotropic/metabotropic receptors. Given the key role of the hippocampus in encoding, storage, and retrieval of memory, the morpho- and electrophysiological alterations that occur in the major synapse of this network-the glutamatergic-deserve special attention. We guide you through the hippocampal anatomy, circuitry, and function in physiological context and focus on alterations in neuronal morphology, calcium dynamics, and plasticity induced by aging and Alzheimer's disease (AD). We provide state-of-the art knowledge on glutamatergic transmission and discuss implications of these novel players for intervention. A link between regular consumption of caffeine-an adenosine receptor blocker-to decreased risk of AD in humans is well established, while the mechanisms responsible have only now been uncovered. We review compelling evidence from humans and animal models that implicate adenosine A2A receptors (A2AR) upsurge as a crucial mediator of age-related synaptic dysfunction. The relevance of this mechanism in patients was very recently demonstrated in the form of a significant association of the A2AR-encoding gene with hippocampal volume (synaptic loss) in mild cognitive impairment and AD. Novel pathways implicate A2AR in the control of mGluR5-dependent NMDAR activation and subsequent Ca2+ dysfunction upon aging. The nature of this receptor makes it particularly suited for long-term therapies, as an alternative for regulating aberrant mGluR5/NMDAR signaling in aging and disease, without disrupting their crucial constitutive activity.

Interhemispheric plasticity is mediated by maximal potentiation of callosal inputs

The corpus callosum is a large fiber bundle which connects contralateral brain regions. After unilateral perturbations such as stroke or amputation, interhemispheric connectivity is altered and often leads to bilateral somatomotor cortical hyperactivity in patients with poor recovery. This study reports that callosal targeting of deprived layer 5 neurons is maximally potentiated in mouse primary somatosensory barrel cortex after unilateral whisker denervation. These neurons also experience an increase in excitability and spontaneous excitatory amplitudes. These results should be relevant to the cortical responses observed in human patients after unilateral nerve transection, amputation, or stroke.

Central or peripheral injury causes reorganization of the brain’s connections and functions. A striking change observed after unilateral stroke or amputation is a recruitment of bilateral cortical responses to sensation or movement of the unaffected peripheral area. The mechanisms underlying this phenomenon are described in a mouse model of unilateral whisker deprivation. Stimulation of intact whiskers yields a bilateral blood-oxygen-level−dependent fMRI response in somatosensory barrel cortex. Whole-cell electrophysiology demonstrated that the intact barrel cortex selectively strengthens callosal synapses to layer 5 neurons in the deprived cortex. These synapses have larger AMPA receptor- and NMDA receptor-mediated events. These factors contribute to a maximally potentiated callosal synapse. This potentiation occludes long-term potentiation, which could be rescued, to some extent, with prior long-term depression induction. Excitability and excitation/inhibition balance were altered in a manner consistent with cell-specific callosal changes and support a shift in the overall state of the cortex. This is a demonstration of a cell-specific, synaptic mechanism underlying interhemispheric cortical reorganization.

Experience-Dependent Development of Feature-Selective Synchronization in the Primary Visual Cortex

Early visual experience is essential for the maturation of visual functions in which the primary visual cortex plays crucial roles. The extraction of visual features based on response selectivity of individual neurons, a fundamental process in the cortex, is basically established by eye opening in rodents, suggesting that visual experience is required for the development of neural functions other than feature extraction. Here, we show that synchronized firing, which is important for visual information processing, occurs selectively in adjacent neurons sharing similar orientation or spatial frequency preferences in layers 2-4 (upper layer) of rat visual cortex. This feature-selective spike synchrony was rudimentary when the eyes opened and became prominent during the first few weeks after eye opening only in the presence of pattern vision. In contrast, synchronization in layers 5-6 (lower layer) was almost independent of orientation similarity and more weakly dependent on spatial frequency similarity compared with upper layer synchrony. Lower layer synchronization was strengthened during development after eye opening independently of visual experience as a whole. However, the feature selectivity of synchronization was regulated by visual inputs, whereas the inputs without contours were sufficient for this regulation. Therefore, we speculate that feature-selective synchronization in the upper layer may convey detailed information on visual objects to the higher-order cortex, whereas weakly feature-selective synchronization in the lower layer may covey rather rough visual information to the subcortical areas or higher-order cortex. A major role of visual experience may be to establish the specific neural circuits underlying highly feature-selective synchronization.SIGNIFICANCE STATEMENT The neuronal mechanisms underlying experience-dependent improvement of visual functions still remain unresolved. In this study, we investigated whether early visual experience contributes to the development of synchronized neural firing in the primary visual cortex, which plays important roles in visual information processing. We found that synchronized firing depends more remarkably on the similarity of preferred visual stimuli in the upper than lower layer neurons. Pattern vision during development was required for the establishment of spike synchrony in the upper but not the lower layer. These findings provide a new view regarding the role of sensory experience in the functional development of the cortex and the differences in the modes of information processing in the upper and lower cortical layers.

Radonjić N, Zecevic N. N-Methyl d-Aspartate Receptor Expression Patterns in the Human Fetal Cerebral Cortex

N-methyl d-aspartate receptors (NMDARs), a subtype of glutamate receptor, have important functional roles in cellular activity and neuronal development. They are well-studied in rodent and adult human brains, but limited information is available about their distribution in the human fetal cerebral cortex. Here we show that 3 NMDAR subunits, NR1, NR2A, and NR2B, are expressed in the human cerebral cortex during the second trimester of gestation, a period of intense neurogenesis and synaptogenesis. With increasing fetal age, expression of the NMDAR-encoding genes Grin1 (NR1) and Grin2a (NR2A) increased while Grin2b (NR2B) expression decreased. The protein levels of all 3 subunits paralleled the changes in gene expression. On cryosections, all 3 subunits were expressed in proliferative ventricular and subventricular zones, in radial glia, and in intermediate progenitor cells, consistent with their role in the proliferation of cortical progenitor cells and in the determination of their respective fates. The detection of NR1, NR2A, and NR2B in both glutamatergic and GABAergic neurons of the cortical plate suggests the involvement of NMDARs in the maturation of human cortical neurons and in early synapse formation. Our results and previous studies in rodents suggest that NMDAR expression in the developing human brain is evolutionarily conserved.

Interactions between synaptic homeostatic mechanisms: an attempt to reconcile BCM theory, synaptic scaling, and changing excitation/inhibition balance

Homeostatic plasticity is proposed to be mediated by synaptic changes, such as synaptic scaling and shifts in the excitation/inhibition balance. These mechanisms are thought to be separate from the Bienenstock, Cooper, Munro (BCM) learning rule, where the threshold for the induction of long-term potentiation and long-term depression slides in response to changes in activity levels. Yet, both sets of mechanisms produce a homeostatic response of a relative increase (or decrease) in strength of excitatory synapses in response to overall activity-level changes. Here we review recent studies, with a focus on in vivo experiments, to re-examine the overlap and differences between these two mechanisms and we suggest how they may interact to facilitate firing-rate homeostasis, while maintaining functional properties of neurons.

Global sensitivity analysis of a model related to memory formation in synapses: Model reduction based on epistemic parameter uncertainties and related issues

We investigate the epistemic uncertainties of parameters of a mathematical model that describes the dynamics of CaMKII-NMDAR complex related to memory formation in synapses using global sensitivity analysis (GSA). The model, which was published in this journal, is nonlinear and complex with Ca²⁺ patterns with different level of frequencies as inputs. We explore the effects of parameter on the key outputs of the model to discover the most sensitive ones using GSA and partial ranking correlation coefficient (PRCC) and to understand why they are sensitive and others are not based on the biology of the problem. We also extend the model to add presynaptic neurotransmitter vesicles release to have action potentials as inputs of different frequencies. We perform GSA on this extended model to show that the parameter sensitivities are different for the extended model as shown by PRCC landscapes. Based on the results of GSA and PRCC, we reduce the original model to a less complex model taking the most important biological processes into account. We validate the reduced model against the outputs of the original model. We show that the parameter sensitivities are dependent on the inputs and GSA would make us understand the sensitivities and the importance of the parameters. A thorough phenomenological understanding of the relationships involved is essential to interpret the results of GSA and hence for the possible model reduction.

Regulation of fear extinction by long-term depression: The roles of endocannabinoids and brain derived neurotrophic factor

The extinction of a conditioned fear response is of great interest in the search for a means of ameliorating adverse neurobiological changes resulting from stress. The discovery that endocannibinoid (EC) levels are inversely related to the extent of such stress, and that the amygdala is a primary site mediating stress, suggests that ECs in this brain region might play a major role in extinction. Supporting this are the observations that the basolateral complex of the amygdala shows an increase in ECs only during extinction and that early clinical trials indicate that cannabinoid-like agents, when taken orally by patients suffering from post traumatic stress disorder (PTSD), reduce insomnia and nightmares. In order to optimize the potential of these agents to ameliorate symptoms of PTSD four important questions need to be answered: first, what is the identity of the cells that release ECs in the amygdala during extinction; second, what are their sites of action; third, what roles do the ECs play in the alleviation of long- depression (LTD), a process central to extinction; and finally, to what extent does brain derived neurotrophic factor (BDNF) facilitate the release of ECs? A review of the relevant literature is presented in an attempt to answer these questions. It is suggested that the principal cell involved in EC synthesis and release during extinction is the so-called excitatory extinction neuron in the basal nucleus of the amygdala. Furthermore that the main site of action of the ECs is the adjacent calcitonin gene-related peptide inhibitory interneurons, whose normal role of blocking the excitatory neurons is greatly diminished. The molecular pathways leading (during extinction trials) to the synthesis and release of ECs from synaptic spines of extinction neurons, that is potentiated by BDNF, are also delineated in this review. Finally, consideration is given to how the autocrine action of BDNF, linked to the release of ECs, can lead to the sustained release of these, so maintaining extinction over long times.

Calcium threshold shift enables frequency-independent control of plasticity by an instructive signal

At glutamatergic synapses, both long-term potentiation (LTP) and long-term depression (LTD) can be induced at the same synaptic activation frequency. Instructive signals determine whether LTP or LTD is induced, by modulating local calcium transients. Synapses maintain the ability to potentiate or depress over a wide frequency range, but it remains unknown how calcium-controlled plasticity operates when frequency variations alone cause differences in calcium amplitudes. We addressed this problem at cerebellar parallel fiber-Purkinje cell synapses, which can undergo LTD or LTP in response to 1-Hz and 100-Hz stimulation. We observed that high-frequency activation elicits larger spine calcium transients than low-frequency stimulation under all stimulus conditions, but, regardless of activation frequency, climbing fiber (CF) coactivation provides an instructive signal that further enhances calcium transients and promotes LTD. At both frequencies, buffering calcium prevents LTD induction and LTP results instead, identifying the enhanced calcium signal amplitude as the critical parameter contributed by the instructive CF signal. These observations show that it is not absolute calcium amplitudes that determine whether LTD or LTP is evoked but, instead, the LTD threshold slides, thus preserving the requirement for relatively larger calcium transients for LTD than for LTP induction at any given stimulus frequency. Cerebellar LTD depends on the activation of calcium/calmodulin-dependent kinase II (CaMKII). Using genetically modified (TT305/6VA and T305D) mice, we identified α-CaMKII inhibition upon autophosphorylation at Thr305/306 as a molecular event underlying the threshold shift. This mechanism enables frequency-independent plasticity control by the instructive CF signal based on relative, not absolute, calcium thresholds.

REVIEW ■ : Reorganization of Sensory Systems of Primates after Injury

The orderly representations of sensory surfaces in the brains of adult mammals have the capacity to reor ganize after injuries that deprive these representations of some of their normal sources of activation. Such reorganizations can be produced by injury that occurs peripherally, such as nerve damage or amputation, or after injury to the CNS, such as spinal cord damage or cortical lesion. These changes likely are mediated by a number of different mechanisms, and can be extensive and involve the growth of new connections. Finally, some types of reorganizations may help mediate the recovery of lost functions, whereas others may lead to sensory abnormalities and perceptual errors. NEUROSCIENTIST 3:123-130, 1997

Homeostatic Plasticity Achieved by Incorporation of Random Fluctuations and Soft-Bounded Hebbian Plasticity in Excitatory Synapses

Homeostatic plasticity is considered to maintain activity in neuronal circuits within a functional range. In the absence of homeostatic plasticity neuronal activity is prone to be destabilized because Hebbian plasticity mechanisms induce positive feedback change. Several studies on homeostatic plasticity assumed the existence of a process for monitoring neuronal activity on a time scale of hours and adjusting synaptic efficacy by scaling up and down. However, the underlying mechanism still remains unclear. Excitatory synaptic efficacy is associated with the size of the dendritic spine, and dendritic spine size fluctuates even after neuronal activity is silenced. These fluctuations could be a non-Hebbian form of synaptic plasticity that serves such a homeostatic function. This study proposed and analyzed a synaptic plasticity model incorporating random fluctuations and soft-bounded Hebbian plasticity at excitatory synapses, and found that the proposed model can prevent excessive changes in neuronal activity by scaling synaptic efficacy up and down. Soft-bounded Hebbian plasticity suppresses strong synapses, thereby scaling synapses down and preventing runaway excitation. Random fluctuations diffuse synaptic efficacy, thereby scaling synapses up and preventing neurons from falling silent. The proposed model acts as a form of homeostatic plasticity, regardless of neuronal activity monitoring.

Modelling bidirectional modulations in synaptic plasticity: A biochemical pathway model to understand the emergence of long term potentiation (LTP) and long term depression (LTD)

Synaptic plasticity induces bidirectional modulations of the postsynaptic response following a synaptic transmission. The long term forms of synaptic plasticity, named long term potentiation (LTP) and long term depression (LTD), are critical for the antithetic functions of the memory system, memory formation and removal, respectively. A common Ca(2+) signalling upstream triggers both LTP and LTD, and the critical proteins and factors coordinating the LTP/LTD inductions are not well understood. We develop an integrated model based on the sub-models of the indispensable synaptic proteins in the emergence of synaptic plasticity to validate and understand their potential roles in the expression of synaptic plasticity. The model explains Ca(2+)/calmodulin (CaM) complex dependent coordination of LTP/LTD expressions by the interactions among the indispensable proteins using the experimentally estimated kinetic parameters. Analysis of the integrated model provides us with insights into the effective timescales of the key proteins and we conclude that the CaM pool size is critical for the coordination between LTP/LTD expressions.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

Requirement of keratan sulfate proteoglycan phosphacan with a specific sulfation pattern for critical period plasticity in the visual cortex

Proteoglycans play important roles in regulating the development and functions of the brain. They consist of a core protein and glycosaminoglycans, which are long sugar chains of repeating disaccharide units with sulfation. A recent study demonstrated that the sulfation pattern of chondroitin sulfate on proteoglycans contributes to regulation of the critical period of experience-dependent plasticity in the mouse visual cortex. In the present study, we investigated the role of keratan sulfate (KS), another glycosaminoglycan, in critical period plasticity in the mouse visual cortex. Immunohistochemical analyses demonstrated the presence of KS containing disaccharide units of N-acetylglucosamine (GlcNAc)-6-sulfate and nonsulfated galactose during the critical period, although KS containing disaccharide units of GlcNAc-6-sulfate and galactose-6-sulfate was already known to disappear before that period. The KS chains were distributed diffusely in the extracellular space and densely around the soma of a large population of excitatory and inhibitory neurons. Electron microscopic analysis revealed that the KS was localized within the perisynaptic spaces and dendrites but not in presynaptic sites. KS was mainly located on phosphacan. In mice deficient in GlcNAc-6-O-sulfotransferase 1, which is one of the enzymes necessary for the synthesis of KS chains, the expression of KS was one half that in wild-type mice. In the knockout mice, monocular deprivation during the critical period resulted in a depression of deprived-eye responses but failed to produce potentiation of nondeprived-eye responses. In addition, T-type Ca(2+) channel-dependent long-term potentiation (LTP), which occurs only during the critical period, was not observed. These results suggest that regulation by KS-phosphacan with a specific sulfation pattern is necessary for the generation of LTP and hence the potentiation of nondeprived-eye responses after monocular deprivation.

Defective Age-Dependent Metaplasticity in a Mouse Model of Alzheimer's Disease

Much of the molecular understanding of synaptic pathology in Alzheimer's disease (AD) comes from studies of various mouse models that express familial AD (FAD)-linked mutations, often in combinations. Most studies compare the absolute magnitudes of long-term potentiation (LTP) and long-term depression (LTD) to assess deficits in bidirectional synaptic plasticity accompanying FAD-linked mutations. However, LTP and LTD are not static, but their induction threshold is adjusted by overall neural activity via metaplasticity. Hence LTP/LTD changes in AD mouse models may reflect defects in metaplasticity processes. To determine this, we examined the LTP/LTD induction threshold in APPswe;PS1ΔE9 transgenic (Tg) mice across two different ages. We found that in young Tg mice (1 month), LTP is enhanced at the expense of LTD, but in adults (6 months), the phenotype is reversed to promote LTD and reduce LTP, compared to age-matched wild-type (WT) littermates. The apparent opposite phenotype across age was due to an initial offset in the induction threshold to favor LTP and the inability to undergo developmental metaplasticity in Tg mice. In WTs, the synaptic modification threshold decreased over development to favor LTP and diminish LTD in adults. However, in Tg mice, the magnitudes of LTP and LTD stayed constant across development. The initial offset in LTP/LTD threshold in young Tg mice did not accompany changes in the LTP/LTD induction mechanisms, but altered AMPA receptor phosphorylation and appearance of Ca(2+)-permeable AMPA receptors. We propose that the main synaptic defect in AD mouse models is due to their inability to undergo developmental metaplasticity.

SIGNIFICANCE STATEMENT

This work offers a new insight that metaplasticity defects are central to synaptic dysfunctions seen in AD mouse models. In particular, we demonstrate that the apparent differences in LTP/LTD magnitude seen across ages in AD transgenic mouse models reflect the inability to undergo a normal developmental shift in metaplasticity.

Frequency-dependent learning achieved using semiconducting polymer/electrolyte composite cells

Frequency-dependent learning has been achieved using semiconducting polymer/electrolyte composite cells. The cells composed of polymer/electrolyte double layers realized the conventional spike-rate-dependent plasticity (SRDP) learning model. These cells responded to depression upon low-frequency stimulation and to potentiation upon high-frequency stimulation and presented long-term memory. The transition threshold θm from depression to potentiation varied depending on the previous stimulations. A nanostructure resembling a bio-synapse in its transport passages was demonstrated and a random channel model was proposed to describe the ionic kinetics at the polymer/electrolyte interface during and after stimulations with various frequencies, accounting for the observed SRDP.