Short-term synaptic plasticity

Activation of TRPV1 Channels Inhibits the Release of Acetylcholine and Improves Muscle Contractility in Mice

TRPV1 represents a non-selective transient receptor potential cation channel found not only in sensory neurons, but also in motor nerve endings and in skeletal muscle fibers. However, the role of TRPV1 in the functioning of the neuromuscular junction has not yet been fully established. In this study, the Levator Auris Longus muscle preparations were used to assess the effect of pharmacological activation of TRPV1 channels on neuromuscular transmission. The presence of TRPV1 channels in the nerve terminal and in the muscle fiber was confirmed by immunohistochemistry. It was verified by electrophysiology that the TRPV1 channel agonist capsaicin inhibits the acetylcholine release, and this effect was completely absent after preliminary application of the TRPV1 channel blocker SB 366791. Nerve stimulation revealed an increase of amplitude of isometric tetanic contractions upon application of capsaicin which was also eliminated after preliminary application of SB 366791. Similar data were obtained during direct muscle stimulation. Thus, pharmacological activation of TRPV1 channels affects the functioning of both the pre- and postsynaptic compartment of the neuromuscular junction. A moderate decrease in the amount of acetylcholine released from the motor nerve allows to maintain a reserve pool of the mediator to ensure a longer signal transmission process, and an increase in the force of muscle contraction, in its turn, also implies more effective physiological muscle activity in response to prolonged stimulation. This assumption is supported by the fact that when muscle was indirect stimulated with a fatigue protocol, muscle fatigue was attenuated in the presence of capsaicin.

Molecular definition of distinct active zone protein machineries for Ca2+ channel clustering and synaptic vesicle priming

Action potentials trigger neurotransmitter release with minimal delay. Active zones mediate this temporal precision by co-organizing primed vesicles with CaV2 Ca2+ channels. The presumed model is that scaffolding proteins directly tether primed vesicles to CaV2s. We find that CaV2 clustering and vesicle priming are executed by separate machineries. At hippocampal synapses, CaV2 nanoclusters are positioned at variable distances from those of the priming protein Munc13. The active zone organizer RIM anchors both proteins, but distinct interaction motifs independently execute these functions. In heterologous cells, Liprin-α and RIM from co-assemblies that are separate from CaV2-organizing complexes upon co-transfection. At synapses, Liprin-α1-4 knockout impairs vesicle priming, but not CaV2 clustering. The cell adhesion protein PTPσ recruits Liprin-α, RIM and Munc13 into priming complexes without co-clustering of CaV2s. We conclude that active zones consist of distinct complexes to organize CaV2s and vesicle priming, and Liprin-α and PTPσ specifically support priming site assembly.

The release of inhibition model reproduces kinetics and plasticity of neurotransmitter release in central synapses

Calcium-evoked release of neurotransmitters from synaptic vesicles (SVs) is catalysed by SNARE proteins. The predominant view is that, at rest, complete assembly of SNARE complexes is inhibited ('clamped') by synaptotagmin and complexin molecules. Calcium binding by synaptotagmins releases this fusion clamp and triggers fast SV exocytosis. However, this model has not been quantitatively tested over physiological timescales. Here we describe an experimentally constrained computational modelling framework to quantitatively assess how the molecular architecture of the fusion clamp affects SV exocytosis. Our results argue that the 'release-of-inhibition' model can indeed account for fast calcium-activated SV fusion, and that dual binding of synaptotagmin-1 and synaptotagmin-7 to the same SNARE complex enables synergistic regulation of the kinetics and plasticity of neurotransmitter release. The developed framework provides a powerful and adaptable tool to link the molecular biochemistry of presynaptic proteins to physiological data and efficiently test the plausibility of calcium-activated neurotransmitter release models.

In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfOx-Based Neuristor Array

With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.

Dopamine-Regulated Plasticity in MoO3 Synaptic Transistors

Field-effect transistor-based biosensors have gained increasing interest due to their reactive surface to external stimuli and the adaptive feedback required for advanced sensing platforms in biohybrid neural interfaces. However, complex probing methods for surface functionalization remain a challenge that limits the industrial implementation of such devices. Herein, a simple, label-free biosensor based on molybdenum oxide (MoO3) with dopamine-regulated plasticity is demonstrated. Dopamine oxidation facilitated locally at the channel surface initiates a charge transfer mechanism between the molecule and the oxide, altering the channel conductance and successfully emulating the tunable synaptic weight by neurotransmitter activity. The oxygen level of the channel is shown to heavily affect the device's electrochemical properties, shifting from a nonreactive metallic characteristic to highly responsive semiconducting behavior. Controllable responsivity is achieved by optimizing the channel's dimension, which allows the devices to operate in wide ranges of dopamine concentration, from 100 nM to sub-mM levels, with excellent selectivity compared with K+, Na+, and Ca2+.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Impaired pre-synaptic plasticity and visual responses in auxilin-knockout mice

Auxilin (DNAJC6/PARK19), an endocytic co-chaperone, is essential for maintaining homeostasis in the readily releasable pool (RRP) by aiding clathrin-mediated uncoating of synaptic vesicles. Its loss-of-function mutations, observed in familial Parkinson's disease (PD), lead to basal ganglia motor deficits and cortical dysfunction. We discovered that auxilin-knockout (Aux-KO) mice exhibited impaired pre-synaptic plasticity in layer 4 to layer 2/3 pyramidal cell synapses in the primary visual cortex (V1), including reduced short-term facilitation and depression. Computational modeling revealed increased RRP refilling during short repetitive stimulation, which diminished during prolonged stimulation. Silicon probe recordings in V1 of Aux-KO mice demonstrated disrupted visual cortical circuit responses, including reduced orientation selectivity, compromised visual mismatch negativity, and shorter visual familiarity-evoked theta oscillations. Pupillometry analysis revealed an impaired optokinetic response. Auxilin-dependent pre-synaptic endocytosis dysfunction was associated with deficits in pre-synaptic plasticity, visual cortical functions, and eye movement prodromally or at the early stage of motor symptoms.

Input-specific synaptic depression shapes temporal integration in mouse visual cortex

Efficient sensory processing requires the nervous system to adjust to ongoing features of the environment. In primary visual cortex (V1), neuronal activity strongly depends on recent stimulus history. Existing models can explain effects of prolonged stimulus presentation but remain insufficient for explaining effects observed after shorter durations commonly encountered under natural conditions. We investigated the mechanisms driving adaptation in response to brief (100 ms) stimuli in L2/3 V1 neurons by performing in vivo whole-cell recordings to measure membrane potential and synaptic inputs. We find that rapid adaptation is generated by stimulus-specific suppression of excitatory and inhibitory synaptic inputs. Targeted optogenetic experiments reveal that these synaptic effects are due to input-specific short-term depression of transmission between layers 4 and 2/3. Thus, brief stimulus presentation engages a distinct adaptation mechanism from that previously reported in response to prolonged stimuli, enabling flexible control of sensory encoding across a wide range of timescales.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

A theory of hippocampal theta correlations accounting for extrinsic and intrinsic sequences

Hippocampal place cell sequences have been hypothesized to serve as diverse purposes as the induction of synaptic plasticity, formation and consolidation of long-term memories, or navigation and planning. During spatial behaviors of rodents, sequential firing of place cells at the theta timescale (known as theta sequences) encodes running trajectories, which can be considered as one-dimensional behavioral sequences of traversed locations. In a two-dimensional space, however, each single location can be visited along arbitrary one-dimensional running trajectories. Thus, a place cell will generally take part in multiple different theta sequences, raising questions about how this two-dimensional topology can be reconciled with the idea of hippocampal sequences underlying memory of (one-dimensional) episodes. Here, we propose a computational model of cornu ammonis 3 (CA3) and dentate gyrus (DG), where sensorimotor input drives the direction-dependent (extrinsic) theta sequences within CA3 reflecting the two-dimensional spatial topology, whereas the intrahippocampal CA3-DG projections concurrently produce intrinsic sequences that are independent of the specific running trajectory. Consistent with experimental data, intrinsic theta sequences are less prominent, but can nevertheless be detected during theta activity, thereby serving as running-direction independent landmark cues. We hypothesize that the intrinsic sequences largely reflect replay and preplay activity during non-theta states.

Adaptor protein AP-3 produces synaptic vesicles that release at high frequency by recruiting phospholipid flippase ATP8A1

Neural systems encode information in the frequency of action potentials, which is then decoded by synaptic transmission. However, the rapid, synchronous release of neurotransmitters depletes synaptic vesicles (SVs), limiting release at high firing rates. How then do synapses convey information about frequency? Here, we show in mouse hippocampal neurons and slices that the adaptor protein AP-3 makes a subset of SVs that respond specifically to high-frequency stimulation. Neurotransmitter transporters slot onto these SVs in different proportions, contributing to the distinct properties of release observed at different excitatory synapses. Proteomics reveals that AP-3 targets the phospholipid flippase ATP8A1 to SVs; loss of ATP8A1 recapitulates the defect in SV mobilization at high frequency observed with loss of AP-3. The mechanism involves recruitment of synapsin by the cytoplasmically oriented phosphatidylserine translocated by ATP8A1. Thus, ATP8A1 enables the subset of SVs made by AP-3 to release at high frequency.

Linking input- and cell-type-specific synaptic plasticity to the reinforcement of alcohol-seeking behavior

The reinforcement of voluntary alcohol-seeking behavior requires dopamine-dependent long-term synaptic plasticity in the striatum. Specifically, the long-term potentiation (LTP) of direct-pathway medium spiny neurons (dMSNs) in the dorsomedial striatum (DMS) promotes alcohol drinking. However, it remains unclear whether alcohol induces input-specific plasticity onto dMSNs and whether this plasticity directly drives instrumental conditioning. In this study, we found that voluntary alcohol intake selectively strengthened glutamatergic transmission from the medial prefrontal cortex (mPFC) to DMS dMSNs in mice. Importantly, mimicking this alcohol-induced potentiation by optogenetically self-stimulating mPFC→dMSN synapse with an LTP protocol was sufficient to drive the reinforcement of lever pressing in operant chambers. Conversely, induction of a post-pre spike timing-dependent LTD at this synapse time-locked to alcohol delivery during operant conditioning persistently decreased alcohol-seeking behavior. Our results establish a causal relationship between input- and cell-type-specific corticostriatal plasticity and the reinforcement of alcohol-seeking behavior. This provides a potential therapeutic strategy to restore normal cortical control of dysregulated basal ganglia circuitries in alcohol use disorder.

BACE1 in PV interneuron tunes hippocampal CA1 local circuits and resets priming of fear memory extinction

BACE1 is the rate-limiting enzyme for β-amyloid (Aβ) production and therefore is considered a prime drug target for treating Alzheimer's disease (AD). Nevertheless, the BACE1 inhibitors failed in clinical trials, even exhibiting cognitive worsening, implying that BACE1 may function in regulating cognition-relevant neural circuits. Here, we found that parvalbumin-positive inhibitory interneurons (PV INs) in hippocampal CA1 express BACE1 at a high level. We designed and developed a mouse strain with conditional knockout of BACE1 in PV neurons. The CA1 fast-spiking PV INs with BACE1 deletion exhibited an enhanced response of postsynaptic N-methyl-D-aspartate (NMDA) receptors to local stimulation on CA1 oriens, with average intrinsic electrical properties and fidelity in synaptic integration. Intriguingly, the BACE1 deletion reorganized the CA1 recurrent inhibitory motif assembled by the heterogeneous pyramidal neurons (PNs) and the adjacent fast-spiking PV INs from the superficial to the deep layer. Moreover, the conditional BACE1 deletion impaired the AMPARs-mediated excitatory transmission of deep CA1 PNs. Further rescue experiments confirmed that these phenotypes require the enzymatic activity of BACE1. Above all, the BACE1 deletion resets the priming of the fear memory extinction. Our findings suggest a neuron-specific working model of BACE1 in regulating learning and memory circuits. The study may provide a potential path of targeting BACE1 and NMDAR together to circumvent cognitive worsening due to a single application of BACE1 inhibitor in AD patients.

Controllable Resistive Switching in ReS2 /WS2 Heterostructure for Nonvolatile Memory and Synaptic Simulation

Memristors with nonvolatile storage performance and simulated synaptic functions are regarded as one of the critical devices to overcome the bottleneck in traditional von Neumann computer architecture. 2D van der Waals heterostructures have paved a new way for the development of advanced memristors by integrating the intriguing features of different materials and offering additional controllability over their optoelectronic properties. Herein, planar memristors with both electrical and optical tunability based on ReS2 /WS2 van der Waals heterostructure are demonstrated. The devices show unique unipolar nonvolatile behavior with high Roff /Ron ratio of up to 106 , desirable endurance, and retention, which are superior to pure ReS2 and WS2 devices. When decreasing the channel length, the set voltage can be notably reduced while the high Roff /Ron ratios are retained. By introducing electrostatic doping through the gate control, the set voltage can be tailored in a wide range from 4.50 to 0.40 V. Furthermore, biological synaptic functions and plasticity, including spike rate-dependent plasticity and paired-pulse facilitation, are successfully realized. By employing optical illumination, resistive switching can also be modulated, which is dependent on the illumination energy and power. A mechanism related to the interlayer charge transfer controlled by optical excitation is revealed.

Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision

Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.

Solvent-assisted sulfur vacancy engineering method in MoS2 for a neuromorphic synaptic memristor

Recently, two-dimensional transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS2) have attracted great attention due to their unique properties. To modulate the electronic properties and structure of TMDs, it is crucial to precisely control chalcogenide vacancies and several methods have already been suggested. However, they have several limitations such as plasma damage by ion bombardment. Herein, we introduced a novel solvent-assisted vacancy engineering (SAVE) method to modulate sulfur vacancies in MoS2. Considering polarity and the Hansen solubility parameter (HSP), three solvents were selected. Sulfur vacancies can be modulated by immersing MoS2 in each solvent, supported by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy analyses. The SAVE method can further expand its application in memory devices representing memristive performance and synaptic behaviors. We represented the charge transport mechanism of sulfur vacancy migration in MoS2. The non-destructive, scalable, and novel SAVE method controlling sulfur vacancies is expected to be a guideline for constructing a vacancy engineering system of TMDs.

Multiple neuron clusters on Micro-Electrode Arrays as an in vitro model of brain network

Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.

Enhanced Synaptic Behaviors in Chitosan Electrolyte-Based Electric-Double-Layer Transistors with Poly-Si Nanowire Channel Structures

In this study, we enhance the synaptic behavior of artificial synaptic transistors by utilizing nanowire (NW)-type polysilicon channel structures. The high surface-to-volume ratio of the NW channels enables efficient modulation of the channel conductance, which is interpreted as the synaptic weight. As a result, NW-type synaptic transistors exhibit a larger hysteresis window compared to film-type synaptic transistors, even within the same gate voltage sweeping range. Moreover, NW-type synaptic transistors demonstrate superior short-term facilitation and long-term memory transition compared with film-type ones, as evidenced by the measured paired-pulse facilitation and excitatory post-synaptic current characteristics at varying frequencies and pulse numbers. Additionally, we observed gradual potentiation/depression characteristics, making these artificial synapses applicable to artificial neural networks. Furthermore, the NW-type synaptic transistors exhibit improved Modified National Institute of Standards and Technology pattern recognition rate of 91.2%. In conclusion, NW structure channels are expected to be a promising technology for next-generation artificial intelligence (AI) semiconductors, and the integration of NW structure channels has significant potential to advance AI semiconductor technology.

Linking spontaneous and stimulated spine dynamics

Our brains continuously acquire and store memories through synaptic plasticity. However, spontaneous synaptic changes can also occur and pose a challenge for maintaining stable memories. Despite fluctuations in synapse size, recent studies have shown that key population-level synaptic properties remain stable over time. This raises the question of how local synaptic plasticity affects the global population-level synaptic size distribution and whether individual synapses undergoing plasticity escape the stable distribution to encode specific memories. To address this question, we (i) studied spontaneously evolving spines and (ii) induced synaptic potentiation at selected sites while observing the spine distribution pre- and post-stimulation. We designed a stochastic model to describe how the current size of a synapse affects its future size under baseline and stimulation conditions and how these local effects give rise to population-level synaptic shifts. Our study offers insights into how seemingly spontaneous synaptic fluctuations and local plasticity both contribute to population-level synaptic dynamics.

Doping-modulated lateral asymmetric Schottky diode as a high-performance self-powered synaptic device

In the post-Moore era, the gradually saturated computational capability of conventional digital computers showing the opposite trend as the exponentially increasing data volumes imperatively required a platform or technology to break this bottleneck. Brain-inspired neuromorphic computing promises to inherently improve the efficiency of information processing and computation by means of the highly parallel hardware architecture to reduce global data transmission. Here, we demonstrate a compact device technology based on the barrier asymmetry to achieve zero-consumption self-powered synaptic devices. In order to tune the device behaviors, the typical chemical doping is used to tailor the asymmetry for energy harvesting. Finally, in our demonstrated devices, the open-circuit voltage (VOC) and power-conversion efficiency (PCE) can be modulated up to 0.77 V and 6%, respectively. Optimized photovoltaic features affords synaptic devices with an outstanding programming weight states, involving training facilitation, stimulus reinforce and consolidation. Based on self-powered system, this work further presents a highly available modulation scheme, which achieves excellent device behaviors while ensuring the zero-energy consumption.

3D-Printed Intrinsically Stretchable Organic Electrochemical Synaptic Transistor Array

Organic electrochemical transistors (OECTs) for skin-like bioelectronics require mechanical stretchability, softness, and cost-effective large-scale manufacturing. However, developing intrinsically stretchable OECTs using a simple and fast-response technique is challenging due to limitations in functional materials, substrate wettability, and integrated processing of multiple materials. In this regard, we propose a fabrication method devised by combining the preparation of a microstructured hydrophilic substrate, multi-material printing of functional inks with varying viscosities, and optimization of the device channel geometries. The resulting intrinsically stretchable OECT array with synaptic properties was successfully manufactured. These devices demonstrated high transconductance (22.5 mS), excellent mechanical softness (Young's modulus ∼ 2.2 MPa), and stretchability (∼30%). Notably, the device also exhibited artificial synapse functionality, mimicking the biological synapse with features such as paired-pulse depression, short-term plasticity, and long-term plasticity. This study showcases a promising strategy for fabricating intrinsically stretchable OECTs and provides valuable insights for the development of brain-computer interfaces.

Electrochemical Ionic Synapses: Progress and Perspectives

Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.

Myelin dystrophy in the aging prefrontal cortex leads to impaired signal transmission and working memory decline: a multiscale computational study

Normal aging leads to myelin alternations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are often correlated with cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First we built a multicompartment pyramidal neuron model fit to monkey dlPFC data, with axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions, to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination in a population of neurons. Lasso regression identified distinctive parameter sets likely to modulate an axon's susceptibility to CV changes following demyelination versus remyelination. Next we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from electron microscopy up to behavior on aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

The Addiction-Susceptibility TaqIA/Ankk1 Controls Reward and Metabolism Through D2 Receptor-Expressing Neurons

BACKGROUND

A large body of evidence highlights the importance of genetic variants in the development of psychiatric and metabolic conditions. Among these, the TaqIA polymorphism is one of the most commonly studied in psychiatry. TaqIA is located in the gene that codes for the ankyrin repeat and kinase domain containing 1 kinase (Ankk1) near the dopamine D2 receptor (D2R) gene. Homozygous expression of the A1 allele correlates with a 30% to 40% reduction of striatal D2R, a typical feature of addiction, overeating, and other psychiatric pathologies. The mechanisms by which the variant influences dopamine signaling and behavior are unknown.

METHODS

Here, we used transgenic and viral-mediated strategies to reveal the role of Ankk1 in the regulation of activity and functions of the striatum.

RESULTS

We found that Ankk1 is preferentially enriched in striatal D2R-expressing neurons and that Ankk1 loss of function in the dorsal and ventral striatum leads to alteration in learning, impulsivity, and flexibility resembling endophenotypes described in A1 carriers. We also observed an unsuspected role of Ankk1 in striatal D2R-expressing neurons of the ventral striatum in the regulation of energy homeostasis and documented differential nutrient partitioning in humans with or without the A1 allele.

CONCLUSIONS

Overall, our data demonstrate that the Ankk1 gene is necessary for the integrity of striatal functions and reveal a new role for Ankk1 in the regulation of body metabolism.

Multifunctional Crystalline InGaSnO Phototransistor Exhibiting Photosensing and Photosynaptic Behavior Using Oxygen Vacancy Engineering

A multifunctional optoelectronic device implementing photodetector, photosynapse, and photomemory is of increasing attention for neuromorphic system. This enables multiple devices to be replaced with a single device, which simplifies the structure of complex, highly integrated electronics. Here, a multifunctional c-axis-aligned crystalline indium gallium tin oxide thin-film transistor (TFT) optoelectronic device is demonstrated. The photodetecting and photosynaptic behaviors could be demonstrated by tuning of gate pulse. The device shows a high responsivity of 1.1 × 106  A W-1 to blue light (467 nm) and cutoff frequency (f-3dB ) of 2400 Hz exhibiting high frequency switching using a gate reset pulse. It is possible to implement photosynaptic behavior using persistent photoconductivity effect by applying a gate bias to make the TFT depletion mode. When potentiation and depression of synaptic weight are implemented with light pulse and gate voltage pulse, respectively, 64-state potentiation-depression curves are demonstrated with excellent nonlinearity of 1.13 and 2.03, respectively. When an artificial neural network is constructed with this device for the Modified National Institute of Standards and Technology training pattern recognition simulation, it shows a high pattern recognition accuracy of 90.4%.

2D Ferroionics: Conductive Switching Mechanisms and Transition Boundaries in Van der Waals Layered Material CuInP2 S6

The recently unfolded ferroionic phenomena in 2D van der Waals (vdW) copper-indium-thiophosphate (CuInP2 S6 or CIPS) have received widespread interest as they allow for dynamic control of conductive switching properties, which are appealing in the paradigm-shift computing. The intricate couplings between ferroelectric polarization and ionic conduction in 2D vdW CIPS facilitate the manipulation and dynamic control of conductive behaviors. However, the complex interplays and underlying mechanisms are not yet fully explored and understood. Here, by investigating polarization switching and ionic conduction in the temperature and applied electric field domains, it is discovered that the conducting mechanisms of CIPS can be divided into four distinctive states (or modes) with transitional boundaries, depending on the dynamics of Cu ions in the material. Further, it demonstrates that dynamically-tunable synaptic responsive behavior can be well implemented by governing the working-state transition. This research provides an in-depth, quantitative understanding of the complex phenomena of conductive switching in 2D vdW CIPS with coexisting ferroelectric order and ionic disorder. The developed insights in this work lay the ground for implementing high-performance, function-enriched devices for information processing, data storage, and neuromorphic computing based on the 2D ferroionic material systems.

Dynamics of neocortical networks: connectivity beyond the canonical microcircuit

The neocortical network consists of two types of excitatory neurons and a variety of GABAergic inhibitory interneurons, which are organized in distinct microcircuits providing feedforward, feedback, lateral inhibition, and disinhibition. This network is activated by layer- and cell-type specific inputs from first and higher order thalamic nuclei, other subcortical regions, and by cortico-cortical projections. Parallel and serial information processing occurs simultaneously in different intracortical subnetworks and is influenced by neuromodulatory inputs arising from the basal forebrain (cholinergic), raphe nuclei (serotonergic), locus coeruleus (noradrenergic), and ventral tegmentum (dopaminergic). Neocortical neurons differ in their intrinsic firing pattern, in their local and global synaptic connectivity, and in the dynamics of their synaptic interactions. During repetitive stimulation, synaptic connections between distinct neuronal cell types show short-term facilitation or depression, thereby activating or inactivating intracortical microcircuits. Specific networks are capable to generate local and global activity patterns (e.g., synchronized oscillations), which contribute to higher cognitive function and behavior. This review article aims to give a brief overview on our current understanding of the structure and function of the neocortical network.

Intersectin and Endophilin condensates prime synaptic vesicles for release site replenishment

Neurotransmitter is released from dedicated sites of synaptic vesicle fusion within a synapse. Following fusion, the vacated sites are replenished immediately by new vesicles for subsequent neurotransmission. These replacement vesicles are assumed to be located near release sites and used by chance. Here, we find that replacement vesicles are clustered around this region by Intersectin-1. Specifically, Intersectin-1 forms dynamic molecular condensates with Endophilin A1 near release sites and sequesters vesicles around this region. In the absence of Intersectin-1, vesicles within 20 nm of the plasma membrane are reduced, and consequently, vacated sites cannot be replenished rapidly, leading to depression of synaptic transmission. Similarly, mutations in Intersectin-1 that disrupt Endophilin A1 binding result in similar phenotypes. However, in the absence of Endophilin, this replacement pool of vesicles is available but cannot be accessed, suggesting that Endophilin A1 is needed to mobilize these vesicles. Thus, our work describes a distinct physical region within a synapse where replacement vesicles are harbored for release site replenishment.

Cortical excitability in human somatosensory and visual cortex: implications for plasticity and learning - a minireview

The balance of excitation and inhibition plays a key role in plasticity and learning. A frequently used, reliable approach to assess intracortical inhibition relies on measuring paired-pulse behavior. Moreover, recent developments of magnetic resonance spectroscopy allows measuring GABA and glutamate concentrations. We give an overview about approaches employed to obtain information about excitatory states in human participants and discuss their putative relation. We summarize paired-pulse techniques and basic findings characterizing paired-pulse suppression in somatosensory (SI) and (VI) visual areas. Paired-pulse suppression describes the effect of paired sensory stimulation at short interstimulus intervals where the cortical response to the second stimulus is significantly suppressed. Simultaneous assessments of paired-pulse suppression in SI and VI indicated that cortical excitability is not a global phenomenon, but instead reflects the properties of local sensory processing. We review studies using non-invasive brain stimulation and perceptual learning experiments that assessed both perceptual changes and accompanying changes of cortical excitability in parallel. Independent of the nature of the excitation/inhibition marker used these data imply a close relationship between altered excitability and altered performance. These results suggest a framework where increased or decreased excitability is linked with improved or impaired perceptual performance. Recent findings have expanded the potential role of cortical excitability by demonstrating that inhibition markers such as GABA concentrations, paired-pulse suppression or alpha power predict to a substantial degree subsequent perceptual learning outcome. This opens the door for a targeted intervention where subsequent plasticity and learning processes are enhanced by altering prior baseline states of excitability.

Synapse-Specific Defects in Synaptic Transmission in the Cerebellum of W246G Mutant ELOVL4 Rats-a Model of Human SCA34

Elongation of very long fatty acids-4 (ELOVL4) mediates biosynthesis of very long chain-fatty acids (VLC-FA; ≥28 carbons). Various mutations in this enzyme result in spinocerebellar ataxia-34 (SCA34). We generated a rat model of human SCA34 by knock-in of a naturally occurring c.736T>G, p.W246G mutation in the Elovl4 gene. Our previous analysis of homozygous W246G mutant ELOVL4 rats (MUT) revealed early-onset gait disturbance and impaired synaptic transmission and plasticity at parallel fiber-Purkinje cell (PF-PC) and climbing fiber-Purkinje cell (CF-PC) synapses. However, the underlying mechanisms that caused these defects remained unknown. Here, we report detailed patch-clamp recordings from Purkinje cells that identify impaired synaptic mechanisms. Our results show that miniature EPSC (mEPSC) frequency is reduced in MUT rats with no change in mEPSC amplitude, suggesting a presynaptic defect of excitatory synaptic transmission on Purkinje cells. We also find alterations in inhibitory synaptic transmission as miniature IPSC (mIPSC) frequency and amplitude are increased in MUT Purkinje cells. Paired-pulse ratio is reduced at PF-PC synapses but increased at CF-PC synapses in MUT rats, which along with results from high-frequency stimulation suggest opposite changes in the release probability at these two synapses. In contrast, we identify exaggerated persistence of EPSC amplitude at CF-PC and PF-PC synapses in MUT cerebellum, suggesting a larger readily releasable pool (RRP) at both synapses. Furthermore, the dendritic spine density is reduced in MUT Purkinje cells. Thus, our results uncover novel mechanisms of action of VLC-FA at cerebellar synapses, and elucidate the synaptic dysfunction underlying SCA34 pathology.SIGNIFICANCE STATEMENT Very long chain-fatty acids (VLC-FA) are an understudied class of fatty acids that are present in the brain. They are critical for brain function as their deficiency caused by mutations in elongation of very long fatty acids-4 (ELOVL4), the enzyme that mediates their biosynthesis, results in neurologic diseases including spinocerebellar ataxia-34 (SCA34), neuroichthyosis, and Stargardt-like macular dystrophy. In this study, we investigated the synaptic defects present in a rat model of SCA34 and identified defects in presynaptic neurotransmitter release and dendritic spine density at synapses in the cerebellum, a brain region involved in motor coordination. These results advance our understanding of the synaptic mechanisms regulated by VLC-FA and describe the synaptic dysfunction that leads to motor incoordination in SCA34.

Co-evolving dynamics and topology in a coupled oscillator model of resting brain function

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

Silicon based Bi0.9La0.1FeO3 ferroelectric tunnel junction memristor for convolutional neural network application

Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.

Metal-Oxide Heterojunction Optoelectronic Synapse and Multilevel Memory Devices Enabled by Broad Spectral Photocarrier Modulation

Broad spectral response and high photoelectric conversion efficiency are key milestones for realizing multifunctional, low-power optoelectronic devices such as artificial synapse and reconfigurable memory devices. Nevertheless, the wide bandgap and narrow spectral response of metal-oxide semiconductors are problematic for efficient metal-oxide optoelectronic devices such as photonic synapse and optical memory devices. Here, a simple titania (TiO2 )/indium-gallium-zinc-oxide (IGZO) heterojunction structure is proposed for efficient multifunctional optoelectronic devices, enabling widen spectral response range and high photoresponsivity. By overlaying a TiO2 film on IGZO, the light absorption range extends to red light, along with enhanced photoresponsivity in the full visible light region. By implementing the TiO2 /IGZO heterojunction structure, various synaptic behaviors are successfully emulated such as short-term memory/long-term memory and paired pulse facilitation. Also, the TiO2 /IGZO synaptic transistor exhibits a recognition rate up to 90.3% in recognizing handwritten digit images. Moreover, by regulating the photocarrier dynamics and retention behavior using gate-bias modulation, a reconfigurable multilevel (≥8 states) memory is demonstrated using visible light.

CPT1C is required for synaptic plasticity and oscillatory activity that supports motor, associative and non-associative learning

Carnitine palmitoyltransferase 1c (CPT1C) is a neuron-specific protein widely distributed throughout the CNS and highly expressed in discrete brain areas including the hypothalamus, hippocampus, amygdala and different motor regions. Its deficiency has recently been shown to disrupt dendritic spine maturation and AMPA receptor synthesis and trafficking in the hippocampus, but its contribution to synaptic plasticity and cognitive learning and memory processes remains mostly unknown. Here, we aimed to explore the molecular, synaptic, neural network and behavioural role of CPT1C in cognition-related functions by using CPT1C knockout (KO) mice. CPT1C-deficient mice showed extensive learning and memory deficits. The CPT1C KO animals exhibited impaired motor and instrumental learning that seemed to be related, in part, to locomotor deficits and muscle weakness but not to mood alterations. In addition, CPT1C KO mice showed detrimental hippocampus-dependent spatial and habituation memory, most probably attributable to inefficient dendritic spine maturation, impairments in long-term plasticity at the CA3-CA1 synapse and aberrant cortical oscillatory activity. In conclusion, our results reveal that CPT1C is not only crucial for motor function, coordination and energy homeostasis, but also has a crucial role in the maintenance of learning and memory cognitive functions. KEY POINTS: CPT1C, a neuron-specific interactor protein involved in AMPA receptor synthesis and trafficking, was found to be highly expressed in the hippocampus, amygdala and various motor regions. CPT1C-deficient animals exhibited energy deficits and impaired locomotion, but no mood changes were found. CPT1C deficiency disrupts hippocampal dendritic spine maturation and long-term synaptic plasticity and reduces cortical γ oscillations. CPT1C was found to be crucial for motor, associative and non-associative learning and memory.

An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.

A centronuclear myopathy-causing mutation in dynamin-2 disrupts neuronal morphology and excitatory synaptic transmission in a murine model of the disease

AIMS

Dynamin-2 is a large GTPase, a member of the dynamin superfamily that regulates membrane remodelling and cytoskeleton dynamics. Mutations in the dynamin-2 gene (DNM2) cause autosomal dominant centronuclear myopathy (CNM), a congenital neuromuscular disorder characterised by progressive weakness and atrophy of the skeletal muscles. Cognitive defects have been reported in some DNM2-linked CNM patients suggesting that these mutations can also affect the central nervous system (CNS). Here we studied how a dynamin-2 CNM-causing mutation influences the CNS function.

METHODS

Heterozygous mice harbouring the p.R465W mutation in the dynamin-2 gene (HTZ), the most common causing autosomal dominant CNM, were used as disease model. We evaluated dendritic arborisation and spine density in hippocampal cultured neurons, analysed excitatory synaptic transmission by electrophysiological field recordings in hippocampal slices, and evaluated cognitive function by performing behavioural tests.

RESULTS

HTZ hippocampal neurons exhibited reduced dendritic arborisation and lower spine density than WT neurons, which was reversed by transfecting an interference RNA against the dynamin-2 mutant allele. Additionally, HTZ mice showed defective hippocampal excitatory synaptic transmission and reduced recognition memory compared to the WT condition.

CONCLUSION

Our findings suggest that the dynamin-2 p.R465W mutation perturbs the synaptic and cognitive function in a CNM mouse model and support the idea that this GTPase plays a key role in regulating neuronal morphology and excitatory synaptic transmission in the hippocampus.

A computational view of short-term plasticity and its implications for E-I balance

Short-term plasticity (STP) and excitatory-inhibitory balance (EI balance) are both ubiquitous building blocks of brain circuits across the animal kingdom. The synapses involved in EI are also subject to short-term plasticity, and several experimental studies have shown that their effects overlap. Recent computational and theoretical work has begun to highlight the functional implications of the intersection of these motifs. The findings are nuanced: while there are general computational themes, such as pattern tuning, normalization, and gating, much of the richness of these interactions comes from region- and modality specific tuning of STP properties. Together these findings point towards the STP-EI balance combination as being a versatile and highly efficient neural building block for a wide range of pattern-specific responses.

Combinatorial gene therapy for epilepsy: Gene sequence positioning and AAV serotype influence expression and inhibitory effect on seizures

Gene therapy with AAV vectors carrying genes for neuropeptide Y and its receptor Y2 has been shown to inhibit seizures in multiple animal models of epilepsy. It is however unknown how the AAV serotype or the sequence order of these two transgenes in the expression cassette affects the actual parenchymal gene expression levels and the seizure-suppressant efficacy. To address these questions, we compared three viral vector serotypes (AAV1, AAV2 and AAV8) and two transgene sequence orders (NPY-IRES-Y2 and Y2-IRES-NPY) in a rat model of acutely induced seizures. Wistar male rats were injected bilaterally with viral vectors and 3 weeks later acute seizures were induced by a subcutaneous injection of kainate. The latency until 1st motor seizure, time spent in motor seizure and latency to status epilepticus were measured to evaluate the seizure-suppressing efficacy of these vectors compared to an empty cassette control vector. Based on the results, the effect of the AAV1-NPY-IRES-Y2 vector was further investigated by in vitro electrophysiology, and its ability to achieve transgene overexpression in resected human hippocampal tissue was evaluated. The AAV1-NPY-IRES-Y2 proved to be better to any other serotype or gene sequence considering both transgene expression and ability to suppress induced seizures in rats. The vector also demonstrated transgene-induced decrease of glutamate release from excitatory neuron terminals and significantly increased both NPY and Y2 expression in resected human hippocampal tissue from patients with drug-resistant temporal lobe epilepsy. These results validate the feasibility of NPY/Y2 receptor gene therapy as a therapeutic opportunity in focal epilepsies.

Memory rescue and learning in synaptic impaired neuronal circuits

Neuronal impairment is a characteristic of Alzheimer's disease (AD), but its effect on neural activity dynamics underlying memory deficits is unclear. Here, we studied the effects of synaptic impairment on neural activities associated with memory recall, memory rescue, and learning a new memory, in an integrate-and-fire neuronal network. Our results showed that reducing connectivity decreases the neuronal synchronization of memory neurons and impairs memory recall performance. Although, slow-gamma stimulation rescued memory recall and slow-gamma oscillations, the rescue caused a side effect of activating mixed memories. During the learning of a new memory, reducing connectivity caused impairment in storing the new memory, but did not affect previously stored memories. We also explored the effects of other types of impairments including neuronal loss and excitation-inhibition imbalance and the rescue by general increase of excitability. Our results reveal potential computational mechanisms underlying the memory deficits caused by impairment in AD.

Mechanistic insights into cAMP-mediated presynaptic potentiation at hippocampal mossy fiber synapses

Presynaptic plasticity is an activity-dependent change in the neurotransmitter release and plays a key role in dynamic modulation of synaptic strength. Particularly, presynaptic potentiation mediated by cyclic adenosine monophosphate (cAMP) is widely seen across the animals and thought to contribute to learning and memory. Hippocampal mossy fiber-CA3 pyramidal cell synapses have been used as a model because of robust presynaptic potentiation in short- and long-term forms. Moreover, direct presynaptic recordings from large mossy fiber terminals allow one to dissect the potentiation mechanisms. Recently, super-resolution microscopy and flash-and-freeze electron microscopy have revealed the localizations of release site molecules and synaptic vesicles during the potentiation at a nanoscale, identifying the molecular mechanisms of the potentiation. Incorporating these growing knowledges, we try to present plausible mechanisms underlying the cAMP-mediated presynaptic potentiation.

Emotional brain network decoded by biological spiking neural network

Introduction

Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown.

Methods

To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions.

Results

The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the α frequency band could effectively represent negative emotions, while the α frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation.

Discussion

The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.

Stimulation along the anterior-posterior axis of lateral frontal cortex reduces visual serial dependence

Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.

Implementation of Highly Stable Memristive Characteristics in an Organic-Inorganic Hybrid Resistive Switching Layer of Chitosan-Titanium Oxide with Microwave-Assisted Oxidation

This study proposes a high-performance organic-inorganic hybrid memristor for the development of neuromorphic devices in the memristor-based artificial synapse. The memristor consists of a solid polymer electrolyte (SPE) chitosan layer and a titanium oxide (TiO_x) layer grown with a low-thermal-budget, microwave-assisted oxidation. The fabricated Ti/SPE-chitosan/TiO_x/Pt-structured memristor exhibited steady bipolar resistive switching (BRS) characteristics and demonstrated excellent endurance in 100-cycle repetition tests. Compared to SPE-chitosan memristors without a TiO_x layer, the proposed organic-inorganic hybrid memristor demonstrated a higher dynamic range and a higher response to pre-synaptic stimuli such as short-term plasticity via paired-pulse facilitation. The effect of adding the TiO_x layer on the BRS properties was examined, and the results showed that the TiO_x layer improved the chemical and electrical superiority of the proposed memristor synaptic device. The proposed SPE-chitosan organic-inorganic hybrid memristor also exhibited a stable spike-timing-dependent plasticity, which closely mimics long-term plasticity. The potentiation and depression behaviors that modulate synaptic weights operated stably via repeated spike cycle tests. Therefore, the proposed SPE-chitosan organic-inorganic hybrid memristor is a promising candidate for the development of neuromorphic devices in memristor-based artificial synapses owing to its excellent stability, high dynamic range, and superior response to pre-synaptic stimuli.

A Library of Polymer-based Microelectrode Array Designs for Recording from the Brain of Different Animal Models

Large-scale network recording technology is critical in linking neural activity to behavior. Stable, long-term recordings collected from behaving animals are the foundation for understanding neural dynamics and the plasticity of neural circuits. Penetrating microelectrode arrays (MEAs) can obtain high-resolution neural activity from different brain regions. However, ensuring the longevity of implantable devices and the consistency of neural signals over time remains one big challenge. A potential solution is to use flexible, polymer-based MEAs to minimize the foreign body response and prolong the lifetime of neural interfacing devices. Rodents and nonhuman primates (NHP) are commonly used animal models in neuroscience and neuroengineering studies. Specially designed MEAs that capture morphological features of different animal brains and various brain structures are powerful tools to simultaneously obtain neural activities from multiple brain regions. In this work, we develop a set of prototype designs of polymer MEAs that cover cortical, sub-cortical, and multiple brain regions of rodents and NHP.

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Drug reinforcement impairs cognitive flexibility by inhibiting striatal cholinergic neurons

Addictive substance use impairs cognitive flexibility, with unclear underlying mechanisms. The reinforcement of substance use is mediated by the striatal direct-pathway medium spiny neurons (dMSNs) that project to the substantia nigra pars reticulata (SNr). Cognitive flexibility is mediated by striatal cholinergic interneurons (CINs), which receive extensive striatal inhibition. Here, we hypothesized that increased dMSN activity induced by substance use inhibits CINs, reducing cognitive flexibility. We found that cocaine administration in rodents caused long-lasting potentiation of local inhibitory dMSN-to-CIN transmission and decreased CIN firing in the dorsomedial striatum (DMS), a brain region critical for cognitive flexibility. Moreover, chemogenetic and time-locked optogenetic inhibition of DMS CINs suppressed flexibility of goal-directed behavior in instrumental reversal learning tasks. Notably, rabies-mediated tracing and physiological studies showed that SNr-projecting dMSNs, which mediate reinforcement, sent axonal collaterals to inhibit DMS CINs, which mediate flexibility. Our findings demonstrate that the local inhibitory dMSN-to-CIN circuit mediates the reinforcement-induced deficits in cognitive flexibility.

Microbiome and tryptophan metabolomics analysis in adolescent depression: roles of the gut microbiota in the regulation of tryptophan-derived neurotransmitters and behaviors in human and mice

BACKGROUND

Adolescent depression is becoming one of the major public health concerns, because of its increased prevalence and risk of significant functional impairment and suicidality. Clinical depression commonly emerges in adolescence; therefore, the prevention and intervention of depression at this stage is crucial. Recent evidence supports the importance of the gut microbiota (GM) in the modulation of multiple functions associated with depression through the gut-brain axis (GBA). However, the underlying mechanisms remain poorly understood. Therefore, in the current study, we aimed to screen the microbiota out from healthy and depressive adolescents, delineate the association of the targeted microbiota and the adolescent depression, address the salutary effects of the targeted microbiota on anti-depressive behaviors in mice involving the metabolism of the tryptophan (Trp)-derived neurotransmitters along the GBA.

RESULTS

Here, we found the gut microbiota from healthy adolescent volunteers, first diagnosis patients of adolescent depression, and sertraline interveners after first diagnosis displayed significant difference, the relative abundance of Faecalibacterium, Roseburia, Collinsella, Blautia, Phascolarctobacterium, Lachnospiraceae-unclassified decreased in adolescent depressive patients, while restored after sertraline treatment. Of note, the Roseburia abundance exhibited a high efficiency in predicting adolescent depression. Intriguingly, transplantation of the fecal microbiota from healthy adolescent volunteers to the chronic restraint stress (CRS)-induced adolescent depressed mice significantly ameliorated mouse depressive behaviors, in which the Roseburia exerted critical roles, since its effective colonization in the mouse colon resulted in remarkably increased 5-HT level and reciprocally decreased kynurenine (Kyn) toxic metabolites quinolinic acid (Quin) and 3-hydroxykynurenine (3-HK) levels in both the mouse brain and colon. The specific roles of the Roseburia were further validated by the target bacteria transplantation mouse model, Roseburia intestinalis (Ri.) was gavaged to mice and importantly, it dramatically ameliorated CRS-induced mouse depressive behaviors, increased 5-HT levels in the brain and colon via promoting tryptophan hydroxylase-2 (TPH2) or -1 (TPH1) expression. Reciprocally, Ri. markedly restrained the limit-step enzyme responsible for kynurenine (indoleamine2,3-dioxygenase 1, IDO1) and quinolinic acid (3-hydroxyanthranilic acid 3,4-dioxygenase, 3HAO) generation, thereby decreased Kyn and Quin levels. Additionally, Ri. administration exerted a pivotal role in the protection of CRS-induced synaptic loss, microglial activation, and astrocyte maintenance.

CONCLUSIONS

This study is the first to delineate the beneficial effects of Ri. on adolescent depression by balancing Trp-derived neurotransmitter metabolism and improving synaptogenesis and glial maintenance, which may yield novel insights into the microbial markers and therapeutic strategies of GBA in adolescent depression. Video Abstract.

From correlation to communication: Disentangling hidden factors from functional connectivity changes

While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, "communication change," utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed "communication change" is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.

Organic multilevel (opto)electronic memories towards neuromorphic applications

In the past decades, neuromorphic computing has attracted the interest of the scientific community due to its potential to circumvent the von Neumann bottleneck. Organic materials, owing to their fine tunablility and their ability to be used in multilevel memories, represent a promising class of materials to fabricate neuromorphic devices with the key requirement of operation with synaptic weight. In this review, recent studies of organic multilevel memory are presented. The operating principles and the latest achievements obtained with devices exploiting the main approaches to reach multilevel operation are discussed, with emphasis on organic devices using floating gates, ferroelectric materials, polymer electrets and photochromic molecules. The latest results obtained using organic multilevel memories for neuromorphic circuits are explored and the major advantages and drawbacks of the use of organic materials for neuromorphic applications are discussed.