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

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.

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.

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.

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.

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.

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 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.

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.

Metaplasticity framework for cross-modal synaptic plasticity in adults

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

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

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

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

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

Epigenetic signature in neural plasticity: the journey so far and journey ahead

Neural plasticity is a remarkable characteristic of the brain which allows neurons to rewire their structure in response to internal and external stimuli. Many external stimuli collectively referred to as 'epigenetic factors' strongly influence structural and functional reorganization of the brain, thereby acting as a potential driver of neural plasticity. DNA methylation and demethylation, histone acetylation, and deacetylation are some of the frontline epigenetic mechanisms behind neural plasticity. Epigenetic signature molecules (mostly proteins) play a pivotal role in epigenetic reprogramming. Though neuro-epigenetics is an incredibly important field of emerging research, the critical role of signature proteins associated with epigenetic alteration and their involvement in neural plasticity needs further attention. This study gives an integrated and systematic overview of the current state of knowledge with a clear idea of types of neural plasticity and the context-dependent role of epigenetic signature molecules and their modulation by some natural bioactive compounds.

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.

Synaptic memory survives molecular turnover

Activation of Ca²⁺/calmodulin-dependent kinase II (CaMKII) plays a critical role in long-term potentiation (LTP), a long accepted cellular model for learning and memory. However, how LTP and memories survive the turnover of synaptic proteins, particularly CaMKII, remains a mystery. Here, we take advantage of the finding that constitutive Ca²⁺-independent CaMKII activity, acquired prior to slice preparation, provides a lasting memory trace at synapses. In slice culture, this persistent CaMKII activity, in the absence of Ca²⁺ stimulation, remains stable over a 2-wk period, well beyond the turnover of CaMKII protein. We propose that the nascent CaMKII protein present at 2 wk acquired its activity from preexisting active CaMKII molecules, which transferred their activity to newly synthesized CaMKII molecules and thus maintain the memory in the face of protein turnover.

NMDA Receptor C-Terminal Domain Signalling in Development, Maturity, and Disease

The NMDA receptor is a Ca²⁺-permeant glutamate receptor which plays key roles in health and disease. Canonical NMDARs contain two GluN2 subunits, of which 2A and 2B are predominant in the forebrain. Moreover, the relative contribution of 2A vs. 2B is controlled both developmentally and in an activity-dependent manner. The GluN2 subtype influences the biophysical properties of the receptor through difference in their N-terminal extracellular domain and transmembrane regions, but they also have large cytoplasmic Carboxyl (C)-terminal domains (CTDs) which have diverged substantially during evolution. While the CTD identity does not influence NMDAR subunit specific channel properties, it determines the nature of CTD-associated signalling molecules and has been implicated in mediating the control of subunit composition (2A vs. 2B) at the synapse. Historically, much of the research into the differential function of GluN2 CTDs has been conducted in vitro by over-expressing mutant subunits, but more recently, the generation of knock-in (KI) mouse models have allowed CTD function to be probed in vivo and in ex vivo systems without heterologous expression of GluN2 mutants. In some instances, findings involving KI mice have been in disagreement with models that were proposed based on earlier approaches. This review will examine the current research with the aim of addressing these controversies and how methodology may contribute to differences between studies. We will also discuss the outstanding questions regarding the role of GluN2 CTD sequences in regulating NMDAR subunit composition, as well as their relevance to neurodegenerative disease and neurodevelopmental disorders.

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI₃ solar cell directly stacking on the CsPbBr₂I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

Designing efficient bio-inspired vision systems remains a challenge. Here, the authors report a bio-inspired striate visual cortex with binocular and orientation selective receptive field based on self-powered memristor to enable machine vision with brisk edge and corner detection in the future applications.

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

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

Neural Information Processing and Computations of Two-Input Synapses

Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological neurons are capable of nonlinear computations for many converging synaptic inputs via homo- and heterosynaptic mechanisms. This nonlinear neuronal computation may play an important role in complex information processing at the neural circuit level. Here we characterize the dynamics and coding properties of neuron models on synaptic transmissions delivered from two hidden states. The neuronal information processing is influenced by the cooperative and competitive interactions among synapses and the coherence of the hidden states. Furthermore, we demonstrate that neuronal information processing under two-input synaptic transmission can be mapped to linearly nonseparable XOR as well as basic AND/OR operations. In particular, the mixtures of linear and nonlinear neuron models outperform the fashion-MNIST test compared to the neural networks consisting of only one type. This study provides a computational framework for assessing information processing of neuron and synapse models that may be beneficial for the design of brain-inspired artificial intelligence algorithms and neuromorphic systems.

Tuning brain networks: The emerging role of transcranial direct current stimulation on structural plasticity

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique (NIBS) that has been proven to promote beneficial effects in a range of neurological and psychiatric disorders. Unfortunately, although has been widely investigated, the mechanism comprehension around tDCS effects presents still some gaps. Therefore, scientists are still trying to uncover the cellular and molecular mechanisms behind its positive effects to permit a more suitable application. Experimental models have provided converging evidence that tDCS elicits improvements in learning and memory by modulating both excitability and synaptic plasticity in neurons. Recently, among tDCS neurobiological effects, neural synchronization and dendritic structural changes have been reported in physiological and pathological conditions, suggesting possible effects at the neuronal circuit level. In this review, we bring in to focus the emerging effects of tDCS on the structural plasticity changes and neuronal rewiring, with the intent to match these two aspects with the underpinning molecular mechanisms identified so far, providing a new perspective to work on to unveil novel tDCS therapeutic use to treat brain dysfunctions.

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.