Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future

Associative Learning-Induced Synaptic Potentiation at the Two Major Hippocampal CA1 Inputs for Cued Memory Acquisition

Learning-associated functional plasticity at hippocampal synapses remains largely unexplored. Here, in a single session of reward-based trace conditioning, we examine learning-induced synaptic plasticity in the dorsal CA1 hippocampus (dCA1). Local field-potential recording combined with selective optogenetic inhibition first revealed an increase of dCA1 synaptic responses to the conditioned stimulus (CS) induced during conditioning at both Schaffer collaterals to the stratum radiatum (Rad) and temporoammonic input to the lacunosum moleculare (LMol). At these dCA1 inputs, synaptic potentiation of CS-responding excitatory synapses was further demonstrated by locally blocking NMDA receptors during conditioning and whole-cell recording sensory-evoked synaptic responses in dCA1 neurons from naive animals. An overall similar time course of the induction of synaptic potentiation was found in the Rad and LMol by multiple-site recording; this emerged later and saturated earlier than conditioned behavioral responses. Our experiments demonstrate a cued memory-associated dCA1 synaptic plasticity induced at both Schaffer collaterals and temporoammonic pathways.

Amygdalo-cortical dialogue underlies memory enhancement by emotional association

Emotional arousal plays a critical role in determining what is remembered from experiences. It is hypothesized that activation of the amygdala by emotional stimuli enhances memory consolidation in its downstream brain regions. However, the physiological basis of the inter-regional interaction and its functions remain unclear. Here, by adding emotional information to a perceptual recognition task that relied on a frontal-sensory cortical circuit in mice, we demonstrated that the amygdala not only associates emotional information with perceptual information but also enhances perceptual memory retention via amygdalo-frontal cortical projections. Furthermore, emotional association increased reactivation of coordinated activity across the amygdalo-cortical circuit during non-rapid eye movement (NREM) sleep but not during rapid eye movement (REM) sleep. Notably, this increased reactivation was associated with amygdala high-frequency oscillations. Silencing of amygdalo-cortical inputs during NREM sleep selectively disrupted perceptual memory enhancement. Our findings indicate that inter-regional reactivation triggered by the amygdala during NREM sleep underlies emotion-induced perceptual memory enhancement.

Mechano-gated iontronic piezomemristor for temporal-tactile neuromorphic plasticity

In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity. This system utilizes a bicontinuous phase-transition heterogel as a stiffness-governed iontronic mechanogate to achieve bidirectional piezoresistive signals, resulting in wide-span dynamic tactile sensing. By micro-integrating the mechanogate with an oscillatory iontronic memristor, it exhibits stiffness-induced bipolarized excitatory and inhibitory neuromorphics, thereby enabling the activation of temporal-tactile memory and learning functions (e.g., Bienenstock-Cooper-Munro and Hebbian learning rules). Owing to dynamic covalent bond network and iontronic features, reconfigurable tactile plasticity can be achieved. Importantly, bridging to bioneuronal interfaces, these systems possess the capacity to construct a biohybrid perception-actuation circuit. We anticipate that such temporal plastic piezomemristor devices for abiotic-biotic interfaces can serve as promising hardware systems for interfacing dynamic tactile behaviors into diverse neuromodulations.

Repetitive Transcranial Magnetic Stimulation for the Treatment of Spinal Cord Injury: Current Status and Perspective

Spinal cord injury (SCI) can lead to devastating dysfunctions and complications, significantly impacting patients' quality of life and aggravating the burden of disease. Since the main pathological mechanism of SCI is the disruption of neuronal circuits, the primary therapeutic strategy for SCI involves reconstructing and activating circuits to restore neural signal transmission. Repetitive transcranial magnetic stimulation (rTMS), a noninvasive brain stimulation technique, can modulate the function or state of the nervous system by pulsed magnetic fields. Here, we discuss the basic principles and potential mechanisms of rTMS for treating SCI, including promoting the reconstruction of damaged circuits in the spinal cord, activating reorganization of the cerebral cortex and circuits, modulating the balance of inputs to motoneurons, improving the microenvironment and intrinsic regeneration ability in SCI. Based on these mechanisms, we provide an overview of the therapeutic effects of rTMS in SCI patients with motor dysfunction, spasticity and neuropathic pain. We also discuss the challenges and prospectives of rTMS, especially the potential of combination therapy of rTMS and neural progenitor cell transplantation, and the synergistic effects on promoting regeneration, relay formation and functional connectivity. This review is expected to offer a relatively comprehensive understanding and new perspectives of rTMS for SCI treatment.

Data-driven synapse classification reveals a logic of glutamate receptor diversity

The rich diversity of synapses facilitates the capacity of neural circuits to transmit, process and store information. We used multiplex super-resolution proteometric imaging through array tomography to define features of single synapses in mouse neocortex. We find that glutamatergic synapses cluster into subclasses that parallel the distinct biochemical and functional categories of receptor subunits: GluA1/4, GluA2/3 and GluN1/GluN2B. Two of these subclasses align with physiological expectations based on synaptic plasticity: large AMPAR-rich synapses may represent potentiated synapses, whereas small NMDAR-rich synapses suggest "silent" synapses. The NMDA receptor content of large synapses correlates with spine neck diameter, and thus the potential for coupling to the parent dendrite. Overall, ultrastructural features predict receptor content of synapses better than parent neuron identity does, suggesting synapse subclasses act as fundamental elements of neuronal circuits. No barriers prevent future generalization of this approach to other species, or to study of human disorders and therapeutics.

An ultra low frequency spike timing dependent plasticity based approach for reducing alcohol drinking

Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S. alone. The effects of alcohol consumption are expected to increase significantly during the COVID-19 pandemic, with alcohol sales increasing by approximately 54%, potentially exacerbating health concerns and risk-taking behaviors. Unfortunately, existing pharmacological and behavioral therapies for AUD are associated with poor success rates, with approximately 40% of individuals relapsing within three years of treatment.Pre-clinical studies have shown that chronic alcohol consumption leads to significant changes in synaptic function within the dorsal medial striatum (DMS), one of the brain regions associated with AUD and responsible for mediating goal-directed behavior. Specifically, chronic alcohol consumption has been associated with hyperactivity of dopamine receptor 1 (D1) medium spiny neurons (MSN) and hypoactivity of dopamine receptor 2 (D1) MSNs within the DMS. Optogenetic, chemogenetic, and transgenic approaches have demonstrated that reducing the D1/D2 MSN signaling imbalance decreases alcohol self-administration in rodent models of AUD.Here, we present an electrical stimulation approach that uses ultra-low (≤ 1 Hz) frequency (ULF) spike-timing-dependent plasticity (STDP) in mouse models of AUD to reduce DMS D1/D2 MSN signaling imbalances by stimulating D1-MSN afferents into the GPi and ACC glutamatergic projections to the DMS in a time-locked stimulation sequence. Our data suggest that GPi/ACC ULF-STDP selectively decreases DMS D1-MSN hyperactivity leading to reduced alcohol consumption without evoking undesired affective behaviors using electrical stimulation rather than approaches requiring genetic modification. This work represents a step towards fulfilling the unmet need for a reliable method of treating severe AUD through cell-type-specific control with clinically available neuromodulation tools.

Rewiring the disordered connectome with circuit-based paired stimulation after stroke-a randomized, double-blind and controlled Phase II trial

The cortico-cortical paired associative stimulation, a combined stimulation based on two brain regions, may be an effective strategy for stroke rehabilitation. Our aim was to confirm that the cortico-cortical paired associative stimulation strengthens the connection between brain regions in the motor circuit and promotes improvements in motor function. This was a randomized double-blind, controlled Phase II trial. 44 Stroke patients were treated in a rehabilitation hospital from October 2020 to January 2021 and were randomly assigned to the sham stimulation group and the cortico-cortical paired associative stimulation group. Patients in both groups received 12 days of rehabilitation therapy. Cortico-cortical paired associative stimulation group received one treatment of cortico-cortical paired associative stimulation invention. Both groups received behavioural assessments such as the Fugl-Meyer upper-extremity scale and resting-state functional MRI scans prior to the intervention and on Day 14. 40 patients completed the intervention session. The results of Fugl-Meyer upper-extremity scale showed a more significant improvement in motor function in the cortico-cortical paired associative stimulation group (6.33 ± 1.29) than in the sham stimulation group (3.16 ± 1.38) (P < 0.001). The functional connectivity showed that cortico-cortical paired associative stimulation strengthens connections between brain regions. Correlation analysis confirmed that the enhancement of functional connectivity was positively correlated with the recovery of Fugl-Meyer upper-extremity scale (r2 = 0.146, P = 0.034; r2 = 0.211, P = 0.0093). The results of functional connectivity suggest that cortico-cortical paired associative stimulation strengthens connections between brain regions. It is expected that this study will provide a positive viewpoint for the neurorehabilitation of stroke patients based on the circuit-level plasticity. (Chinese Clinical Trial Registry: ChiCTR2000036685).

The ventral midline thalamus and long-term memory: What consolidation, what retrieval, what plasticity in rodents?

The ventral midline thalamus, including the reuniens and rhomboid (ReRh) nuclei, connects bidirectionally with the medial prefrontal cortex (mPFC) and hippocampus (Hip), both essential for memory processes. This review compiles and discusses studies on a role for the ReRh nuclei in the system consolidation of memories, also considering their potentially limited participation in memory retrieval or early phases of consolidation. It also examines scientific literature on short- and long-term plasticity in ReRh-mPFC and ReRh-Hip connections, emphasizing plasticity's importance in understanding these nuclei's role in memory. The idea that the two nuclei are at the crossroads of information exchange between the mPFC and the Hip is not new, but the relationship between this status and the plasticity of their connections remains elusive. Since this perspective is relatively recent, our concluding section suggests conceptual and practical avenues for future research, aiming perhaps to bring more order to the apparently multi-functional implication of the ventral midline thalamus in cognition.

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.

Inverse stochastic resonance in adaptive small-world neural networks

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

The role of astrocytes from synaptic to non-synaptic plasticity

Information storage and transfer in the brain require a high computational power. Neuronal network display various local or global mechanisms to allow information storage and transfer in the brain. From synaptic to intrinsic plasticity, the rules of input-output function modulation have been well characterized in neurons. In the past years, astrocytes have been suggested to increase the computational power of the brain and we are only just starting to uncover their role in information processing. Astrocytes maintain a close bidirectional communication with neurons to modify neuronal network excitability, transmission, axonal conduction, and plasticity through various mechanisms including the release of gliotransmitters or local ion homeostasis. Astrocytes have been significantly studied in the context of long-term or short-term synaptic plasticity, but this is not the only mechanism involved in memory formation. Plasticity of intrinsic neuronal excitability also participates in memory storage through regulation of voltage-gated ion channels or axonal morphological changes. Yet, the contribution of astrocytes to these other forms of non-synaptic plasticity remains to be investigated. In this review, we summarized the recent advances on the role of astrocytes in different forms of plasticity and discuss new directions and ideas to be explored regarding astrocytes-neuronal communication and regulation of plasticity.

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators

Neuromodulatory processes in the brain can critically change signal processing on a cellular level, leading to dramatic changes in network level reorganization. Here, we use coupled nonidentical Kuramoto oscillators to investigate how changes in the shape of phase response curves from Type 1 to Type 2, mediated by varying ACh levels, coupled with activity-dependent plasticity may alter network reorganization. We first show that, when plasticity is absent, the Type 1 networks with symmetric adjacency matrix, as expected, exhibit asynchronous dynamics with oscillators of the highest natural frequency robustly evolving faster in terms of their phase dynamics. However, interestingly, Type 1 networks with an asymmetric connectivity matrix can produce stable synchrony (so-called splay states) with complex phase relationships. At the same time, Type 2 networks synchronize independent of the symmetry of their connectivity matrix, with oscillators locked so that those with higher natural frequency have a constant phase lead as compared to those with lower natural frequency. This relationship establishes a robust mapping between the frequency and oscillators' phases in the network, leading to structure and frequency mapping when plasticity is present. Finally, we show that biologically realistic, phase-locking dependent, connection plasticity naturally produces splay states in Type 1 networks that do not display the structure-frequency reorganization observed in synchronized Type II networks. These results indicate that the formation of splay states in the brain could be a common phenomenon.

Organotypic culture of post-mortem adult human brain explants exhibits synaptic plasticity

BACKGROUND

Synaptic plasticity is an essential process encoding fine-tuned brain functions, but models to study this process in adult human systems are lacking.

OBJECTIVE

We aim to test whether ex vivo organotypic culture of post-mortem adult brain explants (OPABs) retain synaptic plasticity.

METHODS

OPABs were seeded on 3D microelectrode arrays to measure local field potential (LFP). Paired stimulation of distant electrodes was performed over three days to investigate our capacity to modulate specific neuronal connections.

RESULTS

Long-term potentiation (LTP) or depression (LTD) did not occur within a single day. In contrast, after two and three days of training, OPABs showed a significant modulation of the paired electrodes' response compared to the non-paired electrodes from the same array. This response was alleviated upon treatment with dopamine.

CONCLUSION

Our work highlights that adult human brain explants retain synaptic plasticity, offering novel approaches to neural circuitry in animal-free models.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

Astrocyte D1/D5 Dopamine Receptors Govern Non-Hebbian Long-Term Potentiation at Sensory Synapses onto Lamina I Spinoparabrachial Neurons

Recent work demonstrated that activation of spinal D1 and D5 dopamine receptors (D1/D5Rs) facilitates non-Hebbian long-term potentiation (LTP) at primary afferent synapses onto spinal projection neurons. However, the cellular localization of the D1/D5Rs driving non-Hebbian LTP in spinal nociceptive circuits remains unknown, and it is also unclear whether D1/D5R signaling must occur concurrently with sensory input in order to promote non-Hebbian LTP at these synapses. Here we investigate these issues using cell-type-selective knockdown of D1Rs or D5Rs from lamina I spinoparabrachial neurons, dorsal root ganglion (DRG) neurons, or astrocytes in adult mice of either sex using Cre recombinase-based genetic strategies. The LTP evoked by low-frequency stimulation of primary afferents in the presence of the selective D1/D5R agonist SKF82958 persisted following the knockdown of D1R or D5R in spinoparabrachial neurons, suggesting that postsynaptic D1/D5R signaling was dispensable for non-Hebbian plasticity at sensory synapses onto these key output neurons of the superficial dorsal horn (SDH). Similarly, the knockdown of D1Rs or D5Rs in DRG neurons failed to influence SKF82958-enabled LTP in lamina I projection neurons. In contrast, SKF82958-induced LTP was suppressed by the knockdown of D1R or D5R in spinal astrocytes. Furthermore, the data indicate that the activation of D1R/D5Rs in spinal astrocytes can either retroactively or proactively drive non-Hebbian LTP in spinoparabrachial neurons. Collectively, these results suggest that dopaminergic signaling in astrocytes can strongly promote activity-dependent LTP in the SDH, which is predicted to significantly enhance the amplification of ascending nociceptive transmission from the spinal cord to the brain.

Mixed selectivity: Cellular computations for complexity

The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.

Gradient-free training of recurrent neural networks using random perturbations

Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.

The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates

Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.

Sensory encoding and memory in the mushroom body: signals, noise, and variability

To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.

Disentangling neuroplasticity mechanisms in post-stroke language recovery

A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.

The promotion-like effect of the M1-STN hyperdirect pathway induced by ccPAS enhanced balance performances: From the perspective of brain connectivity

AIMS

The present study aimed to explore the effect of cortico-cortical paired-associative stimulation (ccPAS) in modulating hyperdirect pathway and its influence on balance performance.

METHODS

Forty healthy participants were randomly allocated to the active ccPAS group (n = 20) or the sham ccPAS group (n = 20). The primary motor cortex and subthalamic nucleus were stimulated sequentially with ccPAS. Unlike the active ccPAS group, one wing of coil was tilted to form a 90° angle with scalp of stimulation locations for the sham ccPAS group. Magnetic resonance imaging, functional reach test (FRT), timed up and go (TUG) test, and limit of stability (LOS) test were performed, and correlation between them was also analyzed.

RESULTS

Three participants in the sham ccPAS group were excluded because of poor quality of NIfTI images. The active group had strengthened hyperdirect pathway, increased functional connectivity (FC) between orbital part of frontal cortex and bilateral precuneus, and decreased FC among basal ganglia (all p < 0.05). Regional network properties of triangular and orbital parts of IFG, middle cingulate cortex, and hippocampus increased. The active group performed better in FRT and LOS (all p < 0.05). FRT positively correlated with FC of the hyperdirect pathway (r = 0.439, p = 0.007) and FCs between orbital part of frontal cortex and bilateral precuneus (all p < 0.05).

CONCLUSION

The ccPAS enhanced balance performance by promotion-like plasticity mechanisms through the hyperdirect pathway.

Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks

OBJECTIVE

Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags.

METHODS

We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity.

RESULTS

We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods.

CONCLUSION

The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks.

SIGNIFICANCE

The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.

A hybrid transistor with transcriptionally controlled computation and plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Gamma oscillation plasticity is mediated via parvalbumin interneurons

Understanding the plasticity of neuronal networks is an emerging field of (patho-) physiological research, yet the underlying cellular mechanisms remain poorly understood. Gamma oscillations (30 to 80 hertz), a biomarker of cognitive performance, require and potentiate glutamatergic transmission onto parvalbumin-positive interneurons (PVIs), suggesting an interface for cell-to-network plasticity. In ex vivo local field potential recordings, we demonstrate long-term potentiation of hippocampal gamma power. Gamma potentiation obeys established rules of PVI plasticity, requiring calcium-permeable AMPA receptors (CP-AMPARs) and metabotropic glutamate receptors (mGluRs). A microcircuit computational model of CA3 gamma oscillations predicts CP-AMPAR plasticity onto PVIs critically outperforms pyramidal cell plasticity in increasing gamma power and completely accounts for gamma potentiation. We reaffirm this ex vivo in three PVI-targeting animal models, demonstrating that gamma potentiation requires PVI-specific signaling via a Gq/PKC pathway comprising mGluR5 and a Gi-sensitive, PKA-dependent pathway. Gamma activity-dependent, metabotropically mediated CP-AMPAR plasticity on PVIs may serve as a guiding principle in understanding network plasticity in health and disease.

Astrocytes in Pain Perception: A Systems Neuroscience Approach

Astrocytes play an active role in the function of the brain integrating neuronal activity and regulating back neuronal dynamic. They have recently emerged as active contributors of brain's emergent properties such as perceptions. Here, we analyzed the role of astrocytes in pain perception from the lens of systems neuroscience, and we do this by analyzing how astrocytes encode nociceptive information within brain processing areas and how they are key regulators of the internal state that determines pain perception. Specifically, we discuss the dynamic interactions between astrocytes and neuromodulators, such as noradrenaline, highlighting their role in shaping the level of activation of the neuronal ensemble, thereby influencing the experience of pain. Also, we will discuss the possible implications of an "Astro-NeuroMatrix" in the integration of pain across sensory, affective, and cognitive dimensions of pain perception.

Metabolic energetics underlying attractors in neural models

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates

Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. Here, we developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) and applied these in a monkey performing a behavioral task. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys-measurement sensors, producing signals that may falsely be classified as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. This spike extraction was performed on data collected from concurrent EChem and EPhys recordings made in a task-performing monkey to discriminate cell-type specific striatal units. These identified units were shown to correlate to specific behavioral task parameters related to reward size and eye-movement direction. Synchronous measures of spike and dopamine signals displayed contrasting relations to the behavioral task parameters, as taken from our small set of representative data, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.

Synaptic plasticity through a naturalistic lens

From the myriad of studies on neuronal plasticity, investigating its underlying molecular mechanisms up to its behavioral relevance, a very complex landscape has emerged. Recent efforts have been achieved toward more naturalistic investigations as an attempt to better capture the synaptic plasticity underpinning of learning and memory, which has been fostered by the development of in vivo electrophysiological and imaging tools. In this review, we examine these naturalistic investigations, by devoting a first part to synaptic plasticity rules issued from naturalistic in vivo-like activity patterns. We next give an overview of the novel tools, which enable an increased spatio-temporal specificity for detecting and manipulating plasticity expressed at individual spines up to neuronal circuit level during behavior. Finally, we put particular emphasis on works considering brain-body communication loops and macroscale contributors to synaptic plasticity, such as body internal states and brain energy metabolism.

Synchronization in STDP-driven memristive neural networks with time-varying topology

Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.

Acetylcholine facilitates localized synaptic potentiation and location specific feature binding

Forebrain acetylcholine (ACh) signaling has been shown to drive attention and learning. Recent experimental evidence of spatially and temporally constrained cholinergic signaling has sparked interest to investigate how it facilitates stimulus-induced learning. We use biophysical excitatory-inhibitory (E-I) multi-module neural network models to show that external stimuli and ACh signaling can mediate spatially constrained synaptic potentiation patterns. The effects of ACh on neural excitability are simulated by varying the conductance of a muscarinic receptor-regulated hyperpolarizing slow K+ current (m-current). Each network module consists of an E-I network with local excitatory connectivity and global inhibitory connectivity. The modules are interconnected with plastic excitatory synaptic connections, that change via a spike-timing-dependent plasticity (STDP) rule. Our results indicate that spatially constrained ACh release influences the information flow represented by network dynamics resulting in selective reorganization of inter-module interactions. Moreover the information flow depends on the level of synchrony in the network. For highly synchronous networks, the more excitable module leads firing in the less excitable one resulting in strengthening of the outgoing connections from the former and weakening of its incoming synapses. For networks with more noisy firing patterns, activity in high ACh regions is prone to induce feedback firing of synchronous volleys and thus strengthening of the incoming synapses to the more excitable region and weakening of outgoing synapses. Overall, these results suggest that spatially and directionally specific plasticity patterns, as are presumed necessary for feature binding, can be mediated by spatially constrained ACh release.

Can we manipulate brain connectivity? A systematic review of cortico-cortical paired associative stimulation effects

OBJECTIVE

Cortico-cortical paired associative stimulation (ccPAS) is a form of dual-site transcranial magnetic stimulation (TMS) entailing a series of single-TMS pulses paired at specific interstimulus intervals (ISI) delivered to distant cortical areas. The goal of this article is to systematically review its efficacy in inducing plasticity in humans focusing on stimulation parameters and hypotheses of underlying neurophysiology.

METHODS

A systematic review of the literature from 2009-2023 was undertaken to identify all articles utilizing ccPAS to study brain plasticity and connectivity. Six electronic databases were searched and included.

RESULTS

32 studies were identified. The studies targeted connections within the same hemisphere or between hemispheres. 28 ccPAS studies were in healthy participants, 1 study in schizophrenia, and 1 in Alzheimer's disease (AD) patients. 2 additional studies used cortico-cortical repetitive paired associative stimulation (cc-rPAS) in generalized anxiety disorder (GAD) patients. Outcome measures include electromyography (EMG), behavioral measures, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). ccPAS seems to be able to modulate brain connectivity depending on the ISI.

CONCLUSIONS

ccPAS can be used to modulate corticospinal excitability, brain activity, and behavior. Although the stimulation parameters used across studies reviewed in this paper are varied, ccPAS is a promising approach for basic research and potential clinical applications.

SIGNIFICANCE

Recent advances in neuroscience have caused a shift of interest from the study of single areas to a more complex approach focusing on networks of areas that orchestrate brain activity. Consequently, the TMS community is also witnessing a change, with a growing interest in targeting multiple brain areas rather than a single locus, as evidenced by an increasing number of papers using ccPAS. In light of this new enthusiasm for brain connectivity, this review summarizes existing literature and stimulation parameters that have proven effective in changing electrophysiological, behavioral, or neuroimaging-derived measures.

An event-driven Spike-DBN model for fault diagnosis using reward-STDP

Deep neural networks (DNNs) have shown high accuracy in fault diagnosis, but they struggle to effectively capture changes over time in multivariate time-series data and suffer from resource consumption issues. Spike deep belief networks (spike-DBNs) address these limitations by capturing the change in time-varying signals and reducing resource consumption, but they sacrifice accuracy. To overcome these limitations, we propose integrating an event-driven approach into spike-DBNs through the Latency-Rate coding method and the reward-STDP learning rule. The encoding method enhances the event representation capability, while the learning rule focuses on the global behavior of spiking neurons triggered by events. Our proposed method not only maintains low resource consumption but also improves the fault diagnosis ability of spike-DBNs. We conducted a series of experiments to verify our model's performance, and the results demonstrate that our proposed method improves the accuracy of fault classification of manipulators and reduces learning time by nearly 76% compared to spike-CNN under the same conditions.

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Advancements in Transcranial Magnetic Stimulation Research and the Path to Precision

Transcranial magnetic stimulation (TMS) has become increasingly popular in clinical practice in recent years, and there have been significant advances in the principles and stimulation modes of TMS. With the development of multi-mode and precise stimulation technology, it is crucial to have a comprehensive understanding of TMS. The neuroregulatory effects of TMS can vary depending on the specific mode of stimulation, highlighting the importance of exploring these effects through multimodal application. Additionally, the use of precise TMS therapy can help enhance our understanding of the neural mechanisms underlying these effects, providing us with a more comprehensive perspective. This article aims to review the mechanism of action, stimulation mode, multimodal application, and precision of TMS.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Sub-anesthetic doses of ketamine increase single cell entrainment in the rat auditory cortex during auditory steady-state response

BACKGROUND

Understanding the effects of the N-methyl-D-aspartate receptor (NMDA-R) antagonist ketamine on brain function is of considerable interest due to the discovery of its fast-acting antidepressant properties. It is well known that gamma oscillations are increased when ketamine is administered to rodents and humans, and increases in the auditory steady-state response (ASSR) have also been observed.

AIMS

To elucidate the cellular substrate of the increase in network activity and synchrony observed by sub-anesthetic doses of ketamine, the aim was to investigate spike timing and regularity and determine how this is affected by the animal's motor state.

METHODS

Single unit activity and local field potentials from the auditory cortex of awake, freely moving rats were recorded with microelectrode arrays during an ASSR paradigm.

RESULTS

Ketamine administration yielded a significant increase in ASSR power and phase locking, both significantly modulated by motor activity. Before drug administration, putative fast-spiking interneurons (FSIs) were significantly more entrained to the stimulus than putative pyramidal neurons (PYRs). The degree of entrainment significantly increased at lower doses of ketamine (3 and 10 mg/kg for FSIs, 10 mg/kg for PYRs). At the highest dose (30 mg/kg), a strong increase in tonic firing of PYRs was observed.

CONCLUSIONS

These findings suggest an involvement of FSIs in the increased network synchrony and provide a possible cellular explanation for the well-documented effects of ketamine-induced increase in power and synchronicity during ASSR. The results support the importance to evaluate different motor states separately for more translational preclinical research.

Incorporating structural plasticity into self-organization recurrent networks for sequence learning

Introduction

Spiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-dependent plasticity (STDP), structural plasticity, and homeostatic plasticity, making network connection patterns and weights to change continuously during the lifecycle. However, it is unclear how these plasticities interact to shape neural networks and affect neural signal processing.

Method

Here, we propose a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate this issue. Specifically, RSRN-SP uses spikes to encode information, and incorporate multiple plasticities including reward-modulated spike timing-dependent plasticity (R-STDP), homeostatic plasticity, and structural plasticity. On the one hand, combined with homeostatic plasticity, R-STDP is presented to guide the updating of synaptic weights. On the other hand, structural plasticity is utilized to simulate the growth and pruning of synaptic connections.

Results and discussion

Extensive experiments for sequential learning tasks are conducted to demonstrate the representational ability of the RSRN-SP, including counting task, motion prediction, and motion generation. Furthermore, the simulations also indicate that the characteristics arose from the RSRN-SP are consistent with biological observations.

Dendritic, delayed, and stochastic CaMKII activation underlies behavioral time scale plasticity in CA1 synapses

Behavioral time scale plasticity (BTSP), is a form of non-Hebbian plasticity induced by integrating pre- and postsynaptic components separated by behavioral time scale (seconds). BTSP in the hippocampal CA1 neurons underlies place cell formation. However, the molecular mechanisms underlying this behavioral time scale (eligibility trace) and synapse specificity are unknown. CaMKII can be activated in a synapse-specific manner and remain active for a few seconds, making it a compelling candidate for the eligibility trace during BTSP. Here, we show that BTSP can be induced in a single dendritic spine using 2-photon glutamate uncaging paired with postsynaptic current injection temporally separated by behavioral time scale. Using an improved CaMKII sensor, we saw no detectable CaMKII activation during this BTSP induction. Instead, we observed a dendritic, delayed, and stochastic CaMKII activation (DDSC) associated with Ca 2+ influx and plateau 20-40 s after BTSP induction. DDSC requires both pre-and postsynaptic activity, suggesting that CaMKII can integrate these two signals. Also, optogenetically blocking CaMKII 30 s after the BTSP protocol inhibited synaptic potentiation, indicating that DDSC is an essential mechanism of BTSP. IP3-dependent intracellular Ca 2+ release facilitates both DDSC and BTSP. Thus, our study suggests that the non-synapse specific CaMKII activation provides an instructive signal with an extensive time window over tens of seconds during BTSP.

Engram neurons: Encoding, consolidation, retrieval, and forgetting of memory

Tremendous strides have been made in our understanding of the neurobiological substrates of memory - the so-called memory "engram". Here, we integrate recent progress in the engram field to illustrate how engram neurons transform across the "lifespan" of a memory - from initial memory encoding, to consolidation and retrieval, and ultimately to forgetting. To do so, we first describe how cell-intrinsic properties shape the initial emergence of the engram at memory encoding. Second, we highlight how these encoding neurons preferentially participate in synaptic- and systems-level consolidation of memory. Third, we describe how these changes during encoding and consolidation guide neural reactivation during retrieval, and facilitate memory recall. Fourth, we describe neurobiological mechanisms of forgetting, and how these mechanisms can counteract engram properties established during memory encoding, consolidation, and retrieval. Motivated by recent experimental results across these four sections, we conclude by proposing some conceptual extensions to the traditional view of the engram, including broadening the view of cell-type participation within engrams and across memory stages. In collection, our review synthesizes general principles of the engram across memory stages, and describes future avenues to further understand the dynamic engram.

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits

In the mammalian brain information processing and storage rely on the complex coding and decoding events performed by neuronal networks. These actions are based on the computational ability of neurons and their functional engagement in neuronal assemblies where precise timing of action potential firing is crucial. Neuronal circuits manage a myriad of spatially and temporally overlapping inputs to compute specific outputs that are proposed to underly memory traces formation, sensory perception, and cognitive behaviors. Spike-timing-dependent plasticity (STDP) and electrical brain rhythms are suggested to underlie such functions while the physiological evidence of assembly structures and mechanisms driving both processes continues to be scarce. Here, we review foundational and current evidence on timing precision and cooperative neuronal electrical activity driving STDP and brain rhythms, their interactions, and the emerging role of glial cells in such processes. We also provide an overview of their cognitive correlates and discuss current limitations and controversies, future perspectives on experimental approaches, and their application in humans.

Neuronal and astrocyte determinants of critical periods of plasticity

Windows of plasticity allow environmental experiences to produce intense activity-dependent changes during postnatal development. The reordering and refinement of neural connections occurs during these periods, significantly influencing the formation of brain circuits and physiological processes in adults. Recent advances have shed light on factors that determine the onset and duration of sensitive and critical periods of plasticity. Although GABAergic inhibition has classically been implicated in closing windows of plasticity, astrocytes and adenosinergic inhibition have also emerged more recently as key determinants of the duration of these periods of plasticity. Here, we review novel aspects of the involvement of GABAergic inhibition, the possible role of presynaptic NMDARs, and the emerging roles of astrocytes and adenosinergic inhibition in determining the duration of windows of plasticity in different brain regions.

Biphasic Cholinergic Modulation of Reverberatory Activity in Neuronal Networks

Acetylcholine (ACh) is an important neuromodulator in various cognitive functions. However, it is unclear how ACh influences neural circuit dynamics by altering cellular properties. Here, we investigated how ACh influences reverberatory activity in cultured neuronal networks. We found that ACh suppressed the occurrence of evoked reverberation at low to moderate doses, but to a much lesser extent at high doses. Moreover, high doses of ACh caused a longer duration of evoked reverberation, and a higher occurrence of spontaneous activity. With whole-cell recording from single neurons, we found that ACh inhibited excitatory postsynaptic currents (EPSCs) while elevating neuronal firing in a dose-dependent manner. Furthermore, all ACh-induced cellular and network changes were blocked by muscarinic, but not nicotinic receptor antagonists. With computational modeling, we found that simulated changes in EPSCs and the excitability of single cells mimicking the effects of ACh indeed modulated the evoked network reverberation similar to experimental observations. Thus, ACh modulates network dynamics in a biphasic fashion, probably by inhibiting excitatory synaptic transmission and facilitating neuronal excitability through muscarinic signaling pathways.

Electrical stimulation methods and protocols for the treatment of traumatic brain injury: a critical review of preclinical research

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of disabilities resulting from cognitive and neurological deficits, as well as psychological disorders. Only recently, preclinical research on electrical stimulation methods as a potential treatment of TBI sequelae has gained more traction. However, the underlying mechanisms of the anticipated improvements induced by these methods are still not fully understood. It remains unclear in which stage after TBI they are best applied to optimize the therapeutic outcome, preferably with persisting effects. Studies with animal models address these questions and investigate beneficial long- and short-term changes mediated by these novel modalities.

METHODS

In this review, we present the state-of-the-art in preclinical research on electrical stimulation methods used to treat TBI sequelae. We analyze publications on the most commonly used electrical stimulation methods, namely transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS) and vagus nerve stimulation (VNS), that aim to treat disabilities caused by TBI. We discuss applied stimulation parameters, such as the amplitude, frequency, and length of stimulation, as well as stimulation time frames, specifically the onset of stimulation, how often stimulation sessions were repeated and the total length of the treatment. These parameters are then analyzed in the context of injury severity, the disability under investigation and the stimulated location, and the resulting therapeutic effects are compared. We provide a comprehensive and critical review and discuss directions for future research. RESULTS AND CONCLUSION: We find that the parameters used in studies on each of these stimulation methods vary widely, making it difficult to draw direct comparisons between stimulation protocols and therapeutic outcome. Persisting beneficial effects and adverse consequences of electrical simulation are rarely investigated, leaving many questions about their suitability for clinical applications. Nevertheless, we conclude that the stimulation methods discussed here show promising results that could be further supported by additional research in this field.

Local 5-HT signaling bi-directionally regulates the coincidence time window for associative learning

The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance

Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter P, which controls STDP, on the characteristic rewiring frequency parameter F, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors. (i) Decreasing P (which enhances the weakening effect of STDP on synaptic weights) and decreasing F (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter τ_{c} has some appropriate values. (ii) Increasing the synaptic time delay τ_{c} induces multiple CR (MCR)-the occurrence of multiple peaks in the degree of coherence as τ_{c} changes-in small-world and random networks, with MCR becoming more pronounced at smaller values of P and F. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.