The BCM theory of synapse modification at 30: interaction of theory with experiment

Manifold learning for olfactory habituation to strongly fluctuating backgrounds

Animals rely on their sense of smell to survive, but important olfactory cues are mixed with confounding background odors that fluctuate due to atmospheric turbulence. It is unclear how the olfactory system habituates to such stochastic backgrounds to detect behaviorally important odors. Here, we explicitly consider the high-dimensional nature of odor coding, the natural statistics of odor fluctuations and the architecture of the early olfactory pathway. We show that their combination favors a manifold learning mechanism for olfactory habituation over alternatives based on predictive filtering. Manifold learning is implemented in our model by a biologically plausible network of inhibitory interneurons in the early olfactory pathway. We demonstrate that plasticity rules based on IBCM or online PCA are effective at implementing this mechanism in turbulent conditions and outperform previous models relying on mean background subtraction. Interneurons with an IBCM plasticity rule acquire selectivity to independently varying odors. This manifold learning mechanism offers a path towards distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments.

Spinal and corticospinal excitability changes with voluntary modulation of motor cortex oscillations

AIM

The aim of this study was to investigate the effects of EEG neurofeedback (NF)-induced modulation of sensorimotor alpha (i.e., mu) rhythm on spinal and corticospinal tract (CST) excitability.

METHODS

Forty-three healthy volunteers participated in 3 sessions of EEG-NF for upregulation (N=24) or downregulation (N=19) of individual alpha oscillations at central location Cz. Spinal excitability was studied before and during NF using H-reflex of the soleus muscle, and CST excitability was tested before and after NF, through Motor-Evoked Potential (MEP) of the tibialis anterior muscle. Mu rhythm was extracted using current source density. Differences in MEP and H-reflex before and during/after NF were analysed using repeated measures analysis. The relationship with motor cortexcortical excitability was estimated through linear regression between change in MEP/H-reflex, and change in power of mu rhythm and the upper portion of mu rhythm, muh.

RESULTS

CST excitability changes were significantly correlated to change in muh (p-value < 0.044, |r|>0.42), while spinal excitability changes were correlated to broad mu power modulation (p-value < 0.04, |r|> 0.43). While no distinct effect of NF on spinal versus CST excitability was found, the correlations indicate an inverted U-shape relationship between cortical and subcortical excitability. The trends of the correlations between spinal/CST excitability change and EEG power change were preserved when participants were grouped by success at NF task, and by mu modulation outcome, indicating that the net effect of power change at Cz weighs more than the task the participants attempted to accomplish.

CONCLUSIONS

The consistent direction of mu power correlation with both MEP, tested after NF, and H-reflex, tested during NF, indicates that modifications in mu activity are associated with spinal and CST adaptations lasting beyond the NF session, evidencing neuroplasticity. Together with the inverted U-shape relationship found between amplitude of mu modulation and spinal/CST excitability change, the results provide support for further research and clinical implementation of NF to induce CNS plasticity, a prerequisite for effective neural rehabilitation.

Rapid hippocampal synaptic potentiation induced by ketamine metabolite (2R,6R)-hydroxynorketamine persistently primes synaptic plasticity

The pharmacologically active (R,S)-ketamine (ketamine) metabolite (2 R,6 R)-hydroxynorketamine (HNK) maintains ketamine's preclinical antidepressant profile without adverse effects. While hypotheses have been proposed to explain how ketamine and its metabolites initiate their antidepressant-relevant effects, it remains unclear how sustained therapeutic actions arise following drug elimination. To distinguish the physiological mechanisms involved in the rapid from sustained actions of HNK, we utilized extracellular electrophysiology combined with pharmacology to develop an in vitro hippocampal slice incubation model that exhibited pharmacological fidelity to the 1) rapid synaptic potentiation induced by HNK at the Schaffer collateral-CA1 (SC-CA1) synapse during bath-application to slices collected from mice, and 2) maintenance of metaplastic (priming) activity that enhanced N-methyl-D-aspartate receptor (NMDAR) activation-dependent long-term potentiation (LTP) hours after in vivo dosing. We used this model to reveal novel mechanisms engaged in HNK's temporally-sensitive antidepressant-relevant synaptic actions, finding that the induction of synaptic potentiation by HNK did not require NMDAR activity, but NMDAR activity was necessary to maintain synaptic priming. HNK required protein kinase A (PKA) activity to rapidly potentiate SC-CA1 neurotransmission to facilitate synaptic priming that persistently promoted LTP formation. HNK's rapid actions were blocked by inhibitors of adenylyl cyclase 1 (AC1), but not an AC5 inhibitor. We conclude that HNK rapidly potentiates SC-CA1 synaptic efficacy, which then stimulates priming mechanisms that persistently favor plasticity. Targeting such priming mechanisms may be an effective antidepressant strategy, and our incubation model may aid in revealing novel pharmacological targets.

Debunking the Myth of Excitatory and Inhibitory Repetitive Transcranial Magnetic Stimulation in Cognitive Neuroscience Research

Repetitive TMS (rTMS) is a powerful neuroscientific tool with the potential to noninvasively identify brain-behavior relationships in humans. Early work suggested that certain rTMS protocols (e.g., continuous theta-burst stimulation, intermittent theta-burst stimulation, high-frequency rTMS, low-frequency rTMS) predictably alter the probability that cortical neurons will fire action potentials (i.e., change cortical excitability). However, despite significant methodological, conceptual, and technical advances in rTMS research over the past few decades, overgeneralization of early rTMS findings has led to a stubbornly persistent assumption that rTMS protocols by their nature induce behavioral and/or physiological inhibition or facilitation, even when they are applied to nonmotor cortical sites or under untested circumstances. In this Perspectives article, we offer a "public service announcement" that summarizes the origins of this problematic assumption, highlighting limitations of seminal studies that inspired them and results of contemporary studies that violate them. Next, we discuss problems associated with holding this assumption, including making brain-behavior inferences without confirming the locality and directionality of neurophysiological changes. Finally, we provide recommendations for researchers to eliminate this misguided assumption when designing and interpreting their own work, emphasizing results of recent studies showing that the effects of rTMS on neurophysiological metrics and their associated behaviors can be caused by mechanisms other than binary changes in excitability of the stimulated brain region or network. Collectively, we contend that no rTMS protocol is by its nature either excitatory or inhibitory, and that researchers must use caution with these terms when forming experimental hypotheses and testing brain-behavior relationships.

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.

Timing Matters: Preconditioning Effects of Cathodal Transcranial Direct Current Stimulation on Intermittent Theta-Burst Stimulation-Induced Neuroplasticity in the Primary Motor Cortex

BACKGROUND

Recent advances have highlighted the interplay between intermittent theta-burst stimulation (iTBS) and transcranial direct current stimulation (tDCS) in neuroplasticity modulation. However, the synergistic potential of these modalities in optimizing plasticity, particularly with cathodal tDCS preconditioning before iTBS, remains poorly understood.

OBJECTIVE

This study examined the effects of cathodal high-definition tDCS (HD-tDCS) preconditioning on iTBS-induced neuroplasticity in the primary motor cortex at different timing intervals.

MATERIALS AND METHODS

Twenty healthy participants underwent four stimulation sessions in a randomized cross-over design, receiving iTBS either immediately or at 10-minute and 30-minute intervals after cathodal HD-tDCS preconditioning, in addition to a control session with iTBS immediately after sham HD-tDCS. Motor evoked potentials (MEPs) were measured at baseline and 5, 10, 15, and 30 minutes after iTBS to assess changes in neuroplasticity. Each session was separated by ≥one week to prevent carry-over effects.

RESULTS

Compared with sham sessions, immediate cathodal HD-tDCS preconditioning significantly enhanced MEPs across all measured intervals after iTBS, with sustained neuroplasticity persisting for up to 30 minutes. Immediate preconditioning produced significant MEP enhancements at 5 and 10 minutes when compared with the 30-minute delayed condition.

CONCLUSIONS

The effectiveness of cathodal tDCS preconditioning in enhancing iTBS-induced neuroplasticity decreased with increasing intervals between tDCS and iTBS application. These findings highlight the essential role of precise timing in tDCS preconditioning for maximizing the neuroplastic effects of iTBS and offer valuable insights for optimizing neurorehabilitation protocols.

The enigmatic HCN channels: A cellular neurophysiology perspective

What physiological role does a slow hyperpolarization-activated ion channel with mixed cation selectivity play in the fast world of neuronal action potentials that are driven by depolarization? That puzzling question has piqued the curiosity of physiology enthusiasts about the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which are widely expressed across the body and especially in neurons. In this review, we emphasize the need to assess HCN channels from the perspective of how they respond to time-varying signals, while also accounting for their interactions with other co-expressing channels and receptors. First, we illustrate how the unique structural and functional characteristics of HCN channels allow them to mediate a slow negative feedback loop in the neurons that they express in. We present the several physiological implications of this negative feedback loop to neuronal response characteristics including neuronal gain, voltage sag and rebound, temporal summation, membrane potential resonance, inductive phase lead, spike triggered average, and coincidence detection. Next, we argue that the overall impact of HCN channels on neuronal physiology critically relies on their interactions with other co-expressing channels and receptors. Interactions with other channels allow HCN channels to mediate intrinsic oscillations, earning them the "pacemaker channel" moniker, and to regulate spike frequency adaptation, plateau potentials, neurotransmitter release from presynaptic terminals, and spike initiation at the axonal initial segment. We also explore the impact of spatially non-homogeneous subcellular distributions of HCN channels in different neuronal subtypes and their interactions with other channels and receptors. Finally, we discuss how plasticity in HCN channels is widely prevalent and can mediate different encoding, homeostatic, and neuroprotective functions in a neuron. In summary, we argue that HCN channels form an important class of channels that mediate a diversity of neuronal functions owing to their unique gating kinetics that made them a puzzle in the first place.

Impaired hippocampal plasticity associated with loss of recycling endosomal SLC9A6/NHE6 is ameliorated by the TrkB agonist 7,8-dihydroxyflavone

Proper maintenance of intracellular vesicular pH is essential for cargo trafficking during synaptic function and plasticity. Mutations in the SLC9A6 gene encoding the recycling endosomal pH regulator (Na+, K+)/H+ exchanger isoform 6 (NHE6) are causal for Christianson syndrome (CS), a severe form of X-linked intellectual disability. NHE6 expression is also downregulated in other neurodevelopmental and neurodegenerative disorders, such as autism spectrum disorder and Alzheimer's disease, suggesting its dysfunction could contribute more broadly to the pathophysiology of other neurological conditions. To understand how ablation of NHE6 function leads to severe learning impairments, we assessed synaptic structure, function, and cellular mechanisms of learning in a novel line of Nhe6 knockout (KO) mice expressing a plasma membrane-tethered green fluorescent protein within hippocampal neurons. We uncovered significant reductions in dendritic spines density, AMPA receptor (AMPAR) expression, and AMPAR-mediated neurotransmission in CA1 pyramidal neurons. The neurons also failed to undergo functional and structural enhancement during long-term potentiation (LTP). Significantly, the selective TrkB agonist 7,8-dihydroxyflavone restored spine density as well as functional and structural LTP in KO neurons. TrkB activation thus may act as a potential clinical intervention to ameliorate cognitive deficits in CS and other neurodegenerative disorders.

Adult visual deprivation engages associative, presynaptic plasticity of thalamic input to cortex

Associative plasticity at thalamocortical synapses is thought to be constrained by age in the mammalian cortex. However, here we show for the first time that prolonged visual deprivation induces robust and reversible plasticity at synapses between first order visual thalamus and cortical layer 4 pyramidal neurons. The plasticity is associative and expressed by changes in presynaptic function, thereby amplifying and relaying the change in efferent drive to the visual cortex.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25 ns/60 ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

tDCS and local scalp cooling do not change corticomotor and intracortical excitability in healthy humans

OBJECTIVE

Scalp cooling might increase the long-term potentiation (LTP)-like effect of transcranial direct current stimulation (tDCS) by reducing the threshold for after-effects according to metaplasticity and increasing electrical current density reaching the cortical neurons. We aimed to investigate whether priming scalp cooling potentiates the tDCS after-effect on motor cortex excitability.

METHODS

This study had a randomized, parallel-arms, sham-controlled, double-blinded design with an adequately powered sample of 105 healthy subjects. Corticomotor and intracortical excitability were assessed with motor evoked potentials (MEP) from transcranial magnetic stimulation (TMS) in short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) paradigms. Subjects were randomly allocated into six intervention groups, including anodal and cathodal tDCS (1-mA/20-min), scalp cooling, and sham. MEPs were recorded before, immediately, and 15 min after the interventions.

RESULTS

We did not observe changes in MEP amplitude from single-pulse TMS, SICI, and ICF with any intervention protocol.

CONCLUSION

Anodal and cathodal tDCS did not have an LTP-like neuromodulatory effect on corticospinal and did not provide detectable GABAergic and glutamatergic neurotransmission changes, which were not influenced by priming scalp cooling.

SIGNIFICANCE

We provide strong evidence that tDCS (1-mA/20-min) does not alter corticomotor and intracortical excitability with or without priming scalp cooling.

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.

Wnt-5a Signaling Mediates Metaplasticity at Hippocampal CA3-CA1 Synapses in Mice

Wnt signaling plays a role in synaptic plasticity, but the specific cellular events and molecular components involved in Wnt signaling-mediated synaptic plasticity are not well defined. Here, we report a change in the threshold required to induce synaptic plasticity that facilitates the induction of long-term potentiation (LTP) and inhibits the induction of long-term depression (LTD) during brief exposure to the noncanonical ligand Wnt-5a. Both effects are related to the metaplastic switch of hippocampal CA3-CA1 synaptic transmission, a complex mechanism underlying the regulation of the threshold required to induce synaptic plasticity and of synaptic efficacy. We observed an early increase in the amplitude of field excitatory postsynaptic potentials (fEPSPs) that persisted over time, including after washout. The first phase involves an increase in the fEPSP amplitude that is required to trigger a spontaneous second phase that depends on Jun N-terminal kinase (JNK) and N-methyl D-aspartate receptor (NMDAR) activity. These changes are prevented by treatment with secreted frizzled-related protein 2 (sFRP-2), an endogenous antagonist of Wnt ligands. Here, we demonstrate the contribution of Wnt-5a signaling to a process associated with metaplasticity at CA3-CA1 synapses that favors LTP over LTD.

Adaptive rewiring: a general principle for neural network development

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring.

Rapid Hippocampal Synaptic Potentiation Induced by Ketamine Metabolite ( 2R , 6R )-Hydroxynorketamine Persistently Primes Synaptic Plasticity

The pharmacologically active ( R , S )-ketamine (ketamine) metabolite ( 2R , 6R )-hydroxynorketamine (HNK) maintains ketamine's preclinical antidepressant profile without adverse effects. While hypotheses have been proposed to explain how ketamine and its metabolites initiate their antidepressant-relevant effects, it remains unclear how sustained therapeutic actions arise following drug elimination. To distinguish the physiological mechanisms involved in the rapid from sustained actions of HNK, we utilized extracellular electrophysiology combined with pharmacology to develop an in vitro hippocampal slice incubation model that exhibited pharmacological fidelity to the 1) rapid synaptic potentiation induced by HNK at the Schaffer collateral-CA1 (SC-CA1) synapse during bath-application to slices collected from mice, and 2) maintenance of metaplastic (priming) activity that lowered the threshold for N- methyl-D-aspartate receptor (NMDAR) activation-dependent long-term potentiation (LTP) hours after in vivo dosing. We then used this model to reveal novel druggable mechanisms engaged in HNK's temporally-sensitive antidepressant synaptic actions, finding that the induction of synaptic potentiation by HNK did not require NMDAR activity, but NMDAR activity was necessary to maintain synaptic priming. HNK required protein kinase A (PKA) activity to rapidly potentiate SC-CA1 neurotransmission to facilitate synaptic priming that persistently promoted LTP formation. HNK's rapid actions were blocked by inhibitors of adenylyl cyclase 1 (AC1), but not an AC5 inhibitor. We conclude that HNK rapidly potentiates SC-CA1 synaptic efficacy, which then stimulates priming mechanisms that persistently favor antidepressant-relevant plasticity. Targeting such priming mechanisms may be an effective antidepressant strategy, and using approaches such as our incubation model may aid in revealing novel pharmacological targets.

Blockade of GluN2B-Containing NMDA Receptors Prevents Potentiation and Depression of Responses during Ocular Dominance Plasticity

Monocular deprivation (MD) causes an initial decrease in synaptic responses to the deprived eye in juvenile mouse primary visual cortex (V1) through Hebbian long-term depression (LTD). This is followed by a homeostatic increase, which has been attributed either to synaptic scaling or to a slide threshold for Hebbian long-term potentiation (LTP) rather than scaling. We therefore asked in mice of all sexes whether the homeostatic increase during MD requires GluN2B-containing NMDA receptor activity, which is required to slide the plasticity threshold but not for synaptic scaling. Selective GluN2B blockade from 2-6 d after monocular lid suture prevented the homeostatic increase in miniature excitatory postsynaptic current (mEPSC) amplitude in monocular V1 of acute slices and prevented the increase in visually evoked responses in binocular V1 in vivo. The decrease in mEPSC amplitude and visually evoked responses during the first 2 d of MD also required GluN2B activity. Together, these results support the idea that GluN2B-containing NMDA receptors first play a role in LTD immediately following eye closure and then promote homeostasis during prolonged MD by sliding the plasticity threshold in favor of LTP.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model

Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up.

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.

Astrocytes enhance plasticity response during reversal learning

Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this mechanism leads to a biophysical model of synaptic plasticity consistent with the phenomenological model known as the BCM model. The resulting mathematical framework can explain the learning deficit observed in mice upon disruption of the D-serine regulatory mechanism. It shows that D-serine enhances plasticity during reversal learning, ensuring fast responses to changes in the external environment. The model provides new testable predictions about the learning process, driving our understanding of the functional role of neuron-glia interaction in learning.

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.

Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons

To mimic the homeostatic functionality of biological neurons, a split-gate field-effect transistor (S-G FET) with a charge trap layer is proposed within a neuron circuit. By adjusting the number of charges trapped in the Si3N4 layer, the threshold voltage (Vth) of the S-G FET changes. To prevent degradation of the gate dielectric due to program/erase pulses, the gates for read operation and Vth control were separated through the fin structure. A circuit that modulates the width and amplitude of the pulse was constructed to generate a Program/Erase pulse for the S-G FET as the output pulse of the neuron circuit. By adjusting the Vth of the neuron circuit, the firing rate can be lowered by increasing the Vth of the neuron circuit with a high firing rate. To verify the performance of the neural network based on S-G FET, a simulation of online unsupervised learning and classification in a 2-layer SNN is performed. The results show that the recognition rate was improved by 8% by increasing the threshold of the neuron circuit fired.

Learning-induced bidirectional enhancement of inhibitory synaptic metaplasticity

Training rodents in a particularly difficult olfactory-discrimination (OD) task results in the acquisition of the ability to perform the task well, termed 'rule learning'. In addition to enhanced intrinsic excitability and synaptic excitation in piriform cortex pyramidal neurons, rule learning results in increased synaptic inhibition across the whole cortical network to the point where it precisely maintains the balance between inhibition and excitation. The mechanism underlying such precise inhibitory enhancement remains to be explored. Here, we use brain slices from transgenic mice (VGAT-ChR2-EYFP), enabling optogenetic stimulation of single GABAergic neurons and recordings of unitary synaptic events in pyramidal neurons. Quantal analysis revealed that learning-induced enhanced inhibition is mediated by increased quantal size of the evoked inhibitory events. Next, we examined the plasticity of synaptic inhibition induced by long-lasting, intrinsically evoked spike firing in post-synaptic neurons. Repetitive depolarizing current pulses from depolarized (-70 mV) or hyperpolarized (-90 mV) membrane potentials induced long-term depression (LTD) and long-term potentiation (LTP) of synaptic inhibition, respectively. We found a profound bidirectional increase in the ability to induce both LTD, mediated by L-type calcium channels, and LTP, mediated by R-type calcium channels after rule learning. Blocking the GABAB receptor reversed the effect of intrinsic stimulation at -90 mV from LTP to LTD. We suggest that learning greatly enhances the ability to modify the strength of synaptic inhibition of principal neurons in both directions. Such plasticity of synaptic plasticity allows fine-tuning of inhibition on each particular neuron, thereby stabilizing the network while maintaining the memory of the rule. KEY POINTS: Olfactory discrimination rule learning results in long-lasting enhancement of synaptic inhibition on piriform cortex pyramidal neurons. Quantal analysis of unitary inhibitory synaptic events, evoked by optogenetic minimal stimulation, revealed that enhanced synaptic inhibition is mediated by increased quantal size. Surprisingly, metaplasticity of synaptic inhibition, induced by intrinsically evoked repetitive spike firing, is increased bidirectionally. The susceptibility to both long-term depression (LTD) and long-term potentiation (LTP) of inhibition is enhanced after learning. LTD of synaptic inhibition is mediated by L-type calcium channels and LTP by R-type calcium channels. LTP is also dependent on activation of GABAB receptors. We suggest that learning-induced changes in the metaplasticity of synaptic inhibition enable the fine-tuning of inhibition on each particular neuron, thereby stabilizing the network while maintaining the memory of the rule.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

Targeting metaplasticity mechanisms to promote sustained antidepressant actions

The discovery that subanesthetic doses of (R, S)-ketamine (ketamine) and (S)-ketamine (esketamine) rapidly induce antidepressant effects and promote sustained actions following drug clearance in depressed patients who are treatment-resistant to other therapies has resulted in a paradigm shift in the conceptualization of how rapidly and effectively depression can be treated. Consequently, the mechanism(s) that next generation antidepressants may engage to improve pathophysiology and resultant symptomology are being reconceptualized. Impaired excitatory glutamatergic synapses in mood-regulating circuits are likely a substantial contributor to the pathophysiology of depression. Metaplasticity is the process of regulating future capacity for plasticity by priming neurons with a stimulation that alters later neuronal plasticity responses. Accordingly, the development of treatment modalities that specifically modulate the duration, direction, or magnitude of glutamatergic synaptic plasticity events such as long-term potentiation (LTP), defined here as metaplastogens, may be an effective approach to reverse the pathophysiology underlying depression and improve depression symptoms. We review evidence that the initiating mechanisms of pharmacologically diverse rapid-acting antidepressants (i.e., ketamine mimetics) converge on consistent downstream molecular mediators that facilitate the expression/maintenance of increased synaptic strength and resultant persisting antidepressant effects. Specifically, while the initiating mechanisms of these therapies may differ (e.g., cell type-specificity, N-methyl-D-aspartate receptor (NMDAR) subtype-selective inhibition vs activation, metabotropic glutamate receptor 2/3 antagonism, AMPA receptor potentiation, 5-HT receptor-activating psychedelics, etc.), the sustained therapeutic mechanisms of putative rapid-acting antidepressants will be mediated, in part, by metaplastic effects that converge on consistent molecular mediators to enhance excitatory neurotransmission and altered capacity for synaptic plasticity. We conclude that the convergence of these therapeutic mechanisms provides the opportunity for metaplasticity processes to be harnessed as a druggable plasticity mechanism by next-generation therapeutics. Further, targeting metaplastic mechanisms presents therapeutic advantages including decreased dosing frequency and associated diminished adverse responses by eliminating the requirement for the drug to be continuously present.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.

Target Engagement and Brain State Dependence of Transcranial Magnetic Stimulation: Implications for Clinical Practice

Transcranial magnetic stimulation (TMS) is capable of noninvasively inducing lasting neuroplastic changes when applied repetitively across multiple treatment sessions. In recent years, repetitive TMS has developed into an established evidence-based treatment for various neuropsychiatric disorders such as depression. Despite significant advancements in our understanding of the mechanisms of action of TMS, there is still much to learn about how these mechanisms relate to the clinical effects observed in patients. If there is one thing about TMS that we know for sure, it is that TMS effects are state dependent. In this review, we describe how the effects of TMS on brain networks depend on various factors, including cognitive brain state, oscillatory brain state, and recent brain state history. These states play a crucial role in determining the effects of TMS at the moment of stimulation and are therefore directly linked to what is referred to as target engagement in TMS therapy. There is no control over target engagement without considering the different brain state dependencies of our TMS intervention. Clinical TMS protocols are largely ignoring this fundamental principle, which may explain the large variability and often still limited efficacy of TMS treatments. We propose that after almost 30 years of research on state dependency of TMS, it is time to change standard clinical practice by taking advantage of this fundamental principle. Rather than ignoring TMS state dependency, we can use it to our clinical advantage to improve the effectiveness of TMS treatments.

Learning what matters: Synaptic plasticity with invariance to second-order input correlations

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Remote neurocognitive interventions for attention-deficit/hyperactivity disorder - Opportunities and challenges

Improving neurocognitive functions through remote interventions has been a promising approach to developing new treatments for attention-deficit/hyperactivity disorder (AD/HD). Remote neurocognitive interventions may address the shortcomings of the current prevailing pharmacological therapies for AD/HD, e.g., side effects and access barriers. Here we review the current options for remote neurocognitive interventions to reduce AD/HD symptoms, including cognitive training, EEG neurofeedback training, transcranial electrical stimulation, and external cranial nerve stimulation. We begin with an overview of the neurocognitive deficits in AD/HD to identify the targets for developing interventions. The role of neuroplasticity in each intervention is then highlighted due to its essential role in facilitating neuropsychological adaptations. Following this, each intervention type is discussed in terms of the critical details of the intervention protocols, the role of neuroplasticity, and the available evidence. Finally, we offer suggestions for future directions in terms of optimizing the existing intervention protocols and developing novel protocols.

The effect of ketamine on synaptic mistuning induced by impaired glutamate reuptake

Mistuning of synaptic transmission has been proposed to underlie many psychiatric disorders, with decreased reuptake of the excitatory neurotransmitter glutamate as one contributing factor. Synaptic tuning occurs through several diverging and converging forms of plasticity. By recording evoked field postsynaptic potentials in the CA1 area in hippocampal slices, we found that inhibiting glutamate transporters using DL-TBOA causes retuning of synaptic transmission, resulting in a new steady state with reduced synaptic strength and a lower threshold for inducing long-term synaptic potentiation (LTP). Moreover, a similar reduced threshold for LTP was observed in a rat model of depression with decreased levels of glutamate transporters. Most importantly, we found that the antidepressant ketamine counteracts the effects of increased glutamate on the various steps involved in synaptic retuning. We, therefore, propose that ketamine's mechanism of action as an antidepressant is to restore adequate synaptic tuning.

Moiré synaptic transistor with room-temperature neuromorphic functionality

Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.

Disrupted Cortical Homeostatic Plasticity Due to Prolonged Capsaicin-induced Pain

Homeostatic plasticity (HP) regulates cortical excitability (CE) stability but is disrupted in persistent pain conditions. This study investigated how prolonged experimental pain affects HP and if pain relief modulates disrupted HP. Twenty-four healthy participants were randomised into a PainRelief or NoPainRelief group and attended four sessions; two sessions on consecutive days, separated by two weeks. Transcranial magnetic stimulation motor-evoked potentials reflecting CE and quantitative sensory testing (QST) measures were recorded. A capsaicin (pain condition) or placebo (control condition) patch was applied to the hand. HP was induced by cathodal-cathodal transcranial direct current stimulation (HP1) with CE assessment before and after. The PainRelief group had ice applied to the patch, while the NoPainRelief group waited for five minutes; subsequently another HP induction (HP2) and CE assessment were performed. After 24 h with the patch on, HP induction (HP3), QST, and CE recordings were repeated. Capsaicin reduced CE and the pain condition showed disrupted homeostatic responses at all time points (HP1: showed CE inhibition instead of facilitation; HP2 & HP3: lack of CE facilitation). Conversely, homeostatic responses were induced at all time points for the placebo condition. Capsaicin pain disrupts HP which is not restored by ice-induced pain relief. Future research may explore the prevention of HP disruption by targeting capsaicin-induced nociception but not pain perception.

The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object representations in computational models, raising the question of how the brain achieves such processing. In the present study, we show that combining Hebbian plasticity with a predictive form of plasticity leads to invariant representations in deep neural network models. We derive a local learning rule that generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity. Finally, our model accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Thus, we provide a plausible normative theory emphasizing the importance of predictive plasticity mechanisms for successful representational learning.

Threshold plasticity of SOI-GST microring resonators

Spiking Neural Networks, also known as third generation Artificial Neural Networks, have widely attracted more attention because of their advantages of behaving more biologically interpretable and being more suitable for hardware implementation. Apart from using traditional synaptic plasticity, neural networks can also be based on threshold plasticity, achieving similar functionality. This can be implemented using e.g. the Bienenstock, Cooper and Munro rule. This is a classical unsupervised learning mechanism in which the threshold is closely related to the output of the post-synaptic neuron. We show in simulations that the threshold characteristics of the nonlinear effects of a microring resonator integrated with Ge2Sb2Te5 demonstrate some complex dependencies on the intracavity refractive index, attenuation, and wavelength detuning of the incident optical pulse, and exhibit class II excitability. We also show that we are able to modify the threshold power of the microring resonator by the changes of the refractive index and loss of Ge2Sb2Te5, due to transitions between the crystalline and amorphous states. Simulations show that the presented device exhibits both excitatory and inhibitory learning behavior, either lowering or raising the threshold.

Intermittent tetraburst stimulation combined with transcranial direct current stimulation once weekly for treatment-resistant depression: a case report

BACKGROUND

Single-time non-invasive brain stimulation was carried out using the two-technique approach on a patient suffering from treatment-resistant depression. Five treatment sessions given at weekly intervals resulted in a significant improvement in the Patient Health Questionnaire-9 score for up to 6 weeks. The findings of this study could pave the way for a more efficient less resource-intensive time- and budget-saving technique of employing non-invasive brain stimulation for patients with treatment-resistant depression by minimizing the number of stimulation sessions.

CASE PRESENTATION

A 67-year-old married non-Latino white American woman suffering from treatment-resistant depression received intermittent tetraburst stimulation in combination with transcranial direct current stimulation weekly for 5 consecutive weeks. Diagnostic transcranial magnetic stimulation showed an observable electrophysiological change. The patient reported a drastic improvement in Patient Health Questionnaire-9 score up until 6-week follow-up and expressed satisfaction with the treatment.

CONCLUSIONS

This case study suggests that a streamlined protocol for using non-invasive brain stimulation could prove more effective for patients and healthcare providers in terms of safety in comparison to the present guidelines.

Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces

Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.

Synaptic memory and CaMKII

Ca2+/calmodulin-dependent protein kinase II (CaMKII) and long-term potentiation (LTP) were discovered within a decade of each other and have been inextricably intertwined ever since. However, like many marriages, it has had its up and downs. Based on the unique biochemical properties of CaMKII, it was proposed as a memory molecule before any physiological linkage was made to LTP. However, as reviewed here, the convincing linkage of CaMKII to synaptic physiology and behavior took many decades. New technologies were critical in this journey, including in vitro brain slices, mouse genetics, single-cell molecular genetics, pharmacological reagents, protein structure, and two-photon microscopy, as were new investigators attracted by the exciting challenge. This review tracks this journey and assesses the state of this marriage 40 years on. The collective literature impels us to propose a relatively simple model for synaptic memory involving the following steps that drive the process: 1) Ca2+ entry through N-methyl-d-aspartate (NMDA) receptors activates CaMKII. 2) CaMKII undergoes autophosphorylation resulting in constitutive, Ca2+-independent activity and exposure of a binding site for the NMDA receptor subunit GluN2B. 3) Active CaMKII translocates to the postsynaptic density (PSD) and binds to the cytoplasmic C-tail of GluN2B. 4) The CaMKII-GluN2B complex initiates a structural rearrangement of the PSD that may involve liquid-liquid phase separation. 5) This rearrangement involves the PSD-95 scaffolding protein, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), and their transmembrane AMPAR-regulatory protein (TARP) auxiliary subunits, resulting in an accumulation of AMPARs in the PSD that underlies synaptic potentiation. 6) The stability of the modified PSD is maintained by the stability of the CaMKII-GluN2B complex. 7) By a process of subunit exchange or interholoenzyme phosphorylation CaMKII maintains synaptic potentiation in the face of CaMKII protein turnover. There are many other important proteins that participate in enlargement of the synaptic spine or modulation of the steps that drive and maintain the potentiation. In this review we critically discuss the data underlying each of the steps. As will become clear, some of these steps are more firmly grounded than others, and we provide suggestions as to how the evidence supporting these steps can be strengthened or, based on the new data, be replaced. Although the journey has been a long one, the prospect of having a detailed cellular and molecular understanding of learning and memory is at hand.

Human deprivation amblyopia: treatment insights from animal models

Amblyopia is a common visual impairment that develops during the early years of postnatal life. It emerges as a sequela to eye misalignment, an imbalanced refractive state, or obstruction to form vision. All of these conditions prevent normal vision and derail the typical development of neural connections within the visual system. Among the subtypes of amblyopia, the most debilitating and recalcitrant to treatment is deprivation amblyopia. Nevertheless, human studies focused on advancing the standard of care for amblyopia have largely avoided recruitment of patients with this rare but severe impairment subtype. In this review, we delineate characteristics of deprivation amblyopia and underscore the critical need for new and more effective therapy. Animal models offer a unique opportunity to address this unmet need by enabling the development of unconventional and potent amblyopia therapies that cannot be pioneered in humans. Insights derived from studies using animal models are discussed as potential therapeutic innovations for the remediation of deprivation amblyopia. Retinal inactivation is highlighted as an emerging therapy that exhibits efficacy against the effects of monocular deprivation at ages when conventional therapy is ineffective, and recovery occurs without apparent detriment to the treated eye.

Efficient simulation of neural development using shared memory parallelization

The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of brain development: morphological growth, migration, and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle has agents called fronts execute model-specific code. In the case of a growing dendritic or axonal front, this will be a choice between extension, branching, or growth termination. Somatic fronts can migrate to new positions and any front can be retracted to prune parts of neurons. Collision detection prevents new or migrating fronts from overlapping with existing ones. NeuroDevSim is a multi-core program that uses an innovative shared memory approach to achieve parallel processing without messaging. We demonstrate linear strong parallel scaling up to 96 cores for large models and have run these successfully on 128 cores. Most of the shared memory parallelism is achieved without memory locking. Instead, cores have only write privileges to private sections of arrays, while being able to read the entire shared array. Memory conflicts are avoided by a coding rule that allows only active fronts to use methods that need writing access. The exception is collision detection, which is needed to avoid the growth of physically overlapping structures. For collision detection, a memory-locking mechanism was necessary to control access to grid points that register the location of nearby fronts. A custom approach using a serialized lock broker was able to manage both read and write locking. NeuroDevSim allows easy modeling of most aspects of neural development for models simulating a few complex or thousands of simple neurons or a mixture of both.

Code available at

https://github.com/CNS-OIST/NeuroDevSim.

Long-Term-But Not Short-Term-Plasticity at the Mossy Fiber-CA3 Pyramidal Cell Synapse in Hippocampus Is Altered in M1/M3 Muscarinic Acetylcholine Receptor Double Knockout Mice

Muscarinic acetylcholine receptors are well-known for their crucial involvement in hippocampus-dependent learning and memory, but the exact roles of the various receptor subtypes (M1-M5) are still not fully understood. Here, we studied how M1 and M3 receptors affect plasticity at the mossy fiber (MF)-CA3 pyramidal cell synapse. In hippocampal slices from M1/M3 receptor double knockout (M1/M3-dKO) mice, the signature short-term plasticity of the MF-CA3 synapse was not significantly affected. However, the rather unique NMDA receptor-independent and presynaptic form of long-term potentiation (LTP) of this synapse was much larger in M1/M3-deficient slices compared to wild-type slices in both field potential and whole-cell recordings. Consistent with its presynaptic origin, induction of MF-LTP strongly enhanced the excitatory drive onto single CA3 pyramidal cells, with the effect being more pronounced in M1/M3-dKO cells. In an earlier study, we found that the deletion of M2 receptors in mice disinhibits MF-LTP in a similar fashion, suggesting that endogenous acetylcholine employs both M1/M3 and M2 receptors to constrain MF-LTP. Importantly, such synergism was not observed for MF long-term depression (LTD). Low-frequency stimulation, which reliably induced LTD of MF synapses in control slices, failed to do so in M1/M3-dKO slices and gave rise to LTP instead. In striking contrast, loss of M2 receptors augmented LTD when compared to control slices. Taken together, our data demonstrate convergence of M1/M3 and M2 receptors on MF-LTP, but functional divergence on MF-LTD, with the net effect resulting in a well-balanced bidirectional plasticity of the MF-CA3 pyramidal cell synapse.

The temporal pattern of synaptic activation determines the longevity of structural plasticity at dendritic spines

Learning is thought to involve physiological and structural changes at individual synapses. Synaptic plasticity has predominantly been studied using regular stimulation patterns, but neuronal activity in the brain normally follows a Poisson distribution. We used two-photon imaging and glutamate uncaging to investigate the structural plasticity of single dendritic spines using naturalistic activation patterns sampled from a Poisson distribution. We showed that naturalistic activation patterns elicit structural plasticity that is both NMDAR and protein synthesis-dependent. Furthermore, we uncovered that the longevity of structural plasticity is dependent on the temporal structure of the naturalistic pattern. Finally, we found that during the delivery of the naturalistic activity, spines underwent rapid structural growth that predicted the longevity of plasticity. This was not observed with regularly spaced activity. These data reveal that different temporal organizations of the same number of synaptic stimulations can produce rather distinct short and long-lasting structural plasticity.

LOV2-based photoactivatable CaMKII and its application to single synapses: Local Optogenetics

Optogenetic techniques offer a high spatiotemporal resolution to manipulate cellular activity. For instance, Channelrhodopsin-2 with global light illumination is the most widely used to control neuronal activity at the cellular level. However, the cellular scale is much larger than the diffraction limit of light (<1 μm) and does not fully exploit the features of the "high spatial resolution" of optogenetics. For instance, until recently, there were no optogenetic methods to induce synaptic plasticity at the level of single synapses. To address this, we developed an optogenetic tool named photoactivatable CaMKII (paCaMKII) by fusing a light-sensitive domain (LOV2) to CaMKIIα, which is a protein abundantly expressed in neurons of the cerebrum and hippocampus and essential for synaptic plasticity. Combining photoactivatable CaMKII with two-photon excitation, we successfully activated it in single spines, inducing synaptic plasticity (long-term potentiation) in hippocampal neurons. We refer to this method as "Local Optogenetics", which involves the local activation of molecules and measurement of cellular responses. In this review, we will discuss the characteristics of LOV2, the recent development of its derivatives, and the development and application of paCaMKII.

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

Partial Recovery of Amblyopia After Fellow Eye Ischemic Optic Neuropathy

BACKGROUND

Recovery from amblyopia in adulthood after fellow eye (FE) vision loss is a well-known phenomenon. Incidence of recovery varies widely following different FE pathologies, and the rate of recovery after FE ischemic optic neuropathy (ION) has not been examined. We aimed to determine the frequency and degree of improvement in amblyopic eye (AE) visual function after ION in the FE.

METHODS

We performed a retrospective chart review of patients between 2007 and 2021 confirmed to have amblyopia and ischemic optic neuropathy in different eyes. Patients with unstable ocular pathology potentially limiting vision were excluded. We compared the best-corrected visual acuity (VA) in each eye before and after FE ION over time. For patients with available data, we examined change in perimetric performance over time.

RESULTS

Among the 12 patients who met the inclusion criteria (mean age 67 ± 8 years), 9 (75%) improved ≥1 line and 2 (17%) improved ≥3 lines. The median time from ION symptom onset to maximal improvement was 6 months (range: 2-101 months). Reliable perimetric data were available for 6 patients. Mean sensitivity improved in the AE for all patients, with mean improvement of 1.9 ± 1.1 dB. There was no correspondence between foci of ION-related field loss and gains in field sensitivity in the AE.

CONCLUSIONS

A high proportion of patients with amblyopia and contralateral ION experience improvement in AEVA. Modest gains in perimetric sensitivity in the AE may accompany FE ION. These findings support the view that residual plasticity in the adult visual cortex can be tapped to support functional improvement in amblyopia.

Putative roles for homeostatic plasticity in epileptogenesis

Homeostatic plasticity allows neural circuits to maintain an average activity level while preserving the ability to learn new associations and efficiently transmit information. This dynamic process usually protects the brain from excessive activity, like seizures. However, in certain contexts, homeostatic plasticity might produce seizures, either in response to an acute provocation or more chronically as a driver of epileptogenesis. Here, we review three seizure conditions in which homeostatic plasticity likely plays an important role: acute drug withdrawal seizures, posttraumatic or disconnection epilepsy, and cyclic seizures. Identifying the homeostatic mechanisms active at different stages of development and in different circuits could allow better targeting of therapies, including determining when neuromodulation might be most effective, proposing ways to prevent epileptogenesis, and determining how to disrupt the cycle of recurring seizure clusters.

Visual Cortical Plasticity: Molecular Mechanisms as Revealed by Induction Paradigms in Rodents

Assessing the molecular mechanism of synaptic plasticity in the cortex is vital for identifying potential targets in conditions marked by defective plasticity. In plasticity research, the visual cortex represents a target model for intense investigation, partly due to the availability of different in vivo plasticity-induction protocols. Here, we review two major protocols: ocular-dominance (OD) and cross-modal (CM) plasticity in rodents, highlighting the molecular signaling pathways involved. Each plasticity paradigm has also revealed the contribution of different populations of inhibitory and excitatory neurons at different time points. Since defective synaptic plasticity is common to various neurodevelopmental disorders, the potentially disrupted molecular and circuit alterations are discussed. Finally, new plasticity paradigms are presented, based on recent evidence. Stimulus-selective response potentiation (SRP) is one of the paradigms addressed. These options may provide answers to unsolved neurodevelopmental questions and offer tools to repair plasticity defects.

Artificial synapse based on 1,4-diphenylbutadiyne with femtojoule energy consumption

Memristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Especially, organic small molecule artificial synapses show great promise for low-energy neuromorphic devices. In this study, the basic functions of biological synapses including paired-pulse facilitation/paired-pulse depression (PPF/PPD), spike rate-dependent plasticity (SRDP) and fast Bienenstock-Cooper-Munro learning rules (BCM) have been successfully simulated in the 1,4-diphenylbutadiyne (DPDA) memristor device. Furthermore, ultra-low energy consumption (∼25 fJ per spike), linear and large conductance changes have been obtained in the small molecule DPDA device. This work makes a great contribution to improve the accuracy, speed and to reduce the energy consumption for neuromorphic computing.

Emerging electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.