Models of Metaplasticity: A Review of Concepts

Rethinking plasticity: Analysing the concept of "destructive plasticity" in the light of neuroscience definitions

As a multilevel and multidisciplinary field, neuroscience is designed to interact with various branches of natural and applied sciences as well as with humanities and philosophy. The continental tradition in philosophy, particularly over the past 20 years, tended to establish strong connections with biology and neuroscience findings. This cross fertilization can however be impeded by conceptual intricacies, such as those surrounding the concept of plasticity. The use of this concept has broadened as scientists applied it to explore an ever-growing range of biological phenomena. Here, we examine the consequences of this ambiguity in an interdisciplinary context through the analysis of the concept of "destructive plasticity" in the philosophical writings of Catherine Malabou. The term "destructive plasticity" was coined by Malabou in 2009 to refer to all processes leading to psycho-cognitive and emotional alterations following traumatic or nontraumatic brain injuries or resulting from neurodevelopmental disorders. By comparing it with the neuroscientific definitions of plasticity, we discuss the epistemological obstacles and possibilities related to the integration of this concept into neuroscience. Improving interdisciplinary exchanges requires an advanced and sophisticated manipulation of neurobiological concepts. These concepts are not only intended to guide research programmes within neuroscience but also to organize and frame the dialogue between different theoretical backgrounds.

Synaptic weight dynamics underlying memory consolidation: implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We propose two biologically plausible implementations for this reset that suggest novel roles for core elements of the cerebellar circuit.

Significance Statement

How are memories transformed over time? We propose a simple organizing principle for how long term memories are moved from an initial to a final site of storage. We show that successful transfer occurs when the late site of memory storage is endowed with synaptic plasticity rules that stably accumulate changes in activity occurring at the early site of memory storage. We instantiate this principle in a simple computational model that is representative of brain circuits underlying a variety of behaviors. The model suggests how a neural circuit can store new memories while preserving core features of older ones, and suggests novel roles for core elements of the cerebellar circuit.

Systemic pharmacological suppression of neural activity reverses learning impairment in a mouse model of Fragile X syndrome

The enhancement of associative synaptic plasticity often results in impaired rather than enhanced learning. Previously, we proposed that such learning impairments can result from saturation of the plasticity mechanism (Nguyen-Vu et al., 2017), or, more generally, from a history-dependent change in the threshold for plasticity. This hypothesis was based on experimental results from mice lacking two class I major histocompatibility molecules, MHCI H2-Kb and H2-Db (MHCI KbDb-/-), which have enhanced associative long-term depression at the parallel fiber-Purkinje cell synapses in the cerebellum (PF-Purkinje cell LTD). Here, we extend this work by testing predictions of the threshold metaplasticity hypothesis in a second mouse line with enhanced PF-Purkinje cell LTD, the Fmr1 knockout mouse model of Fragile X syndrome (FXS). Mice lacking Fmr1 gene expression in cerebellar Purkinje cells (L7-Fmr1 KO) were selectively impaired on two oculomotor learning tasks in which PF-Purkinje cell LTD has been implicated, with no impairment on LTD-independent oculomotor learning tasks. Consistent with the threshold metaplasticity hypothesis, behavioral pre-training designed to reverse LTD at the PF-Purkinje cell synapses eliminated the oculomotor learning deficit in the L7-Fmr1 KO mice, as previously reported in MHCI KbDb-/-mice. In addition, diazepam treatment to suppress neural activity and thereby limit the induction of associative LTD during the pre-training period also eliminated the learning deficits in L7-Fmr1 KO mice. These results support the hypothesis that cerebellar LTD-dependent learning is governed by an experience-dependent sliding threshold for plasticity. An increased threshold for LTD in response to elevated neural activity would tend to oppose firing rate stability, but could serve to stabilize synaptic weights and recently acquired memories. The metaplasticity perspective could inform the development of new clinical approaches for addressing learning impairments in autism and other disorders of the nervous system.

Age-dependent Effects of Dopamine on Working Memory and Synaptic Plasticity in Hippocampal CA3-CA1 Synapses in Mice

Normal aging in mammals is accompanied by a decline in learning and memory. Dopamine plays a vital role in regulating cognitive functions, but it declines with age: During non-pathological aging, dopamine levels, receptors, and transporters decrease. Regarding the role of the dopaminergic system's changes in old age, we examined the effect of age and applied dopamine on working memory, synaptic transmission, and long-term potentiation (LTP) induction and maintenance in young adult and mature adult mice. We employed the Y-maze spontaneous alteration test to evaluate working memory. Maturation had no observed effect on working memory performance. Interestingly, working memory performance increased following intracerebroventricular administration of dopamine only in mature adult mice. We employed evoked field potential recording (in vitro) to assess the effects of age and maturation on the long-term potentiation (LTP) induction and maintenance. There was no difference in LTP induction and maintenance between young and mature adult mice before dopamine application. However, the application of dopamine on mature adult murine slices increased LTP magnitude compared to slices from young adults. According to the obtained results, it may be concluded that hippocampal neural excitability increased in mature adult subjects, and application of dopamine abolished the difference in neural excitability among young mature and adult mature groups; which was accompanied with increment of working memory and synaptic potentiation in mature adult animals.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

A comprehensive review of transcranial magnetic stimulation in secondary dementia

Although primary degenerative diseases are the main cause of dementia, a non-negligible proportion of patients is affected by a secondary and potentially treatable cognitive disorder. Therefore, diagnostic tools able to early identify and monitor them and to predict the response to treatment are needed. Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiological technique capable of evaluating in vivo and in “real time” the motor areas, the cortico-spinal tract, and the neurotransmission pathways in several neurological and neuropsychiatric disorders, including cognitive impairment and dementia. While consistent evidence has been accumulated for Alzheimer’s disease, other degenerative cognitive disorders, and vascular dementia, to date a comprehensive review of TMS studies available in other secondary dementias is lacking. These conditions include, among others, normal-pressure hydrocephalus, multiple sclerosis, celiac disease and other immunologically mediated diseases, as well as a number of inflammatory, infective, metabolic, toxic, nutritional, endocrine, sleep-related, and rare genetic disorders. Overall, we observed that, while in degenerative dementia neurophysiological alterations might mirror specific, and possibly primary, neuropathological changes (and hence be used as early biomarkers), this pathogenic link appears to be weaker for most secondary forms of dementia, in which neurotransmitter dysfunction is more likely related to a systemic or diffuse neural damage. In these cases, therefore, an effort toward the understanding of pathological mechanisms of cognitive impairment should be made, also by investigating the relationship between functional alterations of brain circuits and the specific mechanisms of neuronal damage triggered by the causative disease. Neurophysiologically, although no distinctive TMS pattern can be identified that might be used to predict the occurrence or progression of cognitive decline in a specific condition, some TMS-associated measures of cortical function and plasticity (such as the short-latency afferent inhibition, the short-interval intracortical inhibition, and the cortical silent period) might add useful information in most of secondary dementia, especially in combination with suggestive clinical features and other diagnostic tests. The possibility to detect dysfunctional cortical circuits, to monitor the disease course, to probe the response to treatment, and to design novel neuromodulatory interventions in secondary dementia still represents a gap in the literature that needs to be explored.

Formation and computational implications of assemblies in neural circuits

In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.

Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states.

Metaplastic Reinforcement of Long-Term Potentiation in Hippocampal Area CA2 by Cholinergic Receptor Activation

Hippocampal CA2, an inconspicuously positioned area between the well-studied CA1 and CA3 subfields, has captured research interest in recent years because of its role in social memory formation. However, the role of cholinergic inputs to the CA2 area for the regulation of synaptic plasticity remains to be fully understood. We show that cholinergic receptor activation with the nonselective cholinergic agonist, carbachol (CCh), triggers a protein synthesis-dependent and NMDAR-independent long-term synaptic depression (CCh-LTD) at entorhinal cortical (EC)-CA2 and Schaffer collateral (SC)-CA2 synapses in the hippocampus of adult male Wistar rats. The activation of muscarinic acetylcholine receptors (mAChRs) is critical for the induction of CCh-LTD with the results suggesting an involvement of M3 and M1 mAChRs in the early facilitation of CCh-LTD, while nicotinic AChR activation plays a role in the late maintenance of CCh-LTD at CA2 synapses. Remarkably, we find that CCh priming lowers the threshold for the subsequent induction of persistent long-term potentiation (LTP) of synaptic transmission at EC-CA2 and the plasticity-resistant SC-CA2 pathways. The effects of such a cholinergic-dependent synaptic depression on subsequent LTP at EC-CA2 and SC-CA2 synapses have not been previously explored. Collectively, the results demonstrate that CA2 synaptic learning rules are regulated in a metaplastic manner, whereby modifications triggered by prior cholinergic stimulation can dictate the outcome of future plasticity events. Moreover, the reinforcement of LTP at EC inputs to CA2 following the priming stimulus coexists with concurrent sustained CCh-LTD at the SC-CA2 pathway and is dynamically scaled by modulation of SC-CA2 synaptic transmission.SIGNIFICANCE STATEMENT The release of the neuromodulator acetylcholine is critically involved in processes of hippocampus-dependent memory formation. Cholinergic afferents originating in the medial septum and diagonal bands of Broca terminating in the hippocampal area CA2 might play an important role in the modulation of area-specific synaptic plasticity. Our findings demonstrate that cholinergic receptor activation induces an LTD of synaptic transmission at entorhinal cortical- and Schaffer collateral-CA2 synapses. This cholinergic activation-mediated LTD displays a bidirectional metaplastic switch to LTP on a future timescale. This suggests that such bidirectional synaptic modifications triggered by the dynamic modulation of tonic cholinergic receptor activation may support the formation of CA2-dependent memories given the increased hippocampal cholinergic tone during active wakefulness observed in exploratory behavior.

Metaplasticity in the human swallowing system: clinical implications for dysphagia rehabilitation

Dysphagia is a common and devastating complication following brain damage. Over the last 2 decades, dysphagia treatments have shifted from compensatory to rehabilitative strategies that facilitate neuroplasticity, which is the reorganization of neural networks that is essential for functional recovery. Moreover, there is growing interest in the application of cortical and peripheral neurostimulation to promote such neuroplasticity. Despite some preliminary positive findings, the variability in responsiveness toward these treatments remains substantial. The purpose of this review is to summarize findings on the effects of neurostimulation in promoting neuroplasticity for dysphagia rehabilitation and highlight the need to develop more effective treatment strategies. We then discuss the role of metaplasticity, a homeostatic mechanism of the brain to regulate plasticity changes, in helping to drive neurorehabilitation. Finally, a hypothesis on how metaplasticity could be applied in dysphagia rehabilitation to enhance treatment outcomes is proposed.

Dynamic Interplay between Lower-Grade Glioma Instability and Brain Metaplasticity: Proposal of an Original Model to Guide the Therapeutic Strategy

The behavior of lower-grade glioma (LGG) is changing over time, spontaneously, and in reaction to treatments. First, due to genomic instability and clonal expansion, although LGG progresses slowly during the early period of the disease, its growth velocity will accelerate when this tumor will transform to a higher grade of malignancy. Furthermore, its pattern of progression may change following therapy, e.g., by switching from a proliferative towards a more diffuse profile, in particular after surgical resection. In parallel to this plasticity of the neoplasm, the brain itself is constantly adapting to the tumor and possible treatment(s) thanks to reconfiguration within and between neural networks. Furthermore, the pattern of reallocation can also change, especially by switching from a perilesional to a contrahemispheric functional reorganization. Such a reorientation of mechanisms of cerebral reshaping, related to metaplasticity, consists of optimizing the efficiency of neural delocalization in order to allow functional compensation by adapting over time the profile of circuits redistribution to the behavioral modifications of the glioma. This interplay between LGG mutations and reactional connectomal instability leads to perpetual modulations in the glioma-neural equilibrium, both at ultrastructural and macroscopic levels, explaining the possible preservation of quality of life despite tumor progression. Here, an original model of these dynamic interactions across LGG plasticity and the brain metanetwork is proposed to guide a tailored step-by-step individualized therapeutic strategy over years. Integration of these new parameters, not yet considered in the current guidelines, might improve management of LGG patients.

Nonlinear Dendritic Coincidence Detection for Supervised Learning

Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.

Consolidation of memory traces in cultured cortical networks requires low cholinergic tone, synchronized activity and high network excitability

In systems consolidation, encoded memories are replayed by the hippocampus during slow-wave sleep (SWS), and permanently stored in the neocortex. Declarative memory consolidation is believed to benefit from the oscillatory rhythms and low cholinergic tone observed in this sleep stage, but underlying mechanisms remain unclear. To clarify the role of cholinergic modulation and synchronized activity in memory consolidation, we applied repeated electrical stimulation in mature cultures of dissociated rat cortical neurons with high or low cholinergic tone, mimicking the cue replay observed during systems consolidation under distinct cholinergic concentrations. In the absence of cholinergic input, these cultures display activity patterns hallmarked by network bursts, synchronized events reminiscent of the low frequency oscillations observed during SWS. They display stable activity and connectivity, which mutually interact and achieve an equilibrium. Electrical stimulation reforms the equilibrium to include the stimulus response, a phenomenon interpreted as memory trace formation. Without cholinergic input, activity was burst-dominated. First application of a stimulus induced significant connectivity changes, while subsequent repetition no longer affected connectivity. Presenting a second stimulus at a different electrode had the same effect, whereas returning to the initial stimuli did not induce further connectivity alterations, indicating that the second stimulus did not erase the 'memory trace' of the first. Distinctively, cultures with high cholinergic tone displayed reduced network excitability and dispersed firing, and electrical stimulation did not induce significant connectivity changes. We conclude that low cholinergic tone facilitates memory formation and consolidation, possibly through enhanced network excitability. Network bursts or SWS oscillations may merely reflect high network excitability.

PP1/PP2A phosphatase inhibition-induced metaplasticity in protein synthesis blocker-treated hippocampal slices: LTP and LTD, or There and Back again

Long-term potentiation (LTP) and long-term depression (LTD) are key forms of synaptic plasticity in the hippocampus. LTP and LTD are believed to underlie the processes occurring during learning and memory. Search of mechanisms responsible for switching from LTP to LTD and vice versa is an important fundamental task. Protein synthesis blockers (PSB) are widely used in models of memory impairment and LTP suppression. Here, we found that blockade of serine/threonine phosphatases 1 (PP1) and 2A (PP2A) with the specific blockers, calyculin A (CalyA) or okadaic acid (OA), and simultaneous blockade of the protein translation by anisomycin or cycloheximide leads to a switch from PSB-impaired LTP to LTD. PP1/PP2A-dependent LTD was extremely sensitive to the intensity of the test stimuli, whose increase restored the field excitatory postsynaptic potentials (fEPSP) to the values corresponding to control LTP in the non-treated slices. PP1/PP2A blockade affected the basal synaptic transmission, increasing the paired-pulse facilitation (PPF) ratio, and restored the PSB-impaired PPF 3 h after tetanus. Prolonged exposure to anisomycin led to the NO synthesis increase (measured using fluorescent dye) both in the dendrites and somata of CA1, CA3, dentate gyrus (DG) hippocampal layers. OA partially prevented the NO production in the CA1 dendrites, as well in the CA3 and DG somas. Direct measurements of changes in serine/threonine phosphatase (STPP) activity revealed importance of the PP1/PP2A-dependent component in the late LTP phase (L-LTP) in anisomycin-treated slices. Thus, serine/threonine phosphatases PP1/PP2A influence both basal synaptic transmission and stimulation-induced synaptic plasticity.

Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks

Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified ad hoc by the researcher. This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Additionally, this approach presupposes knowledge on the part of the researcher on how the environment should be partitioned and represented and scales poorly with the size or complexity of the environment. To address these issues and gain insights into how the brain generates its own task-relevant mappings, we propose a learning architecture that combines unsupervised learning on the input projections with biologically motivated clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce clearly distinguishable activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

Evaluation and Treatment of Vascular Cognitive Impairment by Transcranial Magnetic Stimulation

The exact relationship between cognitive functioning, cortical excitability, and synaptic plasticity in dementia is not completely understood. Vascular cognitive impairment (VCI) is deemed to be the most common cognitive disorder in the elderly since it encompasses any degree of vascular-based cognitive decline. In different cognitive disorders, including VCI, transcranial magnetic stimulation (TMS) can be exploited as a noninvasive tool able to evaluate in vivo the cortical excitability, the propension to undergo neural plastic phenomena, and the underlying transmission pathways. Overall, TMS in VCI revealed enhanced cortical excitability and synaptic plasticity that seem to correlate with the disease process and progression. In some patients, such plasticity may be considered as an adaptive response to disease progression, thus allowing the preservation of motor programming and execution. Recent findings also point out the possibility to employ TMS to predict cognitive deterioration in the so-called "brains at risk" for dementia, which may be those patients who benefit more of disease-modifying drugs and rehabilitative or neuromodulatory approaches, such as those based on repetitive TMS (rTMS). Finally, TMS can be exploited to select the responders to specific drugs in the attempt to maximize the response and to restore maladaptive plasticity. While no single TMS index owns enough specificity, a panel of TMS-derived measures can support VCI diagnosis and identify early markers of progression into dementia. This work reviews all TMS and rTMS studies on VCI. The aim is to evaluate how cortical excitability, plasticity, and connectivity interact in the pathophysiology of the impairment and to provide a translational perspective towards novel treatments of these patients. Current pitfalls and limitations of both studies and techniques are also discussed, together with possible solutions and future research agenda.

The Relationship of Transcranial Magnetic Stimulation With Sleep and Plasticity

Neuroplasticity is an area of expanding interest in psychiatry. Plasticity and metaplasticity are processes contributing to the scaling up and down of neuronal connections, and they are involved with changes in learning, memory, mood, and sleep. Effective mood treatments, including repetitive transcranial magnetic stimulation (rTMS), are reputed to work via changes in neuronal circuitry. This article explores the interrelatedness of sleep, plasticity, and rTMS treatment. A PubMed-based literature review was conducted to identify all available studies examining the relationship of rTMS, plasticity, and sleep. Key words used in this search included "TMS," "transcranial magnetic stimulation," "plasticity," "metaplasticity," "sleep," and "insomnia." Depressed mood tends to be associated with impaired neural plasticity, while antidepressant treatments can augment neural plasticity. rTMS impacts plasticity, yielding long-lasting effects, with differing impacts on the waking and sleeping brain. Higher quality sleep promotes plasticity and learning. Reports on the sleep impact of high-frequency and low-frequency rTMS are mixed. The efficacy of rTMS may rely on brain plasticity manipulation, enhanced via the stimulation of neural circuits. Total sleep time and sleep continuity are sleep qualities that are likely necessary but insufficient for the homeostatic plasticity driven by slow-wave sleep. Understanding the relationship between sleep and rTMS treatment is likely critical to enhancing outcomes.

The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations

It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients

Intraoperative direct electrostimulation mapping (DEM) is currently the gold-standard for glioma surgery, since functional-based resection allows an optimization of the onco-functional balance (increased resection with preserved quality of life). Besides intrasurgical awake mapping of conation, cognition, and behavior, preoperative mapping by means of functional neuroimaging (FNI) and transcranial magnetic stimulation (TMS) has increasingly been utilized for surgical selection and planning. However, because these techniques suffer from several limitations, particularly for direct functional mapping of subcortical white matter pathways, DEM remains crucial to map neural connectivity. On the other hand, non-invasive FNI and TMS can be repeated before and after surgical resection(s), enabling longitudinal investigation of brain reorganization, especially in slow-growing tumors like low-grade gliomas. Indeed, these neoplasms generate neuroplastic phenomena in patients with usually no or only slight neurological deficits at diagnosis, despite gliomas involving the so-called "eloquent" structures. Here, data gained from perioperative FNI/TMS mapping methods are reviewed, in order to decipher mechanisms underpinning functional cerebral reshaping induced by the tumor and its possible relapse, (re)operation(s), and postoperative rehabilitation. Heterogeneous spatiotemporal patterns of rearrangement across patients and in a single patient over time have been evidenced, with structural changes as well as modifications of intra-hemispheric (in the ipsi-lesional and/or contra-lesional hemisphere) and inter-hemispheric functional connectivity. Such various fingerprints of neural reconfiguration were correlated to different levels of cognitive compensation. Serial multimodal studies exploring neuroplasticity might lead to new management strategies based upon multistage therapeutic approaches adapted to the individual profile of functional reallocation.

Neuroregeneration and plasticity: a review of the physiological mechanisms for achieving functional recovery postinjury

Neuronal networks, especially those in the central nervous system (CNS), evolved to support extensive functional capabilities while ensuring stability. Several physiological "brakes" that maintain the stability of the neuronal networks in a healthy state quickly become a hinderance postinjury. These "brakes" include inhibition from the extracellular environment, intrinsic factors of neurons and the control of neuronal plasticity. There are distinct differences between the neuronal networks in the peripheral nervous system (PNS) and the CNS. Underpinning these differences is the trade-off between reduced functional capabilities with increased adaptability through the formation of new connections and new neurons. The PNS has "facilitators" that stimulate neuroregeneration and plasticity, while the CNS has "brakes" that limit them. By studying how these "facilitators" and "brakes" work and identifying the key processes and molecules involved, we can attempt to apply these theories to the neuronal networks of the CNS to increase its adaptability. The difference in adaptability between the CNS and PNS leads to a difference in neuroregenerative properties and plasticity. Plasticity ensures quick functional recovery of abilities in the short and medium term. Neuroregeneration involves synthesizing new neurons and connections, providing extra resources in the long term to replace those damaged by the injury, and achieving a lasting functional recovery. Therefore, by understanding the factors that affect neuroregeneration and plasticity, we can combine their advantages and develop rehabilitation techniques. Rehabilitation training methods, coordinated with pharmacological interventions and/or electrical stimulation, contributes to a precise, holistic treatment plan that achieves functional recovery from nervous system injuries. Furthermore, these techniques are not limited to limb movement, as other functions lost as a result of brain injury, such as speech, can also be recovered with an appropriate training program.

The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity

Along sensory pathways, representations of environmental stimuli become increasingly sparse and expanded. If additionally the feed-forward synaptic weights are structured according to the inherent organization of stimuli, the increase in sparseness and expansion leads to a reduction of sensory noise. However, it is unknown how the synapses in the brain form the required structure, especially given the omnipresent noise of environmental stimuli. Here, we employ a combination of synaptic plasticity and intrinsic plasticity—adapting the excitability of each neuron individually—and present stimuli with an inherent organization to a feed-forward network. We observe that intrinsic plasticity maintains the sparseness of the neural code and thereby allows synaptic plasticity to learn the organization of stimuli in low-noise environments. Nevertheless, even high levels of noise can be handled after a subsequent phase of readaptation of the neuronal excitabilities by intrinsic plasticity. Interestingly, during this phase the synaptic structure has to be maintained. These results demonstrate that learning and recalling in the presence of noise requires the coordinated interplay between plasticity mechanisms adapting different properties of the neuronal circuit.

Everyday life requires living beings to continuously recognize and categorize perceived stimuli from the environment. To master this task, the representations of these stimuli become increasingly sparse and expanded along the sensory pathways of the brain. In addition, the underlying neuronal network has to be structured according to the inherent organization of the environmental stimuli. However, how the neuronal network learns the required structure even in the presence of noise remains unknown. In this theoretical study, we show that the interplay between synaptic plasticity—controlling the synaptic efficacies—and intrinsic plasticity—adapting the neuronal excitabilities—enables the network to encode the organization of environmental stimuli. It thereby structures the network to correctly categorize stimuli even in the presence of noise. After having encoded the stimuli’s organization, consolidating the synaptic structure while keeping the neuronal excitabilities dynamic enables the neuronal system to readapt to arbitrary levels of noise resulting in a near-optimal classification performance for all noise levels. These results provide new insights into the interplay between different plasticity mechanisms and how this interplay enables sensory systems to reliably learn and categorize stimuli from the surrounding environment.

Evaluation and Treatment of Vascular Cognitive Impairment by Transcranial Magnetic Stimulation

The exact relationship between cognitive functioning, cortical excitability, and synaptic plasticity in dementia is not completely understood. Vascular cognitive impairment (VCI) is deemed to be the most common cognitive disorder in the elderly since it encompasses any degree of vascular-based cognitive decline. In different cognitive disorders, including VCI, transcranial magnetic stimulation (TMS) can be exploited as a noninvasive tool able to evaluate in vivo the cortical excitability, the propension to undergo neural plastic phenomena, and the underlying transmission pathways. Overall, TMS in VCI revealed enhanced cortical excitability and synaptic plasticity that seem to correlate with the disease process and progression. In some patients, such plasticity may be considered as an adaptive response to disease progression, thus allowing the preservation of motor programming and execution. Recent findings also point out the possibility to employ TMS to predict cognitive deterioration in the so-called "brains at risk" for dementia, which may be those patients who benefit more of disease-modifying drugs and rehabilitative or neuromodulatory approaches, such as those based on repetitive TMS (rTMS). Finally, TMS can be exploited to select the responders to specific drugs in the attempt to maximize the response and to restore maladaptive plasticity. While no single TMS index owns enough specificity, a panel of TMS-derived measures can support VCI diagnosis and identify early markers of progression into dementia. This work reviews all TMS and rTMS studies on VCI. The aim is to evaluate how cortical excitability, plasticity, and connectivity interact in the pathophysiology of the impairment and to provide a translational perspective towards novel treatments of these patients. Current pitfalls and limitations of both studies and techniques are also discussed, together with possible solutions and future research agenda.

Functional mechanisms underlie the emergence of a diverse range of plasticity phenomena

Diverse plasticity mechanisms are orchestrated to shape the spatiotemporal dynamics underlying brain functions. However, why these plasticity rules emerge and how their dynamics interact with neural activity to give rise to complex neural circuit dynamics remains largely unknown. Here we show that both Hebbian and homeostatic plasticity rules emerge from a functional perspective of neuronal dynamics whereby each neuron learns to encode its own activity in the population activity, so that the activity of the presynaptic neuron can be decoded from the activity of its postsynaptic neurons. We explain how a range of experimentally observed plasticity phenomena with widely separated time scales emerge from learning this encoding function, including STDP and its frequency dependence, and metaplasticity. We show that when implemented in neural circuits, these plasticity rules naturally give rise to essential neural response properties, including variable neural dynamics with balanced excitation and inhibition, and approximately log-normal distributions of synaptic strengths, while simultaneously encoding a complex real-world visual stimulus. These findings establish a novel function-based account of diverse plasticity mechanisms, providing a unifying framework relating plasticity, dynamics and neural computation.

Many experiments have documented a variety of ways in which the connectivity strengths between neurons change in response to the activity of neurons. These changes are an important part of learning. However, it is not understood how such a diverse range of observations can be understood as consequences of an underlying algorithm used by brains for learning. In order to understand such a learning algorithm it is also necessary to understand the neural computation that is being learned, that is, how the functions of the brain are encoded in the activity of its neurons and its connectivity. In this work we propose a simple way in which information can be encoded and decoded in a network of neurons for operating on real-world stimuli, and how this can be learned using two fundamental plasticity rules that change the strength of connections between neurons in response to neural activity. Surprisingly, many experimental observations result as consequences of this approach, indicating that studying the learning of function provides a novel framework for unifying plasticity, dynamics, and neural computation.

Theoretical Models of Neural Development

Constructing a functioning nervous system requires the precise orchestration of a vast array of mechanical, molecular, and neural-activity-dependent cues. Theoretical models can play a vital role in helping to frame quantitative issues, reveal mathematical commonalities between apparently diverse systems, identify what is and what is not possible in principle, and test the abilities of specific mechanisms to explain the data. This review focuses on the progress that has been made over the last decade in our theoretical understanding of neural development.

Maternal Preconception Stress Alters Prefrontal Cortex Development in Long-Evans Rat Pups without Changing Maternal Care

Stress during development can shift the typical developmental trajectory. Maternal stress prior to conception has recently been shown to exert similar influences on the offspring. The present study questioned if a consistent maternal stressor prior to conception (elevated platform stress) would impact the pre-weaning development of offspring brain and behavior, and if maternal care was vulnerable to this experience. Adult female Long-Evans rats were subjected to elevated platform stress for 27 days prior to mating with non-stressed males. Maternal care was monitored, and pups were assessed in two tests of early behavioral development, negative geotaxis and open field. Pups were perfused at weaning and their brains were extracted and stained with Cresyl Violet, allowing gross measurements of cortical and subcortical structures and estimates of neuron density. Main findings indicate that a change in prefrontal cortical thickness is evident despite no change in maternal care. Female offspring show a decrease in medial-dorsal thalamus size. The current study failed to find an effect of maternal preconception stress on early behavioral development. These results suggest that the PFC, and likely behavior dependent on the PFC, is vulnerable to maternal preconception stress and that a strong sex effect is evident. Further studies should examine how such offspring fare using a lifespan model and investigate potential mechanisms responsible for these effects.

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").

Dopaminergic innervation and modulation of hippocampal networks

The catecholamine dopamine plays an important role in hippocampus-dependent plasticity and related learning and memory processes. Dopamine secretion in the hippocampus is activated by, e.g., salient or novel stimuli, thereby helping to establish and to stabilize hippocampus-dependent memories. Disturbed dopaminergic function in the hippocampus leads to severe pathophysiological conditions. While the role and importance of dopaminergic modulation of hippocampal networks have been unequivocally proven, there is still a lack of detailed molecular and cellular mechanistic understanding of how dopamine orchestrates these hippocampal processes. In this chapter of the special issue "Hippocampal structure and function," we will discuss the current understanding of dopaminergic modulation of basal synaptic transmission and long-lasting, activity-dependent potentiation or depression.

Metaplasticity: A Promising Tool to Disentangle Chronic Disorders of Consciousness Differential Diagnosis

The extent of cortical reorganization after brain injury in patients with Vegetative State/Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) depends on the residual capability of modulating synaptic plasticity. Neuroplasticity is largely abnormal in patients with UWS, although the fragments of cortical activity may exist, while patients MCS show a better cortical organization. The aim of this study was to evaluate cortical excitability in patients with disorders of consciousness (DoC) using a transcranial direct current stimulation (TDCS) metaplasticity protocol. To this end, we tested motor-evoked potential (MEP) amplitude, short intracortical inhibition (SICI), and intracortical facilitation (ICF). These measures were correlated with the level of consciousness (by the Coma Recovery Scale-Revised, CRS-R). MEP amplitude, SICI, and ICF strength were significantly modulated following different metaplasticity TDCS protocols only in the patients with MCS. SICI modulations showed a significant correlation with the CRS-R score. Our findings demonstrate, for the first time, a partial preservation of metaplasticity properties in some patients with DoC, which correlates with the level of awareness. Thus, metaplasticity assessment may help the clinician in differentiating the patients with DoC, besides the clinical evaluation. Moreover, the responsiveness to metaplasticity protocols may identify the subjects who could benefit from neuromodulation protocols.

Emergent Dynamical Properties of the BCM Learning Rule

The Bienenstock-Cooper-Munro (BCM) learning rule provides a simple setup for synaptic modification that combines a Hebbian product rule with a homeostatic mechanism that keeps the weights bounded. The homeostatic part of the learning rule depends on the time average of the post-synaptic activity and provides a sliding threshold that distinguishes between increasing or decreasing weights. There are, thus, two essential time scales in the BCM rule: a homeostatic time scale, and a synaptic modification time scale. When the dynamics of the stimulus is rapid enough, it is possible to reduce the BCM rule to a simple averaged set of differential equations. In previous analyses of this model, the time scale of the sliding threshold is usually faster than that of the synaptic modification. In this paper, we study the dynamical properties of these averaged equations when the homeostatic time scale is close to the synaptic modification time scale. We show that instabilities arise leading to oscillations and in some cases chaos and other complex dynamics. We consider three cases: one neuron with two weights and two stimuli, one neuron with two weights and three stimuli, and finally a weakly interacting network of neurons.

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'.

Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level

Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.

Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.

The malleable brain: plasticity of neural circuits and behavior - a review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on page 788. Cover Image for this issue: doi: 10.1111/jnc.13815.

Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty

Value-based decision making often involves integration of reward outcomes over time, but this becomes considerably more challenging if reward assignments on alternative options are probabilistic and non-stationary. Despite the existence of various models for optimally integrating reward under uncertainty, the underlying neural mechanisms are still unknown. Here we propose that reward-dependent metaplasticity (RDMP) can provide a plausible mechanism for both integration of reward under uncertainty and estimation of uncertainty itself. We show that a model based on RDMP can robustly perform the probabilistic reversal learning task via dynamic adjustment of learning based on reward feedback, while changes in its activity signal unexpected uncertainty. The model predicts time-dependent and choice-specific learning rates that strongly depend on reward history. Key predictions from this model were confirmed with behavioral data from non-human primates. Overall, our results suggest that metaplasticity can provide a neural substrate for adaptive learning and choice under uncertainty.

Optimal structure of metaplasticity for adaptive learning

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning.

Successful learning from our experience and feedback from the environment requires that the reward value assigned to a given option or action to be updated by a precise amount after each feedback. In the standard model for reward-based learning known as reinforcement learning, the learning rates determine the strength of such update. A large learning rate allows fast update of values (large adaptability) but introduces noise (small precision), whereas a small learning rate does the opposite. Thus, learning seems to be bounded by a tradeoff between adaptability and precision. Here, we asked whether there are synaptic mechanisms that are capable of adjusting the brain’s level of plasticity according to reward statistics, and, therefore, allow the learning process to be adaptive. We showed that metaplasticity, changes in the synaptic state that shape future synaptic modifications without any observable changes in the strength of synapses, could provide such a mechanism and furthermore, identified the optimal structure of such metaplasticity. We propose that metaplasticity, which sometimes causes no observable changes in behavior and thus could be perceived as a lack of learning, can provide a robust mechanism for adaptive learning.

Cortical Plasticity in Depression

Neural plasticity is considered the neurophysiological correlate of learning and memory, although several studies have also noted that it plays crucial roles in a number of neurological and psychiatric diseases. Indeed, impaired brain plasticity may be one of the pathophysiological mechanisms that underlies both cognitive decline and major depression. Moreover, a degree of cognitive impairment is frequently observed throughout the clinical spectrum of mood disorders, and the relationship between depression and cognition is often bidirectional. However, most evidence for dysfunctional neural plasticity in depression has been indirect. Transcranial magnetic stimulation has emerged as a noninvasive tool for investigating several parameters of cortical excitability with the aim of exploring the functions of different neurotransmission pathways and for probing in vivo plasticity in both healthy humans and those with pathological conditions. In particular, depressed patients exhibit a significant interhemispheric difference in motor cortex excitability, an imbalanced inhibitory or excitatory intracortical neurochemical circuitry, reduced postexercise facilitation, and an impaired long-term potentiation-like response to paired-associative transcranial magnetic stimulation, and these symptoms may indicate disrupted plasticity. Research aimed at disentangling the mechanism by which neuroplasticity plays a role in the pathological processes that lead to depression and evaluating the effects of modulating neuroplasticity are needed for the field to facilitate more powerful translational research studies and identify novel therapeutic targets.

Saturation of long-term potentiation in the dorsal cochlear nucleus and its pharmacological reversal in an experimental model of tinnitus

Animal models have demonstrated that tinnitus is a pathology of dysfunctional excitability in the central auditory system, in particular in the dorsal cochlear nucleus (DCN) of the brainstem. We used a murine model and studied whether acoustic over-exposure leading to hearing loss and tinnitus, affects long-term potentiation (LTP) at DCN multisensory synapses. Whole cell and field potential recordings were used to study the effects on release probability and synaptic plasticity, respectively in brainstem slices. Shifts in hearing threshold were quantified by auditory brainstem recordings, and gap-induced prepulse inhibition of the acoustic startle reflex was used as an index for tinnitus. An increased release probability that saturated LTP and thereby induced metaplasticity at DCN multisensory synapses, was observed 4-5days following acoustic over-exposure. Perfusion of an NMDA receptor antagonist or decreasing extracellular calcium concentration, decreased the release probability and restored LTP following acoustic over-exposure. In vivo administration of magnesium-threonate following acoustic over-exposure restored LTP at DCN multisensory synapses, and reduced gap detection deficits observed four months following acoustic over-exposure. These observations suggest that consequences of noise-induced metaplasticity could underlie the gap detection deficits that follow acoustic over-exposure, and that early therapeutic intervention could target metaplasticity and alleviate tinnitus.

A Model of Fast Hebbian Spike Latency Normalization

Hebbian changes of excitatory synapses are driven by and enhance correlations between pre- and postsynaptic neuronal activations, forming a positive feedback loop that can lead to instability in simulated neural networks. Because Hebbian learning may occur on time scales of seconds to minutes, it is conjectured that some form of fast stabilization of neural firing is necessary to avoid runaway of excitation, but both the theoretical underpinning and the biological implementation for such homeostatic mechanism are to be fully investigated. Supported by analytical and computational arguments, we show that a Hebbian spike-timing-dependent metaplasticity rule, accounts for inherently-stable, quick tuning of the total input weight of a single neuron in the general scenario of asynchronous neural firing characterized by UP and DOWN states of activity.

Cortical Plasticity in Depression

Neural plasticity is considered the neurophysiological correlate of learning and memory, although several studies have also noted that it plays crucial roles in a number of neurological and psychiatric diseases. Indeed, impaired brain plasticity may be one of the pathophysiological mechanisms that underlies both cognitive decline and major depression. Moreover, a degree of cognitive impairment is frequently observed throughout the clinical spectrum of mood disorders, and the relationship between depression and cognition is often bidirectional. However, most evidence for dysfunctional neural plasticity in depression has been indirect. Transcranial magnetic stimulation has emerged as a noninvasive tool for investigating several parameters of cortical excitability with the aim of exploring the functions of different neurotransmission pathways and for probing in vivo plasticity in both healthy humans and those with pathological conditions. In particular, depressed patients exhibit a significant interhemispheric difference in motor cortex excitability, an imbalanced inhibitory or excitatory intracortical neurochemical circuitry, reduced postexercise facilitation, and an impaired long-term potentiation-like response to paired-associative transcranial magnetic stimulation, and these symptoms may indicate disrupted plasticity. Research aimed at disentangling the mechanism by which neuroplasticity plays a role in the pathological processes that lead to depression and evaluating the effects of modulating neuroplasticity are needed for the field to facilitate more powerful translational research studies and identify novel therapeutic targets.

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the meeting and finally suggest potential directions of research that we believe are most promising to develop an understanding of how these two forms of plasticity interact to facilitate functional changes in the brain.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery

Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms, and repetitive movement, which together can represent brain state, effect of incoming stimulus, and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute to the future design of novel protocols, in which combined tDCS and motor rehabilitation strategies are tailored to the ongoing dynamics that they interact with, by considering the known biophysical factors recruited by such protocols and their interaction.

Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions

BACKGROUND

Tracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging.

NEW METHOD

We developed a novel multiwavelet-based time-varying generalized Laguerre-Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data.

RESULTS

The proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data.

COMPARISON WITH EXISTING METHODS

The proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev polynomial basis function method gave the good parametric estimation results, it requires prior parameter estimation. It was shown that the proposed multiwavelet-based TVGLV method can track the time-varying parameter changes rapidly and accurately.

CONCLUSIONS

The multiwavelet-based TVGLV modeling framework developed in this paper can not only provide a computational modeling scheme for investigating such nonstationary properties, track more general forms of changes in time-varying neural dynamics, and but also may potentially be applied to investigate the spatial-temporal information underlying biomedical spiking signals.

Induction and Consolidation of Calcium-Based Homo- and Heterosynaptic Potentiation and Depression

The adaptive mechanisms of homo- and heterosynaptic plasticity play an important role in learning and memory. In order to maintain plasticity-induced changes for longer time scales (up to several days), they have to be consolidated by transferring them from a short-lasting early-phase to a long-lasting late-phase state. The underlying processes of this synaptic consolidation are already well-known for homosynaptic plasticity, however, it is not clear whether the same processes also enable the induction and consolidation of heterosynaptic plasticity. In this study, by extending a generic calcium-based plasticity model with the processes of synaptic consolidation, we show in simulations that indeed heterosynaptic plasticity can be induced and, furthermore, consolidated by the same underlying processes as for homosynaptic plasticity. Furthermore, we show that by local diffusion processes the heterosynaptic effect can be restricted to a few synapses neighboring the homosynaptically changed ones. Taken together, this generic model reproduces many experimental results of synaptic tagging and consolidation, provides several predictions for heterosynaptic induction and consolidation, and yields insights into the complex interactions between homo- and heterosynaptic plasticity over a broad variety of time (minutes to days) and spatial scales (several micrometers).

Mechanisms of homeostatic plasticity in the excitatory synapse

Brain development, sensory information processing, and learning and memory processes depend on Hebbian forms of synaptic plasticity, and on the remodeling and pruning of synaptic connections. Neurons in networks implicated in these processes carry out their functions while facing constant perturbation; homeostatic responses are therefore required to maintain neuronal activity within functional ranges for proper brain function. Here, we will review in vitro and in vivo studies demonstrating that several mechanisms underlie homeostatic plasticity of excitatory synapses, and identifying participant molecular players. Emerging evidence suggests a link between disrupted homeostatic synaptic plasticity and neuropsychiatric and neurologic disorders. Hebbian forms of synaptic plasticity, such as long-term potentiation (LTP), induce long-lasting changes in synaptic strength, which can be destabilizing and drive activity to saturation. Conversely, homeostatic plasticity operates to compensate for prolonged activity changes, stabilizing neuronal firing within a dynamic physiological range. We review mechanisms underlying homeostatic plasticity, and address how neurons integrate distinct forms of plasticity for proper brain function. This article is part of a mini review series: "Synaptic Function and Dysfunction in Brain Diseases".

Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron–interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.

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.