Spike timing-dependent plasticity: from synapse to perception

Determining the effects of transcranial alternating current stimulation on corticomotor excitability and motor performance: A sham-controlled comparison of four frequencies

Transcranial alternating current stimulation (tACS) modulates brain oscillations and corticomotor plasticity. We examined the effects of four tACS frequencies (20 Hz, 40 Hz, 60 Hz, and 80 Hz) on motor cortex (M1) excitability and motor performance. In a randomised crossover design, 12 adults received 20-minute tACS sessions, with Sham as control. Corticomotor and intracortical excitability was measured up to 60-minutes post-tACS. Motor performance was evaluated using the Grooved Pegboard Test (GPT) and sensorimotor assessments. Our findings demonstrated frequency-dependent modulation of corticomotor excitability based on MEP amplitude. 20 Hz and 40 Hz tACS reduced MEPs, while 60 Hz and 80 Hz increased MEPs. Inhibition (cortical silent period, SP) was reduced across all tACS frequencies compared to Sham, with 20 Hz and 40 Hz showing consistent reductions, 60 Hz showing effects at post-0 and post-30, and 80 Hz at post-60. Furthermore, 60 Hz tACS decreased intracortical inhibition at post-0, while intracortical facilitation increased with 20 Hz and 60 Hz at post-0, and 40 Hz at post-60. Motor performance remained unaffected across frequencies. Regression analyses revealed that shorter SP at 60 min post 60 Hz tACS predicted faster reaction times, while greater MEP amplitudes at 60 min following 80 Hz tACS predicted improved hand dexterity. Overall, beta and gamma tACS frequencies modulate M1 excitability, with consistent effects on SP, suggesting potential use in conditions involving SP elongation, such as stroke and Huntington's disease. These findings highlight 60 Hz tACS as a potential tool for motor rehabilitation therapies.

Exploring cerebellar transcranial magnetic stimulation in post-stroke limb dysfunction rehabilitation: a narrative review

This review delves into the emerging field of cerebellar Transcranial Magnetic Stimulation (TMS) in the rehabilitation of limb dysfunction following a stroke. It synthesizes findings from randomized controlled trials and case studies, examining the efficacy, safety, and underlying mechanisms of cerebellar TMS. The review outlines advancements in TMS technologies, such as low-frequency repetitive TMS, intermittent Theta Burst Stimulation, and Cerebello-Motor Paired Associative Stimulation, and their integration with physiotherapy. The role of the cerebellum in motor control, the theoretical underpinnings of cerebellar stimulation on motor cortex excitability, and the indirect effects on cognition and motor learning are explored. Additionally, the review discusses current challenges, including coil types, safety, and optimal timing and modes of stimulation, and suggests future research directions. This comprehensive analysis highlights cerebellar TMS as a promising, though complex, approach in stroke rehabilitation, offering insights for its clinical optimization.

Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing

Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant challenges that impede their commercialization. These challenges include high variability due to their stochastic nature. Microfabricated electrochemical synapses offer a promising approach by functioning as an analog programmable resistor based on deterministic ion-insertion mechanisms. Here, an all-solid-state oxide-ion synaptic transistor is developed, employing Bi2V0.9Cu0.1O5.35 as a superior oxide-ion conductor electrolyte and La0.5Sr0.5FeO3-δ as a variable-resistance channel able to efficiently operate at temperatures compatible with conventional electronics. This transistor exhibits essential synaptic behaviors such as long- and short-term potentiation, paired-pulse facilitation, and post-tetanic potentiation, mimicking fundamental properties of biological neural networks. Key criteria for efficient neuromorphic computing are satisfied, including excellent linear and symmetric synaptic plasticity, low energy consumption per programming pulse, and high endurance with minimal cycle-to-cycle variation. Integrated into an artificial neural network (ANN) simulation for handwritten digit recognition, the presented synaptic transistor achieved a 96% accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, illustrating the effective implementation of the device in ANNs. These findings demonstrate the potential of oxide-ion based synaptic transistors for effective implementation in analog neuromorphic computing based on iontronics.

CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies

Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computational capacity and lower power requirements than sigmoidal neural networks. This article introduces a new type of SNN that draws inspiration and incorporates concepts from neuronal assemblies in the human brain. The proposed network, termed as class-dependent neuronal activation-based SNN (CDNA-SNN), assigns each neuron learnable values known as CDNAs which indicate the neuron's average relative spiking activity in response to samples from different classes. A new learning algorithm that categorizes the neurons into different class assemblies based on their CDNAs is also presented. These neuronal assemblies are trained via a novel training method based on spike-timing-dependent plasticity (STDP) to have high activity for their associated class and low firing rate for other classes. Also, using CDNAs, a new type of STDP that controls the amount of plasticity based on the assemblies of pre- and postsynaptic neurons is proposed. The performance of CDNA-SNN is evaluated on five datasets from the University of California, Irvine (UCI) machine learning repository, as well as Modified National Institute of Standards and Technology (MNIST) and Fashion MNIST, using nested cross-validation (N-CV) for hyperparameter optimization. Our results show that CDNA-SNN significantly outperforms synaptic weight association training (SWAT) ( ) and SpikeProp ( ) on 3/5 and self-regulating evolving spiking neural (SRESN) ( ) on 2/5 UCI datasets while using the significantly lower number of trainable parameters. Furthermore, compared to other supervised, fully connected SNNs, the proposed SNN reaches the best performance for Fashion MNIST and comparable performance for MNIST and neuromorphic-MNIST (N-MNIST), also utilizing much less (1%-35%) parameters.

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit of advancing neuromorphic hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection and processing of massive, dynamic data sets require an innovative computational paradigm akin to that of the human brain. Spiking neural networks (SNNs), representing the third generation of ANNs, are emerging as a promising solution for real-time spatiotemporal information processing due to their event-based spatiotemporal capabilities. The ideal hardware supporting SNN operations comprises artificial neurons, artificial synapses, and their integrated arrays. Currently, the structural complexity of SNNs and spike-based methodologies requires hardware components with biomimetic behaviors that are distinct from those of conventional memristors used in deep neural networks. These distinctive characteristics required for neuron and synapses devices pose significant challenges. Developing effective building blocks for SNNs, therefore, necessitates leveraging the intrinsic properties of the materials constituting each unit and overcoming the integration barriers. This review focuses on the progress toward memristor-based spiking neural network neuromorphic hardware, emphasizing the role of individual components such as memristor-based neurons, synapses, and array integration along with relevant biological insights. We aim to provide valuable perspectives to researchers working on the next generation of brain-like computing systems based on these foundational elements.

Developmental Spike Timing-Dependent Long-Term Depression Requires Astrocyte d-Serine at L2/3-L2/3 Synapses of the Mouse Somatosensory Cortex

Spike timing-dependent plasticity (STDP) is a learning rule important for synaptic refinement and for learning and memory during development. While different forms of presynaptic t-LTD have been deeply investigated, little is known about the mechanisms of somatosensory cortex postsynaptic t-LTD. In the present work, we investigated the requirements and mechanisms for induction of developmental spike timing-dependent long-term depression (t-LTD) at L2/3-L2/3 synapses in the juvenile mouse somatosensory cortex. We found that postnatal day (P) 13-21 mice of either sex show t-LTD at L2/3-L2/3 synapses induced by pairing single presynaptic activity with single postsynaptic action potentials at low stimulation frequency (0.2 Hz) that is expressed postsynaptically and requires the activation of ionotropic postsynaptic NMDA-type glutamate receptors containing GluN2B subunits. In addition, it requires postsynaptic Ca2+, Ca2+ release from internal stores, calcineurin, postsynaptic endocannabinoid synthesis, activation of CB1 receptors, and astrocytic signaling to release the gliotransmitter d-serine to activate postsynaptic NMDARs to induce t-LTD. These results show direct evidence of the mechanism involved in developmental postsynaptic t-LTD at L2/3-L2/3 synapses, revealing a central role of astrocytes and their release of d-serine in its induction.

Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey

With the adoption of smart systems, artificial neural networks (ANNs) have become ubiquitous. Conventional ANN implementations have high energy consumption, limiting their use in embedded and mobile applications. Spiking neural networks (SNNs) mimic the dynamics of biological neural networks by distributing information over time through binary spikes. Neuromorphic hardware has emerged to leverage the characteristics of SNNs, such as asynchronous processing and high activation sparsity. Therefore, SNNs have recently gained interest in the machine learning community as a brain-inspired alternative to ANNs for low-power applications. However, the discrete representation of the information makes the training of SNNs by backpropagation-based techniques challenging. In this survey, we review training strategies for deep SNNs targeting deep learning applications such as image processing. We start with methods based on the conversion from an ANN to an SNN and compare these with backpropagation-based techniques. We propose a new taxonomy of spiking backpropagation algorithms into three categories, namely, spatial, spatiotemporal, and single-spike approaches. In addition, we analyze different strategies to improve accuracy, latency, and sparsity, such as regularization methods, training hybridization, and tuning of the parameters specific to the SNN neuron model. We highlight the impact of input encoding, network architecture, and training strategy on the accuracy-latency tradeoff. Finally, in light of the remaining challenges for accurate and efficient SNN solutions, we emphasize the importance of joint hardware-software codevelopment.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

Synaptopodin is required for long-term depression at Schaffer collateral-CA1 synapses

Synaptopodin (SP), an actin-associated protein found in telencephalic neurons, affects activity-dependant synaptic plasticity and dynamic changes of dendritic spines. While being required for long-term depression (LTD) mediated by metabotropic glutamate receptor (mGluR-LTD), little is known about its role in other forms of LTD induced by low frequency stimulation (LFS-LTD) or spike-timing dependent plasticity (STDP). Using electrophysiology in ex vivo hippocampal slices from SP-deficient mice (SPKO), we show that absence of SP is associated with a deficit of LTD at Sc-CA1 synapses induced by LFS-LTD and STDP. As LTD is known to require AMPA- receptors internalization and IP3-receptors calcium signaling, we tested by western blotting and immunochemistry if there were changes in their expression which we found to be reduced. While we were not able to induce LTD, long-term potentiation (LTP), albeit diminished in SPKO, can be recovered by using a stronger stimulation protocol. In SPKO we found no differences in NMDAR, which are the primary site of calcium signalling to induce LTP. Our study shows, for the first time, the key role of the requirement of SP to allow induction of activity-dependant LTD at Sc-CA1 synapses.

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.

Enhanced Release Probability without Changes in Synaptic Delay during Analogue-Digital Facilitation

Neuronal timing with millisecond precision is critical for many brain functions such as sensory perception, learning and memory formation. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). For instance, paired-pulse facilitation or presynaptic long-term potentiation are associated with reductions in the synaptic delay, whereas paired-pulse depression or presynaptic long-term depression are associated with an increased synaptic delay. Parallelly, the AP broadening that results from the inactivation of voltage gated potassium (Kv) channels responsible for the repolarization phase of the AP delays the synaptic response, and the inactivation of sodium (Nav) channels by voltage reduces the synaptic latency. However, whether synaptic delay is modulated during depolarization-induced analogue-digital facilitation (d-ADF), a form of context-dependent synaptic facilitation induced by prolonged depolarization of the presynaptic neuron and mediated by the voltage-inactivation of presynaptic Kv1 channels, remains unclear. We show here that despite Pr being elevated during d-ADF at pyramidal L5-L5 cell synapses, the synaptic delay is surprisingly unchanged. This finding suggests that both Pr- and AP-dependent changes in synaptic delay compensate for each other during d-ADF. We conclude that, in contrast to other short- or long-term modulations of presynaptic release, synaptic timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Prefrontal projections modulate recurrent circuitry in the insular cortex to support short-term memory

Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons' functional connectivity and memory-coding ability to support STM.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Serotonergic Modulation of the Excitation/Inhibition Balance in the Visual Cortex

Serotonergic neurons constitute one of the main systems of neuromodulators, whose diffuse projections regulate the functions of the cerebral cortex. Serotonin (5-HT) is known to play a crucial role in the differential modulation of cortical activity related to behavioral contexts. Some features of the 5-HT signaling organization suggest its possible participation as a modulator of activity-dependent synaptic changes during the critical period of the primary visual cortex (V1). Cells of the serotonergic system are among the first neurons to differentiate and operate. During postnatal development, ramifications from raphe nuclei become massively distributed in the visual cortical area, remarkably increasing the availability of 5-HT for the regulation of excitatory and inhibitory synaptic activity. A substantial amount of evidence has demonstrated that synaptic plasticity at pyramidal neurons of the superficial layers of V1 critically depends on a fine regulation of the balance between excitation and inhibition (E/I). 5-HT could therefore play an important role in controlling this balance, providing the appropriate excitability conditions that favor synaptic modifications. In order to explore this possibility, the present work used in vitro intracellular electrophysiological recording techniques to study the effects of 5-HT on the E/I balance of V1 layer 2/3 neurons, during the critical period. Serotonergic action on the E/I balance has been analyzed on spontaneous activity, evoked synaptic responses, and long-term depression (LTD). Our results pointed out that the predominant action of 5-HT implies a reduction in the E/I balance. 5-HT promoted LTD at excitatory synapses while blocking it at inhibitory synaptic sites, thus shifting the Hebbian alterations of synaptic strength towards lower levels of E/I balance.

Determining the corticospinal, intracortical and motor function responses to transcranial alternating current stimulation of the motor cortex in healthy adults: A systematic review and meta-analysis

BACKGROUND

Transcranial Alternating Current Stimulation (tACS) employs low-intensity sinusoidal currents to influence cortical plasticity and motor function. Despite extensive research, inconsistent results require a comprehensive review of tACS efficacy.

OBJECTIVE

This study systematically assesses tACS effects on corticospinal and intracortical excitability, and motor function over the motor cortex (M1), focusing on alpha, beta, and gamma frequencies.

METHODS

Relevant studies were identified through database searches and citations were tracked until July 10, 2023. The methodological quality of the included studies (29) was evaluated by Downs and Black. Data synthesis involved meta-analysis (n = 25) and best evidence synthesis (n = 5).

RESULTS

Meta-analysis revealed that alpha and beta tACS with intensities > 1 mA and tACS with individualized alpha frequency (IAF) increased corticospinal excitability (CSE). tACS over M1 improved motor function, irrespective of stimulation frequency and intensity. Sub-analysis showed that alpha and beta tACS with an intensity ≤ 1 mA led to improved motor function, while gamma tACS at 2 mA enhanced motor function. Additionally, beta tACS at a fixed frequency of 20 Hz, as well as both low gamma (30-55) and high gamma (55-80) tACS, resulted in improved motor function. A stimulation duration of 20 min led to improvements in both CSE and motor function, and tACS with electrode sizes smaller than 35 cm2 and an electrode montage over M1-supraorbital region (SOR) were found to enhance motor function. Notably, both online and offline tACS improved motor function, regardless of stimulation factors.

CONCLUSION

tACS modulates CSE and improves motor function, with outcomes dependent on stimulation parameters and timing.

PPy/SWCNTs-Modified Microelectrode Array for Learning and Memory Model Construction through Electrical Stimulation and Detection of In Vitro Hippocampal Neuronal Network

The learning and memory functions of the brain remain unclear, which are in urgent need for the detection of both a single cell signal with high spatiotemporal resolution and network activities with high throughput. Here, an in vitro microelectrode array (MEA) was fabricated and further modified with polypyrrole/carboxylated single-walled carbon nanotubes (PPy/SWCNTs) nanocomposites as the interface between biological and electronic systems. The deposition of the nanocomposites significantly improved the performance of microelectrodes including low impedance (60.3 ± 28.8 k Ω), small phase delay (-32.8 ± 4.4°), and good biocompatibility. Then the modified MEA was used to apply learning training and test on hippocampal neuronal network cultured for 21 days through electrical stimulation, and multichannel electrophysiological signals were recorded simultaneously. During the process of learning training, the stimulus/response ratio of the hippocampal learning population gradually increased and the response time gradually decreased. After training, the mean spikes in burst, number of bursts, and mean burst duration increased by 53%, 191%, and 52%, respectively, and the correlation of neurons in the network was significantly enhanced from 0.45 ± 0.002 to 0.78 ± 0.002. In addition, the neuronal network basically retained these characteristics for at least 5 h. These results indicated that we have successfully constructed a learning and memory model of hippocampal neurons on the in vitro MEA, contributing to understanding learning and memory based on synaptic plasticity. The proposed PPy/SWCNTs-modified in vitro MEA will provide a promising platform for the exploration of learning and memory mechanism and their applications in vitro.

Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule

Intelligent behavior depends on the brain's ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

Effects of cortico-cortical paired associative stimulation based on multisensory integration to brain network connectivity in stroke patients: study protocol for a randomized doubled blind clinical trial

INTRODUCTION

Brain has a spontaneous recovery after stroke, reflecting the plasticity of the brain. Currently, TMS is used for studies of single-target brain region modulation, which lacks consideration of brain networks and functional connectivity. Cortico-cortical paired associative stimulation (ccPAS) promotes recovery of motor function. Multisensory effects in primary visual cortex(V1) directly influence behavior and perception, which facilitate motor functional recovery in stroke patients. Therefore, in this study, dual-targeted precise stimulation of V1 and primary motor cortex(M1) on the affected hemisphere of stroke patients will be used for cortical visuomotor multisensory integration to improve motor function.

METHOD

This study is a randomized, double-blind controlled clinical trial over a 14-week period. 69 stroke subjects will be enrolled and divided into sham stimulation group, ccPAS low frequency group, and ccPAS high frequency group. All groups will receive conventional rehabilitation. The intervention lasted for two weeks, five times a week. Assessments will be performed before the intervention, at the end of the intervention, and followed up at 6 and 14 weeks. The primary assessment indicator is the 'Fugl-Meyer Assessment of the Upper Extremity ', secondary outcomes were 'The line bisection test', 'Modified Taylor Complex Figure', 'NIHSS' and neuroimaging assessments. All adverse events will be recorded.

DISCUSSION

Currently, ccPAS is used for the modulation of neural circuits. Based on spike-timing dependent plasticity theory, we can precisely intervene in the connections between different cortices to promote the recovery of functional connectivity on damaged brain networks after stroke. We hope to achieve the modulation of cortical visuomotor interaction by combining ccPAS with the concept of multisensory integration. We will further analyze the correlation between analyzing visual and motor circuits and explore the alteration of neuroplasticity by the interactions between different brain networks. This study will provide us with a new clinical treatment strategy to achieve precise rehabilitation for patient with motor dysfunction after stroke.

TRIAL REGISTRATION

This trial was registered in the Chinese Clinical Trial Registry with code ChiCTR2300067422 and was approved on January 16, 2023.

Origin of the efficiency of spike timing-based neural computation for processing temporal information

Although the advantage of spike timing-based over rate-based network computation has been recognized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information.

Paired associative stimulation on the soleus H-Reflex using motor point and peripheral nerve stimulation

Paired associative stimulation (PAS) has been shown to modulate the corticospinal excitability via spike timing dependent plasticity (STDP). In this study, we aimed to suppress the spinal H-Reflex using PAS. We paired two stimulation modalities, i.e., peripheral nerve stimulation (PNS) and motor point stimulation (MPS). We used PNS to dominantly activate the Ia sensory axon, and we used MPS to dominantly activate the α-motoneuron cell body antidromically. Thus, we applied both PNS and MPS such that the α-motoneuron cell body was activated 5 ms before the activation of the Ia sensory axon ending at the Ia-α motoneuron synapse. If the spinal reflexes can be modulated by STDP, and a combination of MPS and PNS is timed appropriately, then the H-Reflex amplitude will decrease while no change in H-Reflex amplitude is expected for MPS or PNS only. To test this hypothesis, six young healthy participants (5M/1F: 26.8 ± 4.1 yrs) received one of the three following conditions on days separated by at least 24 hr: 1) PAS, 2) MPS only or 3) PNS only. The H-Reflex and M-wave recruitment curves of the soleus were measured immediately prior to, immediately after, 30 min and 60 min after the intervention. The normalized H-Reflex amplitudes were then compared across conditions and times using a two-way ANOVA (3 conditions × 4 times). No main effects of condition or time, or interaction effect were found. These results suggest that relying solely on STDP may be insufficient to inhibit the soleus H-Reflex.

Synaptic Transistors Based on PVA: Chitosan Biopolymer Blended Electric-Double-Layer with High Ionic Conductivity

This study proposed a biocompatible polymeric organic material-based synaptic transistor gated with a biopolymer electrolyte. A polyvinyl alcohol (PVA):chitosan (CS) biopolymer blended electrolyte with high ionic conductivity was used as an electrical double layer (EDL). It served as a gate insulator with a key function as an artificial synaptic transistor. The frequency-dependent capacitance characteristics of PVA:CS-based biopolymer EDL were evaluated using an EDL capacitor (Al/PVA: CS blended electrolyte-based EDL/Pt configuration). Consequently, the PVA:CS blended electrolyte behaved as an EDL owing to high capacitance (1.53 µF/cm²) at 100 Hz and internal mobile protonic ions. Electronic synaptic transistors fabricated using the PVA:CS blended electrolyte-based EDL membrane demonstrated basic artificial synaptic behaviors such as excitatory post-synaptic current modulation, paired-pulse facilitation, and dynamic signal-filtering functions by pre-synaptic spikes. In addition, the spike-timing-dependent plasticity was evaluated using synaptic spikes. The synaptic weight modulation was stable during repetitive spike cycles for potentiation and depression. Pattern recognition was conducted through a learning simulation for artificial neural networks (ANNs) using Modified National Institute of Standards and Technology datasheets to examine the neuromorphic computing system capability (high recognition rate of 92%). Therefore, the proposed synaptic transistor is suitable for ANNs and shows potential for biological and eco-friendly neuromorphic systems.

Neural ensembles in navigation: From single cells to population codes

The brain can represent behaviorally relevant information through the firing of individual neurons as well as the coordinated firing of ensembles of neurons. Neurons in the hippocampus and associated cortical regions participate in a variety of types of ensembles to support navigation. These ensemble types include single cell codes, population codes, time-compressed sequences, behavioral sequences, and engrams. We present the physiological basis and behavioral relevance of ensemble firing. We discuss how these traditional definitions of ensembles can constrain or expand potential analyses due to the underlying assumptions and abstractions made. We highlight how coding can change at the ensemble level while underlying single cell codes remain intact. Finally, we present how ensemble definitions could be broadened to better understand the full complexity of the brain.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.

Sleep-mediated regulation of reward circuits: implications in substance use disorders

Our modern society suffers from both pervasive sleep loss and substance abuse-what may be the indications for sleep on substance use disorders (SUDs), and could sleep contribute to the individual variations in SUDs? Decades of research in sleep as well as in motivated behaviors have laid the foundation for us to begin to answer these questions. This review is intended to critically summarize the circuit, cellular, and molecular mechanisms by which sleep influences reward function, and to reveal critical challenges for future studies. The review also suggests that improving sleep quality may serve as complementary therapeutics for treating SUDs, and that formulating sleep metrics may be useful for predicting individual susceptibility to SUDs and other reward-associated psychiatric diseases.

Ketamine and Zinc: Treatment of Anorexia Nervosa Via Dual NMDA Receptor Modulation

Anorexia nervosa is a disorder associated with serious adverse health outcomes, for which there is currently considerable treatment ineffectiveness. Characterised by restrictive eating behaviours, distorted body image perceptions and excessive physical activity, there is growing recognition anorexia nervosa is associated with underlying dysfunction in excitatory and inhibitory neurometabolite metabolism and signalling. This narrative review critically explores the role of N-methyl-D-aspartate receptor-mediated excitatory and inhibitory neurometabolite dysfunction in anorexia nervosa and its associated biomarkers. The existing magnetic resonance spectroscopy literature in anorexia nervosa is reviewed and we outline the brain region-specific neurometabolite changes that have been reported and their connection to anorexia nervosa psychopathology. Considering the proposed role of dysfunctional neurotransmission in anorexia nervosa, the potential utility of zinc supplementation and sub-anaesthetic doses of ketamine in normalising this is discussed with reference to previous research in anorexia nervosa and other neuropsychiatric conditions. The rationale for future research to investigate the combined use of low-dose ketamine and zinc supplementation to potentially extend the therapeutic benefits in anorexia nervosa is subsequently explored and promising biological markers for assessing and potentially predicting treatment response are outlined.

Subcortical glutamatergic inputs exhibit a Hebbian form of long-term potentiation in the dentate gyrus

The hippocampus receives glutamatergic and GABAergic inputs from subcortical regions. Despite the important roles of these subcortical inputs in the regulation of hippocampal circuit, it has not been explored whether associative activation of the subcorticohippocampal pathway induces Hebbian plasticity of subcortical inputs. Here, we demonstrate that the hypothalamic supramammillary nucleus (SuM) to the dentate granule cell (GC) synapses, which co-release glutamate and GABA, undergo associative long-term potentiation (LTP) of glutamatergic, but not GABAergic, co-transmission. This LTP is induced by pairing of SuM inputs with GC spikes. We found that this Hebbian LTP is input-specific, requires NMDA receptors and CaMKII activation, and is expressed postsynaptically. By the net increase in excitatory drive of SuM inputs following LTP induction, associative inputs of SuM and the perforant path effectively discharge GCs. Our results highlight the important role of associative plasticity at SuM-GC synapses in the regulation of dentate gyrus activity and for the encoding of SuM-related information.

Impaired synaptic plasticity in an animal model of autism exhibiting early hippocampal GABAergic-BDNF/TrkB signaling alterations

In Neurodevelopmental Disorders, alterations of synaptic plasticity may trigger structural changes in neuronal circuits involved in cognitive functions. This hypothesis was tested in mice carrying the human R451C mutation of Nlgn3 gene (NLG3^R451C KI), found in some families with autistic children. To this aim, the spike time dependent plasticity (STDP) protocol was applied to immature GABAergic Mossy Fibers (MF)-CA3 connections in hippocampal slices from NLG3^R451C KI mice. These animals failed to exhibit STD-LTP, an effect that persisted in adulthood when these synapses became glutamatergic. Similar results were obtained in mice lacking the Nlgn3 gene (NLG3 KO mice), suggesting a loss of function. The loss of STD-LTP was associated with a premature shift of GABA from the depolarizing to the hyperpolarizing direction, a reduced BDNF availability and TrkB phosphorylation at potentiated synapses. These effects may constitute a general mechanism underlying cognitive deficits in those forms of Autism caused by synaptic dysfunctions.

Aftereffects of Frontoparietal Theta tACS on Verbal Working Memory: Behavioral and Neurophysiological Analysis

Verbal working memory is supported by a left-lateralized frontoparietal theta oscillatory (4–8 Hz) network. We tested whether stimulating the left frontoparietal network at theta frequency during verbal working memory can produce observable after-stimulation effects in behavior and neurophysiology. Weak theta-band alternating electric currents were delivered via two 4 × 1 HD electrode arrays centered at F3 and P3. Three stimulation configurations, including in-phase, anti-phase, or sham, were tested on three different days in a cross-over (within-subject) design. On each test day, the subject underwent three experimental sessions: pre-, during- and post-stimulation sessions. In all sessions, the subject performed a Sternberg verbal working memory task with three levels of memory load (load 2, 4 and 6), imposing three levels of cognitive demand. Analyzing behavioral and EEG data from the post-stimulation session, we report two main observations. First, in-phase stimulation improved task performance in subjects with higher working memory capacity (WMC) under higher memory load (load 6). Second, in-phase stimulation enhanced frontoparietal theta synchrony during working memory retention in subjects with higher WMC under higher memory loads (load 4 and load 6), and the enhanced frontoparietal theta synchronization is mainly driven by enhanced frontal→parietal theta Granger causality. These observations suggest that (1) in-phase theta transcranial alternating current stimulation (tACS) during verbal working memory can result in observable behavioral and neurophysiological consequences post stimulation, (2) the short-term plasticity effects are state- and individual-dependent, and (3) enhanced executive control underlies improved behavioral performance.

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Frontoparietal network was stimulated at theta frequency (4 - 8Hz) during verbal working memory and aftereffeccts analyzed

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In-phase frontoparietal theta stimulation improved working memory performance in participants with higher working memory capacity

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Enhanced behavioral performance was accompanied by enhanced frontoparietal theta synchrony

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Enhanced frontoparietal theta synchronization was driven by enhanced frontal→parietal theta Granger causality

Transcranial Magnetic Stimulation Following a Paired Associative Stimulation Protocol Based on a Video Game Neuromodulates Cortical Excitability and Motor Behavior

Transcranial Magnetic Stimulation (TMS) can be used to modulate cortico-spinal excitability following a paired associative stimulation (PAS) protocol. Movement-related cortical stimulation (MRCS) is a PAS protocol based on the synchronization of a single-pulse TMS with a movement task. However, plasticity and motor performance potentiation due to MRCS has been related exclusively to single-movement tasks. In order to unveil the effects of an MRCS protocol in complex movements, we applied PAS synchronized with a movement-related dynamic task (MRDT) with a customized video game. In 22 healthy subjects, we measured the reaction time (RT), trajectory error (TE), and the number of collected and avoided items when playing the custom video game to evaluate the task motor performance. Moreover, we assessed the recruitment curve of Motor Evoked Potentials (MEPs) with five different intensities to evaluate the motor corticospinal excitability. MEPs were recorded in Abductor Pollicis Brevis (APB) and Abductor Digiti Minimi (ADM), before, right after, and 30 min after the PAS intervention, in an active versus sham experimental design. The MRCS PAS intervention resulted in RT reduction, and motor corticospinal excitability was modulated, reflected as significant MEP amplitude change at 110% RMT intensity in ADM and at 130% RMT intensity in APB. RTs and ADM MEP amplitudes correlated positively in specific time and intensity assessments. We conclude that the proposed PAS protocol facilitated RT performance in a complex task. This phenomenon might be useful to develop neurorehabilitation strategies with complex movements, similar to activities of daily living.

Brain and spinal cord paired stimulation coupled with locomotor training facilitates motor output in human spinal cord injury

Combined interventions for neuromodulation leading to neurorecovery have gained great attention by researchers to resemble clinical rehabilitation approaches. In this randomized clinical trial, we established changes in the net output of motoneurons innervating multiple leg muscles during stepping when transcranial magnetic stimulation (TMS) of the primary motor cortex was paired with transcutaneous spinal (transspinal) stimulation over the thoracolumbar region during locomotor training. TMS was delivered before (TMS-transspinal) or after (transspinal-TMS) transspinal stimulation during the stance phase of the less impaired leg. Ten individuals with chronic incomplete or complete SCI received at least 20 sessions of training. Each session consisted of 240 paired stimuli delivered over 10-min blocks for 1 h during robotic assisted step training on a motorized treadmill. Body weight support, leg guidance force and treadmill speed were adjusted based on each subject's ability to step without knee buckling or toe dragging. Most transspinal evoked potentials (TEPs) recorded before and after each intervention from ankle and knee muscles during assisted stepping were modulated in a phase-dependent pattern. Transspinal-TMS and locomotor training affected motor neuron output of knee and ankle muscles with ankle TEPs to be modulated in a phase-dependent manner. TMS-transspinal and locomotor training increased motor neuron output for knee but not for ankle muscles. Our results support that targeted brain and spinal cord stimulation alters responsiveness of neurons over multiple spinal segments in people with chronic SCI. Noninvasive stimulation of the brain and spinal cord along with locomotor training is a novel neuromodulation method that can become a promising modality for rehabilitation in humans after SCI.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

A surrogate gradient spiking baseline for speech command recognition

Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence (AI); they typically use real valued neuron responses. By contrast, biological neurons are known to operate using spike trains. In principle, spiking neural networks (SNNs) may have a greater representational capability than ANNs, especially for time series such as speech; however their adoption has been held back by both a lack of stable training algorithms and a lack of compatible baselines. We begin with a fairly thorough review of literature around the conjunction of ANNs and SNNs. Focusing on surrogate gradient approaches, we proceed to define a simple but relevant evaluation based on recent speech command tasks. After evaluating a representative selection of architectures, we show that a combination of adaptation, recurrence and surrogate gradients can yield light spiking architectures that are not only able to compete with ANN solutions, but also retain a high degree of compatibility with them in modern deep learning frameworks. We conclude tangibly that SNNs are appropriate for future research in AI, in particular for speech processing applications, and more speculatively that they may also assist in inference about biological function.

Anxiety and hippocampal neuronal activity: Relationship and potential mechanisms

The hippocampus has been implicated in modulating anxiety. It interacts with a variety of brain regions, both cortical and subcortical areas regulating emotion and stress responses, including prefrontal cortex, amygdala, hypothalamus, and the nucleus accumbens, to adjust anxiety levels in response to a variety of stressful conditions. Growing evidence indicates that anxiety is associated with increased neuronal excitability in the hippocampus, and alterations in local regulation of hippocampal excitability have been suggested to underlie behavioral disruptions characteristic of certain anxiety disorders. Furthermore, studies have shown that some anxiolytics can treat anxiety by altering the excitability and plasticity of hippocampal neurons. Hence, identifying cellular and molecular mechanisms and neural circuits that regulate hippocampal excitability in anxiety may be beneficial for developing targeted interventions for treatment of anxiety disorders particularly for the treatment-resistant cases. We first briefly review a role of the hippocampus in fear. We then review the evidence indicating a relationship between the hippocampal activity and fear/anxiety and discuss some possible mechanisms underlying stress-induced hippocampal excitability and anxiety-related behavior.

Brain and spinal cord paired stimulation coupled with locomotor training affects polysynaptic flexion reflex circuits in human spinal cord injury

Neurorecovery from locomotor training is well established in human spinal cord injury (SCI). However, neurorecovery resulting from combined interventions has not been widely studied. In this randomized clinical trial, we established the tibialis anterior (TA) flexion reflex modulation pattern when transcranial magnetic stimulation (TMS) of the primary motor cortex was paired with transcutaneous spinal cord (transspinal) stimulation over the thoracolumbar region during assisted step training. Single pulses of TMS were delivered either before (TMS-transspinal) or after (transspinal-TMS) transspinal stimulation during the stance phase of the less impaired leg. Eight individuals with chronic incomplete or complete SCI received at least 20 sessions of paired stimulation during assisted step training. Each session consisted of 240 paired stimuli delivered over 10-min blocks for 1 h during robotic-assisted step training with the Lokomat6 Pro^®. Body weight support, leg guidance force and treadmill speed were adjusted based on each participant's ability to step without knee buckling or toe dragging. Both the early and late TA flexion reflex remained unaltered after TMS-transspinal and locomotor training. In contrast, the early and late TA flexion reflexes were significantly depressed during stepping after transspinal-TMS and locomotor training. Reflex changes occurred at similar slopes and intercepts before and after training. Our findings support that targeted brain and spinal cord stimulation coupled with locomotor training reorganizes the function of flexion reflex pathways, which are a part of locomotor networks, in humans with varying levels of sensorimotor function after SCI.Trial registration number NCT04624607; Registered on November 12, 2020.

Efficient dendritic learning as an alternative to synaptic plasticity hypothesis

Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning. The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location. We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification. It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron. Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates. However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power. Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built.

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.

New treatment for amblyopia based on rules of synaptic plasticity: a randomized clinical trial

Amblyopia resulting from early deprivation of vision or defocus in one eye reflects an imbalance of input from the eyes to the visual cortex. We tested the hypothesis that asynchronous stimulation of the two eyes might induce synaptic plasticity and rebalance input. Experiments on normal adults showed that repetitive brief exposure of grating stimuli, with the onset of each stimulus delayed by 8.3 ms in one eye, results in a shift in perceptual eye dominance. Clinical studies (Clinical trial registration number: ChiCTR2100049130), using popular 3D movies with similar asynchrony between the two eyes (amblyopic eye stimulated first) to treat anisometropic amblyopia, established that just 10.5 h of conditioning over <3 weeks produced improvement that met criteria for successful treatment. The benefits of asynchronous conditioning accumulate over 20-30 45 min sessions, and are maintained for at least 2 years. Finally, we demonstrate that asynchronous binocular treatment alone is more effective than patching only. This novel treatment is popular with children and is some 50 times more efficient than patching alone.

Entrainment and Spike-Timing Dependent Plasticity – A Review of Proposed Mechanisms of Transcranial Alternating Current Stimulation

Specific frequency bands of neural oscillations have been correlated with a range of cognitive and behavioral effects (e.g., memory and attention). The causal role of specific frequencies may be investigated using transcranial alternating current stimulation (tACS), a non-invasive brain stimulation method. TACS involves applying a sinusoidal current between two or more electrodes attached on the scalp, above neural regions that are implicated in cognitive processes of interest. The theorized mechanisms by which tACS affects neural oscillations have implications for the exact stimulation frequency used, as well as its anticipated effects. This review outlines two main mechanisms that are thought to underlie tACS effects – entrainment, and spike-timing dependent plasticity (STDP). Entrainment suggests that the stimulated frequency synchronizes the ongoing neural oscillations, and is thought to be most effective when the stimulated frequency is at or close to the endogenous frequency of the targeted neural network. STDP suggests that stimulation leads to synaptic changes based on the timing of neuronal firing in the target neural network. According to the principles of STDP, synaptic strength is thought to increase when pre-synaptic events occur prior to post-synaptic events (referred to as long-term potentiation, LTP). Conversely, when post-synaptic events occur prior to pre-synaptic events, synapses are thought to be weakened (referred to as long-term depression, LTD). In this review, we summarize the theoretical frameworks and critically review the tACS evidence for each hypothesis. We also discuss whether each mechanism alone can account for tACS effects or whether a combined account is necessary.

Transcranial magnetic stimulation as a tool to induce and explore plasticity in humans

Activity-dependent synaptic plasticity is the main theoretical framework to explain mechanisms of learning and memory. Synaptic plasticity can be explored experimentally in animals through various standardized protocols for eliciting long-term potentiation and long-term depression in hippocampal and cortical slices. In humans, several non-invasive protocols of repetitive transcranial magnetic stimulation and transcranial direct current stimulation have been designed and applied to probe synaptic plasticity in the primary motor cortex, as reflected by long-term changes in motor evoked potential amplitudes. These protocols mimic those normally used in animal studies for assessing long-term potentiation and long-term depression. In this chapter, we first discuss the physiologic basis of theta-burst stimulation, paired associative stimulation, and transcranial direct current stimulation. We describe the current biophysical and theoretical models underlying the molecular mechanisms of synaptic plasticity and metaplasticity, defined as activity-dependent changes in neural functions that modulate subsequent synaptic plasticity such as long-term potentiation (LTP) and long-term depression (LTD), in the human motor cortex including calcium-dependent plasticity, spike-timing-dependent plasticity, the role of N-methyl-d-aspartate-related transmission and gamma-aminobutyric-acid interneuronal activity. We also review the putative microcircuits responsible for synaptic plasticity in the human motor cortex. We critically readdress the issue of variability in studies investigating synaptic plasticity and propose available solutions. Finally, we speculate about the utility of future studies with more advanced experimental approaches.

Disparate insults relevant to schizophrenia converge on impaired spike synchrony and weaker synaptic interactions in prefrontal local circuits

Schizophrenia results from hundreds of known causes, including genetic, environmental, and developmental insults that cooperatively increase risk of developing the disease. In spite of the diversity of causal factors, schizophrenia presents with a core set of symptoms and brain abnormalities (both structural and functional) that particularly impact the prefrontal cortex. This suggests that many different causal factors leading to schizophrenia may cause prefrontal neurons and circuits to fail in fundamentally similar ways. The nature of convergent malfunctions in prefrontal circuits at the cell and synaptic levels leading to schizophrenia are not known. Here, we apply convergence-guided search to identify core pathological changes in the functional properties of prefrontal circuits that lie downstream of mechanistically distinct insults relevant to the disease. We compare the impacts of blocking NMDA receptors in monkeys and deleting a schizophrenia risk gene in mice on activity timing and effective communication in prefrontal local circuits. Although these manipulations operate through distinct molecular pathways and biological mechanisms, we found they produced convergent pathophysiological effects on prefrontal local circuits. Both manipulations reduced the frequency of synchronous (0-lag) spiking between prefrontal neurons and weakened functional interactions between prefrontal neurons at monosynaptic lags as measured by information transfer between the neurons. The two observations may be related, as reduction in synchronous spiking between prefrontal neurons would be expected to weaken synaptic connections between them via spike-timing-dependent synaptic plasticity. These data suggest that the link between spike timing and synaptic connectivity could comprise the functional vulnerability that multiple risk factors exploit to produce disease.

Maturation of Corticolimbic Functional Connectivity During Sensitive Periods of Brain Development

The maturation of key corticolimbic structures and the prefrontal cortex during sensitive periods of brain development from early life through adolescence is crucial for the acquisition of a variety of cognitive and affective processes associated with adult behavior. In this chapter, we first review how key cellular and circuit level changes during adolescence dictate the development of the prefrontal cortex and its capacity to integrate contextual and emotional information from the ventral hippocampus and the amygdala. We further discuss how afferent transmission from ventral hippocampal and amygdala inputs displays unique age-dependent trajectories that directly impact prefrontal functional maturation through adolescence. We conclude by proposing that time-sensitive strengthening of specific corticolimbic synapses is a critical contributing factor for the protracted maturation of cognitive and emotional regulation by the prefrontal cortex.

Sex-specific accelerated decay in time/activity-dependent plasticity and associative memory in an animal model of Alzheimer's disease

Clinical studies have shown that female brains are more predisposed to neurodegenerative diseases such as Alzheimer's disease (AD), but the cellular and molecular mechanisms behind this disparity remain unknown. In several mouse models of AD, synaptic plasticity dysfunction is an early event and appears before significant accumulation of amyloid plaques and neuronal degeneration. However, it is unclear whether sexual dimorphism at the synaptic level contributes to the higher risk and prevalence of AD in females. Our studies on APP/PS1 (APPSwe/PS1dE9) mouse model show that AD impacts hippocampal long‐term plasticity in a sex‐specific manner. Long‐term potentiation (LTP) induced by strong tetanic stimulation (STET), theta burst stimulation (TBS) and population spike timing‐dependent plasticity (pSTDP) show a faster decay in AD females compared with age‐matched AD males. In addition, behavioural tagging (BT), a model of associative memory, is specifically impaired in AD females with a faster decay in memory compared with males. Together with the plasticity and behavioural data, we also observed an upregulation of neuroinflammatory markers, along with downregulation of transcripts that regulate cellular processes associated with synaptic plasticity and memory in females. Immunohistochemistry of AD brains confirms that female APP/PS1 mice carry a higher amyloid plaque burden and have enhanced microglial activation compared with male APP/PS1 mice. Their presence in the diseased mice also suggests a link between the impairment of LTP and the upregulation of the inflammatory response. Overall, our data show that synaptic plasticity and associative memory impairments are more prominent in females and this might account for the faster progression of AD in females.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia

BACKGROUND

Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain.

METHODS

The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery.

RESULTS

To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up.

SIGNIFICANCE

The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years.

CONCLUSION

The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.

Adiabatic dynamic causal modelling

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

Neuromorphic Devices for Bionic Sensing and Perception

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