Spike timing-dependent plasticity: from synapse to perception

Spine dynamics in the brain, mental disorders and artificial neural networks

In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or 'extrinsic' dynamics) and dynamics independent of activity ('intrinsic' dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.

A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

Plasticity in the Hippocampus, Neurogenesis and Drugs of Abuse

Synaptic plasticity in the hippocampus assists with consolidation and storage of long-lasting memories. Decades of research has provided substantial information on the cellular and molecular mechanisms underlying synaptic plasticity in the hippocampus, and this review discusses these mechanisms in brief. Addiction is a chronic relapsing disorder with loss of control over drug taking and drug seeking that is caused by long-lasting memories of drug experience. Relapse to drug use is caused by exposure to context and cues associated with the drug experience, and is a major clinical problem that contributes to the persistence of addiction. This review also briefly discusses some evidence that drugs of abuse alter plasticity in the hippocampus, and that development of novel treatment strategies that reverse or prevent drug-induced synaptic alterations in the hippocampus may reduce relapse behaviors associated with addiction.

Contribution of TMS and TMS-EEG to the Understanding of Mechanisms Underlying Physiological Brain Aging

In the human brain, aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission. However, there is increasing evidence to support the notion that the aged brain has a remarkable ability to reorganize itself, with the aim of preserving its physiological activity. It is important to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated with many neurological diseases. Transcranial magnetic stimulation (TMS), coupled with electromyography or electroencephalography (EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated with behavioral manipulation. In this review, we aimed to provide up to date information about the role of TMS and TMS-EEG in the investigation of brain aging. In particular, we focused on data about cortical excitability, connectivity and plasticity, obtained by using readouts such as motor evoked potentials and transcranial evoked potentials. Overall, findings in the literature support an important potential contribution of TMS to the understanding of the mechanisms underlying normal brain aging. Further studies are needed to expand the current body of information and to assess the applicability of TMS findings in the clinical setting.

Energy sensors in drug addiction: A potential therapeutic target

Addiction is defined as the repeated exposure and compulsive seek of psychotropic drugs that, despite the harmful effects, generate relapse after the abstinence period. The psychophysiological processes associated with drug addiction (acquisition/expression, withdrawal, and relapse) imply important alterations in neurotransmission and changes in presynaptic and postsynaptic plasticity and cellular structure (neuroadaptations) in neurons of the reward circuits (dopaminergic neuronal activity) and other corticolimbic regions. These neuroadaptation mechanisms imply important changes in neuronal energy balance and protein synthesis machinery. Scientific literature links drug-induced stimulation of dopaminergic and glutamatergic pathways along with presence of neurotrophic factors with alterations in synaptic plasticity and membrane excitability driven by metabolic sensors. Here, we provide current knowledge of the role of molecular targets that constitute true metabolic/energy sensors such as AMPK, mTOR, ERK, or K_ATP in the development of the different phases of addiction standing out the main brain regions (ventral tegmental area, nucleus accumbens, hippocampus, and amygdala) constituting the hubs in the development of addiction. Because the available treatments show very limited effectiveness, evaluating the drug efficacy of AMPK and mTOR specific modulators opens up the possibility of testing novel pharmacotherapies for an individualized approach in drug abuse.

Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP

Understanding and producing embedded sequences according to supra-regular grammars in language has always been considered a high-level cognitive function of human beings, named "syntax barrier" between humans and animals. However, some neurologists recently showed that macaques could be trained to produce embedded sequences involving supra-regular grammars through a well-designed experiment paradigm. Via comparing macaques and preschool children's experimental results, they claimed that human uniqueness might only lie in the speed and learning strategy resulting from the chunking mechanism. Inspired by their research, we proposed a Brain-inspired Sequence Production Spiking Neural Network (SP-SNN) to model the same production process, followed by memory and learning mechanisms of the multi-brain region cooperation. After experimental verification, we demonstrated that SP-SNN could also handle embedded sequence production tasks, striding over the "syntax barrier." SP-SNN used Population-Coding and STDP mechanism to realize working memory, Reward-Modulated STDP mechanism for acquiring supra-regular grammars. Therefore, SP-SNN needs to simultaneously coordinate short-term plasticity (STP) and long-term plasticity (LTP) mechanisms. Besides, we found that the chunking mechanism indeed makes a difference in improving our model's robustness. As far as we know, our work is the first one toward the "syntax barrier" in the SNN field, providing the computational foundation for further study of related underlying animals' neural mechanisms in the future.

Learning poly-synaptic paths with traveling waves

Traveling waves are commonly observed across the brain. While previous studies have suggested the role of traveling waves in learning, the mechanism remains unclear. We adopted a computational approach to investigate the effect of traveling waves on synaptic plasticity. Our results indicate that traveling waves facilitate the learning of poly-synaptic network paths when combined with a reward-dependent local synaptic plasticity rule. We also demonstrate that traveling waves expedite finding the shortest paths and learning nonlinear input/output mapping, such as exclusive or (XOR) function.

There are approximately 10¹¹ neurons with 10¹⁴ connections in the human brain. Information transmission among neurons in this large network is considered crucial for our behavior. To achieve this, multiple synaptic connections along a poly-synaptic network path must be adjusted coherently during learning. Because the previously proposed reward-dependent synaptic plasticity rule requires coactivation of presynaptic and postsynaptic neurons, learning can fail if a subset of neurons along a distant network path is inactive at the beginning of learning. We suggest that traveling waves that are initiated at an information source can mitigate this problem. We performed computer simulations of spiking neural networks with reward-dependent local synaptic plasticity rules and traveling waves. Our results show that this combination facilitates the learning and refinement of synaptic network paths. We argue that these features are a general biological strategy for maintaining and optimizing our brain function. Our research provides new insights into how complex neural networks in the brain form during learning and memory consolidation.

Cross-regional phase amplitude coupling supports the encoding of episodic memories

Phase amplitude coupling (PAC) between theta and gamma oscillations represents a key neurophysiological mechanism that promotes the temporal organization of oscillatory activity. For this reason, PAC has been implicated in item/context integration for episodic processes, including coordinating activity across multiple cortical regions. While data in humans has focused principally on PAC within a single brain region, data in rodents has revealed evidence that the phase of the hippocampal theta oscillation modulates gamma oscillations in the cortex (and vice versa). This pattern, termed cross-regional PAC (xPAC), has not previously been observed in human subjects engaged in mnemonic processing. We use a unique dataset with intracranial electrodes inserted simultaneously into the hippocampus and seven cortical regions across 40 human subjects to (1) test for the presence of significant cross-regional PAC (xPAC), (2) to establish that the magnitude of xPAC predicts memory encoding success, (3) to describe specific frequencies within the broad 2-9 Hz theta range that govern hippocampal-cortical interactions in xPAC, and (4) compare anterior versus posterior hippocampal xPAC patterns. We find that strong functional xPAC occurs principally between the hippocampus and other mesial temporal structures, namely entorhinal and parahippocampal cortices, and that xPAC is overall stronger for posterior hippocampal connections. We also show that our results are not confounded by alternative factors such as inter-regional phase synchrony, local PAC occurring within cortical regions, or artifactual theta oscillatory waveforms.

Parametric study of transcranial alternating current stimulation for brain alpha power modulation

Transcranial alternating current stimulation, a non-invasive brain stimulation technique, has been used to increase alpha (8-12 Hz) power, the latter being associated with various brain functions and states. Heterogeneity among stimulation parameters across studies makes it difficult to implement reliable transcranial alternating current stimulation protocols, explaining the absence of consensus on optimal stimulation parameters to modulate the alpha rhythm. This project documents the differential impact of controlling for key transcranial alternating current stimulation parameters, namely the intensity, the frequency and the stimulation site (anterior versus posterior). Phase 1:20 healthy participants underwent 4 different stimulation conditions. In each experimental condition, stimulation via 2 electrodes was delivered for 20 min. Stimulation conditions were administered at PO7-PO8 or F3-F4 at individual's alpha frequency, or at individual's theta frequency or sham. Stimulation intensity was set according to each participant's comfort following a standardized unpleasantness scale (≤ 40 out of 100) and could not exceed 6 mA. All conditions were counterbalanced. Phase 2: participants who tolerated higher intensity of stimulation (4-6 mA) underwent alpha-frequency stimulation applied over PO7-PO8 at 1 mA to investigate within-subject modulation of stimulation response according to stimulation intensity. Whether set over posterior or anterior cortical sites, alpha-frequency stimulation showed greater increase in alpha power relative to stimulation at theta frequency and sham stimulation. Posterior alpha-frequency stimulation showed a greater increase in alpha power relative to the adjacent frequency bands over frontal and occipito-parietal brain areas. Low intensity (1 mA) posterior alpha stimulation showed a similar increase in alpha power than at high (4-6 mA) intensity when measured immediately after stimulation. However, when tested at 60 min or 120 min, low intensity stimulation was associated with significantly superior alpha power increase relative to high intensity stimulation. This study shows that posterior individual's alpha frequency stimulation at higher intensities is well tolerated but fails to increase stimulation aftereffects recorded within 2 h of stimulation on brain oscillations of the corresponding frequency band. In sharp contrast, stimulating at 1 mA (regardless of phosphene generation or sensory perception) effectively and selectively modulates alpha power within that 2-h time window, thus validating that it as a reliable stimulus intensity for future studies. This study also shows that posterior alpha-frequency stimulation preferentially modulates endogenous brain oscillations of the corresponding frequency band. Moreover, our data suggest that posterior alpha-frequency transcranial alternating current stimulation is a reliable and precise non-invasive brain stimulation technique for persistent modulation of both frontal and occipito-parietal alpha power.

A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns

This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.

Neuro-Transistor Based on UV-Treated Charge Trapping in MoTe2 for Artificial Synaptic Features

The diversity of brain functions depend on the release of neurotransmitters in chemical synapses. The back gated three terminal field effect transistors (FETs) are auspicious candidates for the emulation of biological functions to recognize the proficient neuromorphic computing systems. In order to encourage the hysteresis loops, we treated the bottom side of MoTe₂ flake with deep ultraviolet light in ambient conditions. Here, we modulate the short-term and long-term memory effects due to the trapping and de-trapping of electron events in few layers of a MoTe₂ transistor. However, MoTe₂ FETs are investigated to reveal the time constants of electron trapping/de-trapping while applying the gate-voltage pulses. Our devices exploit the hysteresis effect in the transfer curves of MoTe₂ FETs to explore the excitatory/inhibitory post-synaptic currents (EPSC/IPSC), long-term potentiation (LTP), long-term depression (LTD), spike timing/amplitude-dependent plasticity (STDP/SADP), and paired pulse facilitation (PPF). Further, the time constants for potentiation and depression is found to be 0.6 and 0.9 s, respectively which seems plausible for biological synapses. In addition, the change of synaptic weight in MoTe₂ conductance is found to be 41% at negative gate pulse and 38% for positive gate pulse, respectively. Our findings can provide an essential role in the advancement of smart neuromorphic electronics.

Autonomous Programming for General Purposes: Theory

The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.

Low-intensity repetitive paired associative stimulation targeting the motor hand area at theta frequency causes a lasting reduction in corticospinal excitability

OBJECTIVE

Sub-motor threshold 5 Hz repetitive paired associative stimulation (5 Hz-rPAS_25ms) produces a long-lasting increase in corticospinal excitability. Assuming a spike-timing dependent plasticity-like (STDP-like) mechanism, we hypothesized that 5 Hz-rPAS at a shorter inter-stimulus interval (ISI) of 15 ms (5 Hz-rPAS_15ms) would exert a lasting inhibitory effect on corticospinal excitability.

METHODS

20 healthy volunteers received two minutes of 5 Hz-rPAS_15ms. Transcranial magnetic stimulation (TMS) was applied over the motor hotspot of the right abductor pollicis brevis muscle at 90% active motor threshold. Sub-motor threshold peripheral electrical stimulation was given to the left median nerve 15 ms before each TMS pulse. We assessed changes in mean amplitude of the unconditioned motor evoked potential (MEP), short-latency intracortical inhibition (SICI), intracortical facilitation (ICF), short-latency afferent inhibition (SAI), long-latency afferent inhibition (LAI), and cortical silent period (CSP) before and for 60 minutes after 5-Hz rPAS_15ms.

RESULTS

Subthreshold 5-Hz rPAS_15ms produced a 20-40% decrease in mean MEP amplitude along with an attenuation in SAI, lasting at least 60 minutes. A follow-up experiment revealed that MEP facilitation was spatially restricted to the target muscle.

CONCLUSIONS

Subthreshold 5-Hz rPAS_15ms effectively suppresses corticospinal excitability. Together with the facilitatory effects of subthreshold 5-Hz rPAS_25ms (Quartarone et al., J Physiol 2006;575:657-670), the results show that sub-motor threshold 5-Hz rPAS induces STDP-like bidirectional plasticity in the motor cortex.

SIGNIFICANCE

The results of the present study provide a new short-time paradigm of long term depression (LTD) induction in human sensory-motor cortex.

Muscarinic Regulation of Spike Timing Dependent Synaptic Plasticity in the Hippocampus

Long-term changes in synaptic transmission between neurons in the brain are considered the cellular basis of learning and memory. Over the last few decades, many studies have revealed that the precise order and timing of activity between pre- and post-synaptic cells ("spike-timing-dependent plasticity; STDP") is crucial for the sign and magnitude of long-term changes at many central synapses. Acetylcholine (ACh) via the recruitment of diverse muscarinic receptors is known to influence STDP in a variety of ways, enabling flexibility and adaptability in brain network activity during complex behaviors. In this review, we will summarize and discuss different mechanistic aspects of muscarinic modulation of timing-dependent plasticity at both excitatory and inhibitory synapses in the hippocampus to shape learning and memory.

Targeting Gamma-Related Pathophysiology in Autism Spectrum Disorder Using Transcranial Electrical Stimulation: Opportunities and Challenges

A range of scalp electroencephalogram (EEG) abnormalities correlates with the core symptoms of autism spectrum disorder (ASD). Among these are alterations of brain oscillations in the gamma-frequency EEG band in adults and children with ASD, whose origin has been linked to dysfunctions of inhibitory interneuron signaling. While therapeutic interventions aimed to modulate gamma oscillations are being tested for neuropsychiatric disorders such as schizophrenia, Alzheimer's disease, and frontotemporal dementia, the prospects for therapeutic gamma modulation in ASD have not been extensively studied. Accordingly, we discuss gamma-related alterations in the setting of ASD pathophysiology, as well as potential interventions that can enhance gamma oscillations in patients with ASD. Ultimately, we argue that transcranial electrical stimulation modalities capable of entraining gamma oscillations, and thereby potentially modulating inhibitory interneuron circuitry, are promising methods to study and mitigate gamma alterations in ASD. Autism Res 2020, 13: 1051-1071. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain functions are mediated by various oscillatory waves of neuronal activity, ranging in amplitude and frequency. In certain neuropsychiatric disorders, such as schizophrenia and Alzheimer's disease, reduced high-frequency oscillations in the "gamma" band have been observed, and therapeutic interventions to enhance such activity are being explored. Here, we review and comment on evidence of reduced gamma activity in ASD, arguing that modalities used in other disorders may benefit individuals with ASD as well.

Cognitive Control Errors in Nonhuman Primates Resembling Those in Schizophrenia Reflect Opposing Effects of NMDA Receptor Blockade on Causal Interactions Between Cells and Circuits in Prefrontal and Parietal Cortices

BACKGROUND

The causal biology underlying schizophrenia is not well understood, but it is likely to involve a malfunction in how neurons adjust synaptic connections in response to patterns of activity in networks. We examined statistical dependencies between neural signals at the cell, local circuit, and distributed network levels in prefrontal and parietal cortices of monkeys performing a variant of the AX continuous performance task paradigm. We then quantified changes in the pattern of neural interactions across levels of scale following NMDA receptor (NMDAR) blockade and related these changes to a pattern of cognitive control errors closely matching the performance of patients with schizophrenia.

METHODS

We recorded the spiking activity of 1762 neurons along with local field potentials at multiple electrode sites in prefrontal and parietal cortices concurrently, and we generated binary time series indicating the presence or absence of spikes in single neurons or local field potential power above or below a threshold. We then applied causal discovery analysis to the time series to detect statistical dependencies between the signals (causal interactions) and compared the pattern of these interactions before and after NMDAR blockade.

RESULTS

Global blockade of NMDAR produced distinctive and frequently opposite changes in neural interactions at the cell, local circuit, and network levels in prefrontal and parietal cortices. Cognitive control errors were associated with decreased interactions at the cell level and with opposite changes at the network level in prefrontal and parietal cortices.

CONCLUSIONS

NMDAR synaptic deficits change causal interactions between neural signals at different levels of scale that correlate with schizophrenia-like deficits in cognitive control.

Neuronal Plasticity: Neuronal Organization is Associated with Neurological Disorders

Stimuli from stressful events, attention in the classroom, and many other experiences affect the functionality of the brain by changing the structure or reorganizing the connections between neurons and their communication. Modification of the synaptic transmission is a vital mechanism for generating neural activity via internal or external stimuli. Neuronal plasticity is an important driving force in neuroscience research, as it is the basic process underlying learning and memory and is involved in many other functions including brain development and homeostasis, sensorial training, and recovery from brain injury. Indeed, neuronal plasticity has been explored in numerous studies, but it is still not clear how neuronal plasticity affects the physiology and morphology of the brain. Thus, unraveling the molecular mechanisms of neuronal plasticity is essential for understanding the operation of brain functions. In this timeline review, we discuss the molecular mechanisms underlying different forms of synaptic plasticity and their association with neurodegenerative/neurological disorders as a consequence of alterations in neuronal plasticity.

The etiological contribution of GABAergic plasticity to the pathogenesis of neuropathic pain

Neuropathic pain developing after peripheral or central nerve injury is the result of pathological changes generated through complex mechanisms. Disruption in the homeostasis of excitatory and inhibitory neurons within the central nervous system is a crucial factor in the formation of hyperalgesia or allodynia occurring with neuropathic pain. The central GABAergic pathway has received attention for its extensive distribution and function in neural circuits, including the generation and development of neuropathic pain. GABAergic inhibitory changes that occur in the interneurons along descending modulatory and nociceptive pathways in the central nervous system are believed to generate neuronal plasticity, such as synaptic plasticity or functional plasticity of the related genes or proteins, that is the foundation of persistent neuropathic pain. The primary GABAergic plasticity observed in neuropathic pain includes GABAergic synapse homo- and heterosynaptic plasticity, decreased synthesis of GABA, down-expression of glutamic acid decarboxylase and GABA transporter, abnormal expression of NKCC1 or KCC2, and disturbed function of GABA receptors. In this review, we describe possible mechanisms associated with GABAergic plasticity, such as central sensitization and GABAergic interneuron apoptosis, and the epigenetic etiologies of GABAergic plasticity in neuropathic pain. Moreover, we summarize potential therapeutic targets of GABAergic plasticity that may allow for successful relief of hyperalgesia from nerve injury. Finally, we compare the effects of the GABAergic system in neuropathic pain to other types of chronic pain to understand the contribution of GABAergic plasticity to neuropathic pain.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Effect of interpopulation spike-timing-dependent plasticity on synchronized rhythms in neuronal networks with inhibitory and excitatory populations

We consider a two-population network consisting of both inhibitory (I) interneurons and excitatory (E) pyramidal cells. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation spike-timing-dependent plasticity (STDP). In previous works without STDPs, fast sparsely synchronized rhythms, related to diverse cognitive functions, were found to appear in a range of noise intensity D for static synaptic strengths. Here, by varying D, we investigate the effect of interpopulation STDPs on fast sparsely synchronized rhythms that emerge in both the I- and the E-populations. Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur. Then, the degree of fast sparse synchronization varies due to effects of LTP and LTD. In a broad region of intermediate D, the degree of good synchronization (with higher synchronization degree) becomes decreased, while in a region of large D, the degree of bad synchronization (with lower synchronization degree) gets increased. Consequently, in each I- or E-population, the synchronization degree becomes nearly the same in a wide range of D (including both the intermediate and the large D regions). This kind of "equalization effect" is found to occur via cooperative interplay between the average occupation and pacing degrees of spikes (i.e., the average fraction of firing neurons and the average degree of phase coherence between spikes in each synchronized stripe of spikes in the raster plot of spikes) in fast sparsely synchronized rhythms. Finally, emergences of LTP and LTD of interpopulation synaptic strengths (leading to occurrence of equalization effect) are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.

Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

Induction of activity synchronization among primed hippocampal neurons out of random dynamics is key for trace memory formation and retrieval

Memory is thought to be encoded by sparsely distributed neuronal ensembles in memory‐related regions. However, it is unclear how memory‐eligible neurons react during learning to encode trace fear memory and how they retrieve a memory. We implemented a fiber‐optic confocal fluorescence endomicroscope to directly visualize calcium dynamics of hippocampal CA1 neurons in freely behaving mice subjected to trace fear conditioning. Here we report that the overall activity levels of CA1 neurons showed a right‐skewed lognormal distribution, with a small portion of highly active neurons (termed Primed Neurons) filling the long‐tail. Repetitive training induced Primed Neurons to shift from random activity to well‐tuned synchronization. The emergence of activity synchronization coincided with the appearance of mouse freezing behaviors. In recall, a partial synchronization among the same subset of Primed Neurons was induced from random dynamics, which also coincided with mouse freezing behaviors. Additionally, training‐induced synchronization facilitated robust calcium entry into Primed Neurons. In contrast, most CA1 neurons did not respond to tone and foot shock throughout the training and recall cycles. In conclusion, Primed Neurons are preferably recruited to encode trace fear memory and induction of activity synchronization among Primed Neurons out of random dynamics is critical for trace memory formation and retrieval.

On the Modulatory Roles of Neuregulins/ErbB Signaling on Synaptic Plasticity

Neuregulins (NRGs) are a family of epidermal growth factor-related proteins, acting on tyrosine kinase receptors of the ErbB family. NRGs play an essential role in the development of the nervous system, since they orchestrate vital functions such as cell differentiation, axonal growth, myelination, and synapse formation. They are also crucially involved in the functioning of adult brain, by directly modulating neuronal excitability, neurotransmission, and synaptic plasticity. Here, we provide a review of the literature documenting the roles of NRGs/ErbB signaling in the modulation of synaptic plasticity, focusing on evidence reported in the hippocampus and midbrain dopamine (DA) nuclei. The emerging picture shows multifaceted roles of NRGs/ErbB receptors, which critically modulate different forms of synaptic plasticity (LTP, LTD, and depotentiation) affecting glutamatergic, GABAergic, and DAergic synapses, by various mechanisms. Further, we discuss the relevance of NRGs/ErbB-dependent synaptic plasticity in the control of brain processes, like learning and memory and the known involvement of NRGs/ErbB signaling in the modulation of synaptic plasticity in brain’s pathological conditions. Current evidence points to a central role of NRGs/ErbB receptors in controlling glutamatergic LTP/LTD and GABAergic LTD at hippocampal CA3–CA1 synapses, as well as glutamatergic LTD in midbrain DA neurons, thus supporting that NRGs/ErbB signaling is essential for proper brain functions, cognitive processes, and complex behaviors. This suggests that dysregulated NRGs/ErbB-dependent synaptic plasticity might contribute to mechanisms underlying different neurological and psychiatric disorders.

Single-cell transcriptomic evidence for dense intracortical neuropeptide networks

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here, we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

Relation between gamma oscillations and neuronal plasticity in the visual cortex

Learning is thought to be mediated by activity-dependent modification of neuronal interactions. To avoid maladaptive modifications of synaptic transmission by spurious activity, synaptic plasticity has to be gated. In the case of supervised learning, these gating functions are accomplished by reinforcement through value-assigning systems. Here we show that the dynamic state of local circuits correlates with the occurrence of activity-dependent long-term changes in neuronal response properties. We find that repeated visual stimuli induce long-term changes of orientation preference of neuronal populations in visual cortex if stimuli induce synchronized population responses oscillating at ɣ-frequencies. This suggests that neuronal plasticity is controlled by a hierarchy of gating systems and assigns critical gating functions to resonance properties of local circuits.

Use-dependent long-term changes of neuronal response properties must be gated to prevent irrelevant activity from inducing inappropriate modifications. Here we test the hypothesis that local network dynamics contribute to such gating. As synaptic modifications depend on temporal contiguity between presynaptic and postsynaptic activity, we examined the effect of synchronized gamma (ɣ) oscillations on stimulation-dependent modifications of orientation selectivity in adult cat visual cortex. Changes of orientation maps were induced by pairing visual stimulation with electrical activation of the mesencephalic reticular formation. Changes in orientation selectivity were assessed with optical recording of intrinsic signals and multiunit recordings. When conditioning stimuli were associated with strong ɣ-oscillations, orientation domains matching the orientation of the conditioning grating stimulus became more responsive and expanded, because neurons with preferences differing by less than 30° from the orientation of the conditioning grating shifted their orientation preference toward the conditioned orientation. When conditioning stimuli induced no or only weak ɣ-oscillations, responsiveness of neurons driven by the conditioning stimulus decreased. These differential effects depended on the power of oscillations in the low ɣ-band (20 Hz to 48 Hz) and not on differences in discharge rate of cortical neurons, because there was no correlation between the discharge rates during conditioning and the occurrence of changes in orientation preference. Thus, occurrence and polarity of use-dependent long-term changes of cortical response properties appear to depend on the occurrence of ɣ-oscillations during induction and hence on the degree of temporal coherence of the change-inducing network activity.

Plasticity and recovery of function

The frontal lobe plays a crucial role in human motor behavior. It is one of the last areas of the brain to mature, especially the prefrontal regions. After a brief historical perspective on the perceived dichotomy between the view of the brain as a static organ and that of a plastic, constantly changing structure, we discuss the stability/plasticity dilemma including examples of documented cortical reorganization taking place at multiple spatial and temporal scales. We pose that while plasticity is needed for motor learning, stability of the system is necessary for storage and maintenance of memorized skills. We discuss how this plasticity/stability dilemma is resolved along the life span and after a brain injury. We then examine the main challenges that clinicians have to overcome to promote recovery of function in patients with brain lesions, including attempts to use neurostimulation techniques as adjuvant to training-based customary neurorehabilitation.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Using EMG to deliver lumbar dynamic electrical stimulation to facilitate cortico-spinal excitability

BACKGROUND

Potentiation of synaptic activity in spinal networks is reflected in the magnitude of modulation of motor responses evoked by spinal and cortical input. After spinal cord injury, motor evoked responses can be facilitated by pairing cortical and peripheral nerve stimuli.

OBJECTIVE

To facilitate synaptic potentiation of cortico-spinal input with epidural electrical stimulation, we designed a novel neuromodulation method called dynamic stimulation (DS), using patterns derived from hind limb EMG signal during stepping.

METHODS

DS was applied dorsally to the lumbar enlargement through a high-density epidural array composed of independent platinum-based micro-electrodes.

RESULTS

In fully anesthetized intact adult rats, at the interface array/spinal cord, the temporal and spatial features of DS neuromodulation affected the entire lumbosacral network, particularly the most rostral and caudal segments covered by the array. DS induced a transient (at least 1 min) increase in spinal cord excitability and, compared to tonic stimulation, generated a more robust potentiation of the motor output evoked by single pulses applied to the spinal cord. When sub-threshold pulses were selectively applied to a cortical motor area, EMG responses from the contralateral leg were facilitated by the delivery of DS to the lumbosacral cord. Finally, based on motor-evoked responses, DS was linked to a greater amplitude of motor output shortly after a calibrated spinal cord contusion.

CONCLUSION

Compared to traditional tonic waveforms, DS amplifies both spinal and cortico-spinal input aimed at spinal networks, thus significantly increasing the potential and accelerating the rate of functional recovery after a severe spinal lesion.

Neuromuscular electrical stimulation-promoted plasticity of the human brain

The application of neuromuscular electrical stimulation (NMES) to paretic limbs has demonstrated utility for motor rehabilitation following brain injury. When NMES is delivered to a mixed peripheral nerve, typically both efferent and afferent fibres are recruited. Muscle contractions brought about by the excitation of motor neurons are often used to compensate for disability by assisting actions such as the formation of hand aperture, or by preventing others including foot drop. In this context, exogenous stimulation provides a direct substitute for endogenous neural drive. The goal of the present narrative review is to describe the means through which NMES may also promote sustained adaptations within central motor pathways, leading ultimately to increases in (intrinsic) functional capacity. There is an obvious practical motivation, in that detailed knowledge concerning the mechanisms of adaptation has the potential to inform neurorehabilitation practice. In addition, responses to NMES provide a means of studying CNS plasticity at a systems level in humans. We summarize the fundamental aspects of NMES, focusing on the forms that are employed most commonly in clinical and experimental practice. Specific attention is devoted to adjuvant techniques that further promote adaptive responses to NMES thereby offering the prospect of increased therapeutic potential. The emergent theme is that an association with centrally initiated neural activity, whether this is generated in the context of NMES triggered by efferent drive or via indirect methods such as mental imagery, may in some circumstances promote the physiological changes that can be induced through peripheral electrical stimulation.

Nanoscale resistive switching memory devices: a review

In this review the different concepts of nanoscale resistive switching memory devices are described and classified according to their I-V behaviour and the underlying physical switching mechanisms. By means of the most important representative devices, the current state of electrical performance characteristics is illuminated in-depth. Moreover, the ability of resistive switching devices to be integrated into state-of-the-art CMOS circuits under the additional consideration with a suitable selector device for memory array operation is assessed. From this analysis, and by factoring in the maturity of the different concepts, a ranking methodology for application of the nanoscale resistive switching memory devices in the memory landscape is derived. Finally, the suitability of the different device concepts for beyond pure memory applications, such as brain inspired and neuromorphic computational or logic in memory applications that strive to overcome the vanNeumann bottleneck, is discussed.

Theories of rhythmogenesis

Rhythmogenesis is the process that develops the capacity for rhythmic activity in a non-rhythmic system. Theoretical works suggested a wide array of possible mechanisms for rhythmogenesis ranging from the regulation of cellular properties to top-down control. Here we discuss theories of rhythmogenesis with an emphasis on spike timing-dependent plasticity. We argue that even though the specifics of different mechanisms vary greatly they all share certain key features. Namely, rhythmogenesis can be described as a flow on the phase diagram leading the system into a rhythmic region and stabilizing it on a specific manifold characterized by the desired rhythmic activity. Functionality is retained despite biological diversity by forcing the system into a specific manifold, but allowing fluctuations within that manifold.

Endocannabinoid-Dependent Long-Term Potentiation of Synaptic Transmission at Rat Barrel Cortex

Brain-derived neurotrophic factor (BDNF) plays a critical role in modulating plasticity in sensory cortices. Indeed, a BDNF-dependent long-term potentiation (LTP) at distal basal excitatory synapses of Layer 5 pyramidal neurons (L5PNs) has been demonstrated in disinhibited rat barrel cortex slices. Although it is well established that this LTP requires the pairing of excitatory postsynaptic potentials (PSPs) with Ca2+ spikes, its induction when synaptic inhibition is working remains unexplored. Here we show that low-frequency stimulation at basal dendrites of L5PNs is able to trigger a PSP followed by an action potential (AP) and a slow depolarization (termed PSP-Ca2+ response) in thalamocortical slices without blocking synaptic inhibition. We demonstrate that AP barrage-mediated release of endocannabinoids (eCBs) from the recorded L5PNs induces PSP-Ca2+ response facilitation and BDNF-dependent LTP. Indeed, this LTP requires the type 1 cannabinoid receptors activation, is prevented by postsynaptic intracellular 1,2-bis(2-aminophenoxy) ethane-N,N,N,N'-tetraacetic acid (BAPTA) or the anandamide membrane transporter inhibitor AM404, and only occurs in L5PNs neurons showing depolarization-induced suppression of inhibition. Additionally, electrical stimulation at the posteromedial thalamic nucleus induced similar response and LTP. These results reveal a novel form of eCB-dependent LTP at L5PNs that could be relevant in the processing of sensory information in the barrel cortex.

A Hypothetical Model Concerning How Spike-Timing-Dependent Plasticity Contributes to Neural Circuit Formation and Initiation of the Critical Period in Barrel Cortex

Spike timing is an important factor in the modification of synaptic strength. Various forms of spike timing-dependent plasticity (STDP) occur in the brains of diverse species, from insects to humans. In unimodal STDP, only LTP or LTD occurs at the synapse, regardless of which neuron spikes first; the magnitude of potentiation or depression increases as the time between presynaptic and postsynaptic spikes decreases. This from of STDP may promote developmental strengthening or weakening of early projections. In bidirectional Hebbian STDP, the magnitude and the sign (potentiation or depression) of plasticity depend, respectively, on the timing and the order of presynaptic and postsynaptic spikes. In the rodent barrel cortex, multiple forms of STDP appear sequentially during development, and they contribute to network formation, retraction, or fine-scale functional reorganization. Hebbian STDP appears at L4-L2/3 synapses starting at postnatal day (P) 15; the synapses exhibit unimodal "all-LTP STDP" before that age. The appearance of Hebbian STDP at L4-L2/3 synapses coincides with the maturation of parvalbumin-containing GABA interneurons in L4, which contributes to the generation of L4-before-L2/3 spiking in response to thalamic input by producing fast feedforward suppression of both L4 and L2/3 cells. After P15, L4-L2/3 STDP mediates fine-scale circuit refinement, essential for the critical period in the barrel cortex. In this review, we first briefly describe the relevance of STDP to map plasticity in the barrel cortex, then look over roles of distinct forms of STDP during development. Finally, we propose a hypothesis that explains the transition from network formation to the initiation of the critical period in the barrel cortex.

Repetitive transcranial magnetic stimulation: Re-wiring the alcoholic human brain

Alcohol use disorders (AUDs) are one of the leading causes of mortality and morbidity worldwide. In spite of significant advances in understanding the neural underpinnings of AUDs, therapeutic options remain limited. Recent studies have highlighted the potential of repetitive transcranial magnetic stimulation (rTMS) as an innovative, safe, and cost-effective treatment for AUDs. Here, we summarize the fundamental principles of rTMS and its putative mechanisms of action via neurocircuitries related to alcohol addiction. We will also discuss advantages and limitations of rTMS, and argue that Hebbian plasticity and connectivity changes, as well as state-dependency, play a role in shaping some of the long-term effects of rTMS. Visual imaging studies will be linked to recent clinical pilot studies describing the effect of rTMS on alcohol craving and intake, pinpointing new advances, and highlighting conceptual gaps to be filled by future controlled studies.

From membrane receptors to protein synthesis and actin cytoskeleton: Mechanisms underlying long lasting forms of synaptic plasticity

Synaptic plasticity, the activity dependent change in synaptic strength, forms the molecular foundation of learning and memory. Synaptic plasticity includes structural changes, with spines changing their size to accomodate insertion and removal of postynaptic receptors, which are correlated with functional changes. Of particular relevance for memory storage are the long lasting forms of synaptic plasticity which are protein synthesis dependent. Due to the importance of spine structural plasticity and protein synthesis, this review focuses on the signaling pathways that connect synaptic stimulation with regulation of protein synthesis and remodeling of the actin cytoskeleton. We also review computational models that implement novel aspects of molecular signaling in synaptic plasticity, such as the role of neuromodulators and spatial microdomains, as well as highlight the need for computational models that connect activation of memory kinases with spine actin dynamics.

Is plasticity of synapses the mechanism of long-term memory storage?

It has been 70 years since Donald Hebb published his formalized theory of synaptic adaptation during learning. Hebb's seminal work foreshadowed some of the great neuroscientific discoveries of the following decades, including the discovery of long-term potentiation and other lasting forms of synaptic plasticity, and more recently the residence of memories in synaptically connected neuronal assemblies. Our understanding of the processes underlying learning and memory has been dominated by the view that synapses are the principal site of information storage in the brain. This view has received substantial support from research in several model systems, with the vast majority of studies on the topic corroborating a role for synapses in memory storage. Yet, despite the neuroscience community's best efforts, we are still without conclusive proof that memories reside at synapses. Furthermore, an increasing number of non-synaptic mechanisms have emerged that are also capable of acting as memory substrates. In this review, we address the key findings from the synaptic plasticity literature that make these phenomena such attractive memory mechanisms. We then turn our attention to evidence that questions the reliance of memory exclusively on changes at the synapse and attempt to integrate these opposing views.

Whole-Cell Patch-Clamp Electrophysiology to Study Ionotropic Glutamatergic Receptors and Their Roles in Addiction

Development of the whole-cell patch-clamp electrophysiology technique has allowed for enhanced visualization and experimentation of ionic currents in neurons of mammalian tissue with high spatial and temporal resolution. Electrophysiology has become an exceptional tool for identifying single cellular mechanisms underlying behavior. Specifically, the role of glutamatergic signaling through α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors underlying behavior has been extensively studied. Here we will discuss commonly used protocols and techniques for performing whole-cell patch-clamp recordings and exploring AMPA and NMDA receptor-mediated glutamatergic responses and alterations in the context of substance abuse.

Deep learning in spiking neural networks

In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagation. Vast amounts of labeled training examples are required, but the resulting classification accuracy is truly impressive, sometimes outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and are arguably the only viable option if one wants to understand how the brain computes at the neuronal description level. The spikes of biological neurons are sparse in time and space, and event-driven. Combined with bio-plausible local learning rules, this makes it easier to build low-power, neuromorphic hardware for SNNs. However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents using backpropagation. Here we review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy and computational cost. The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNNs typically require many fewer operations and are the better candidates to process spatio-temporal data.

Artificial optic-neural synapse for colored and color-mixed pattern recognition

The priority of synaptic device researches has been given to prove the device potential for the emulation of synaptic dynamics and not to functionalize further synaptic devices for more complex learning. Here, we demonstrate an optic-neural synaptic device by implementing synaptic and optical-sensing functions together on h-BN/WSe₂ heterostructure. This device mimics the colored and color-mixed pattern recognition capabilities of the human vision system when arranged in an optic-neural network. Our synaptic device demonstrates a close to linear weight update trajectory while providing a large number of stable conduction states with less than 1% variation per state. The device operates with low voltage spikes of 0.3 V and consumes only 66 fJ per spike. This consequently facilitates the demonstration of accurate and energy efficient colored and color-mixed pattern recognition. The work will be an important step toward neural networks that comprise neural sensing and training functions for more complex pattern recognition.

Artificial neural networks can emulate the human vision because of their spike-based operation by employing memristors as synapses. Here, Seo et al. integrate synaptic and optical sensing functions in a single heterostructure, which enables accurate and energy-efficient recognition of colored patterns.

Unsupervised speech recognition through spike-timing-dependent plasticity in a convolutional spiking neural network

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier-tempotron-can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently.

Regulation of myelin structure and conduction velocity by perinodal astrocytes

The speed of impulse transmission is critical for optimal neural circuit function, but it is unclear how the appropriate conduction velocity is established in individual axons. The velocity of impulse transmission is influenced by the thickness of the myelin sheath and the morphology of electrogenic nodes of Ranvier along axons. Here we show that myelin thickness and nodal gap length are reversibly altered by astrocytes, glial cells that contact nodes of Ranvier. Thrombin-dependent proteolysis of a cell adhesion molecule that attaches myelin to the axon (neurofascin 155) is inhibited by vesicular release of thrombin protease inhibitors from perinodal astrocytes. Transgenic mice expressing a dominant-negative fragment of VAMP2 in astrocytes, to reduce exocytosis by 50%, exhibited detachment of adjacent paranodal loops of myelin from the axon, increased nodal gap length, and thinning of the myelin sheath in the optic nerve. These morphological changes alter the passive cable properties of axons to reduce conduction velocity and spike-time arrival in the CNS in parallel with a decrease in visual acuity. All effects were reversed by the thrombin inhibitor Fondaparinux. Similar results were obtained by viral transfection of tetanus toxin into astrocytes of rat corpus callosum. Previously, it was unknown how the myelin sheath could be thinned and the functions of perinodal astrocytes were not well understood. These findings describe a form of nervous system plasticity in which myelin structure and conduction velocity are adjusted by astrocytes. The thrombin-dependent cleavage of neurofascin 155 may also have relevance to myelin disruption and repair.

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Trettenbrein (2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb's (1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein's (2016) critiques of the synaptic theory in light of Hebb's theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb's theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of "intelligent" robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.

Theoretical Models of Neural Development

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

Spine-to-Dendrite Calcium Modeling Discloses Relevance for Precise Positioning of Ryanodine Receptor-Containing Spine Endoplasmic Reticulum

The endoplasmic reticulum (ER) forms a complex endomembrane network that reaches into the cellular compartments of a neuron, including dendritic spines. Recent work discloses that the spine ER is a dynamic structure that enters and leaves spines. While evidence exists that ER Ca²⁺ release is involved in synaptic plasticity, the role of spine ER morphology remains unknown. Combining a new 3D spine generator with 3D Ca²⁺ modeling, we addressed the relevance of ER positioning on spine-to-dendrite Ca²⁺ signaling. Our simulations, which account for Ca²⁺ exchange on the plasma membrane and ER, show that spine ER needs to be present in distinct morphological conformations in order to overcome a barrier between the spine and dendritic shaft. We demonstrate that RyR-carrying spine ER promotes spine-to-dendrite Ca²⁺ signals in a position-dependent manner. Our simulations indicate that RyR-carrying ER can initiate time-delayed Ca²⁺ reverberation, depending on the precise position of the spine ER. Upon spine growth, structural reorganization of the ER restores spine-to-dendrite Ca²⁺ communication, while maintaining aspects of Ca²⁺ homeostasis in the spine head. Our work emphasizes the relevance of precise positioning of RyR-containing spine ER in regulating the strength and timing of spine Ca²⁺ signaling, which could play an important role in tuning spine-to-dendrite Ca²⁺ communication and homeostasis.

Dopamine Receptor Activation Is Required for GABAergic Spike Timing-Dependent Plasticity in Response to Complex Spike Pairing in the Ventral Tegmental Area

One of the most influential synaptic learning rules explored in the past decades is activity dependent spike-timing-dependent plasticity (STDP). In STDP, synapses are either potentiated or depressed based on the order of pre- and postsynaptic neuronal activation within narrow, milliseconds-long, time intervals. STDP is subject to neuromodulation by dopamine (DA), a potent neurotransmitter that significantly impacts synaptic plasticity and reward-related behavioral learning. Previously, we demonstrated that GABAergic synapses onto ventral tegmental area (VTA) DA neurons are able to express STDP (Kodangattil et al., 2013), however it is still unclear whether DA modulates inhibitory STDP in the VTA. Here, we used whole-cell recordings in rat midbrain slices to investigate whether DA D1-like and/or D2-like receptor (D1R/D2R) activation is required for induction of STDP in response to a complex pattern of spiking. We found that VTA but not Substantia nigra pars compact (SNc) DA neurons exhibit long-term depression (LTD_GABA) in response to a combination of positive (pre-post) and negative (post-pre) timing of spiking (a complex STDP protocol). Blockade of either D1Rs or D2Rs prevented the induction of LTD_GABA while activation of D1Rs did not affect the plasticity in response to this complex STDP protocol in VTA DA neurons.Our data suggest that this DA-dependent GABAergic STDP is selectively expressed at GABAergic synapses onto VTA DA neurons which could be targeted by drugs of abuse to mediate drug-induced modulation of DA signaling within the VTA, as well as in VTA-projection areas, thereby affecting reward-related learning and drug-associated memories.

Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l ∗ and the asymmetry parameter Δ l in the SFN.

Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks

Experimental evidence recently indicated that neural networks can learn in a different manner than was previously assumed, using adaptive nodes instead of adaptive links. Consequently, links to a node undergo the same adaptation, resulting in cooperative nonlinear dynamics with oscillating effective link weights. Here we show that the biological reality of stationary log-normal distribution of effective link weights in neural networks is a result of such adaptive nodes, although each effective link weight varies significantly in time. The underlying mechanism is a stochastic restoring force emerging from a spontaneous temporal ordering of spike pairs, generated by strong effective link preceding by a weak one. In addition, for feedforward adaptive node networks the number of dynamical attractors can scale exponentially with the number of links. These results are expected to advance deep learning capabilities and to open horizons to an interplay between adaptive node rules and the distribution of network link weights.

Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses

Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.

Endocannabinoid-LTP Mediated by CB1 and TRPV1 Receptors Encodes for Limited Occurrences of Coincident Activity in Neocortex

Synaptic efficacy changes, long-term potentiation (LTP) and depression (LTD), underlie various forms of learning and memory. Synaptic plasticity is generally assessed under prolonged activation, whereas learning can emerge from few or even a single trial. Here, we investigated the existence of rapid responsiveness of synaptic plasticity in response to a few number of spikes, in neocortex in a synaptic Hebbian learning rule, the spike-timing-dependent plasticity (STDP). We investigated the effect of lowering the number of pairings from 100 to 50, and 10 on STDP expression, using whole-cell recordings from pyramidal cells in rodent somatosensory cortical brain slices. We found that a low number of paired stimulations induces LTP at neocortical layer 4–2/3 synapses. Besides the asymmetric Hebbian STDP reported in the neocortex induced by 100 pairings, we observed a symmetric anti-Hebbian LTD for 50 pairings and unveiled a unidirectional Hebbian spike-timing-dependent LTP (tLTP) induced by 10–15 pairings. This tLTP was not mediated by NMDA receptor activation but requires CB₁ receptors and transient receptor potential vanilloid type-1 (TRPV1) activated by endocannabinoids (eCBs). eCBs have been widely described as mediating short- and long-term synaptic depression. Here, the eCB-tLTP reported at neocortical synapses could constitute a substrate operating in the online learning of new associative memories or during the initial stages of learning. In addition, these findings should provide useful insight into the mechanisms underlying eCB-plasticity occurring during marijuana intoxication.