Dendritic excitability and synaptic plasticity

The plasticity of pyramidal neurons in the behaving brain

Neurons are plastic. That is, they change their activity according to different behavioural conditions. This endows pyramidal neurons with an incredible computational power for the integration and processing of synaptic inputs. Plasticity can be investigated at different levels of investigation within a single neuron, from spines to dendrites, to synaptic input. Although most of our knowledge stems from the in vitro brain slice preparation, plasticity plays a vital role during behaviour by providing a flexible substrate for the execution of appropriate actions in our ever-changing environment. Owing to advances in recording techniques, the plasticity of neurons and the neural networks in which they are embedded is now beginning to be realized in the in vivo intact brain. This review focuses on the structural and functional synaptic plasticity of pyramidal neurons, with a specific focus on the latest developments from in vivo studies. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Engram mechanisms of memory linking and identity

Memories are thought to be stored in neuronal ensembles referred to as engrams. Studies have suggested that when two memories occur in quick succession, a proportion of their engrams overlap and the memories become linked (in a process known as prospective linking) while maintaining their individual identities. In this Review, we summarize the key principles of memory linking through engram overlap, as revealed by experimental and modelling studies. We describe evidence of the involvement of synaptic memory substrates, spine clustering and non-linear neuronal capacities in prospective linking, and suggest a dynamic somato-synaptic model, in which memories are shared between neurons yet remain separable through distinct dendritic and synaptic allocation patterns. We also bring into focus retrospective linking, in which memories become associated after encoding via offline reactivation, and discuss key temporal and mechanistic differences between prospective and retrospective linking, as well as the potential differences in their cognitive outcomes.

Bursts from the past: Intrinsic properties link a network model to zebra finch song

Neuronal intrinsic excitability is a mechanism implicated in learning and memory that is distinct from synaptic plasticity. Prior work in songbirds established that intrinsic properties (IPs) of premotor basal-ganglia-projecting neurons (HVC X ) relate to learned song. Here we find that temporal song structure is related to specific HVC X IPs: HVC X from birds who sang longer songs including longer invariant vocalizations (harmonic stacks) had IPs that reflected increased post-inhibitory rebound. This suggests a rebound excitation mechanism underlying the ability of HVC X neurons to integrate over long periods of time and represent sequence information. To explore this, we constructed a network model of realistic neurons showing how in-vivo HVC bursting properties link rebound excitation to network structure and behavior. These results demonstrate an explicit link between neuronal IPs and learned behavior. We propose that sequential behaviors exhibiting temporal regularity require IPs to be included in realistic network-level descriptions.

Ca 2+ -dependent phosphodiesterase 1 regulates the plasticity of striatal spiny projection neuron glutamatergic synapses

Long-term synaptic plasticity at glutamatergic synapses on striatal spiny projection neurons (SPNs) is central to learning goal-directed behaviors and habits. Although considerable attention has been paid to the mechanisms underlying synaptic strengthening and new learning, little scrutiny has been given to those involved in the attenuation of synaptic strength that attends suppression of a previously learned association. Our studies revealed a novel, non-Hebbian, long-term, postsynaptic depression of glutamatergic SPN synapses induced by interneuronal nitric oxide (NO) signaling (NO-LTD) that was preferentially engaged at quiescent synapses. This form of plasticity was gated by local Ca 2+ influx through CaV1.3 Ca 2+ channels and stimulation of phosphodiesterase 1 (PDE1), which degraded cyclic guanosine monophosphate (cGMP) and blunted NO signaling. Consistent with this model, mice harboring a gain-of-function mutation in the gene coding for the pore-forming subunit of CaV1.3 channels had elevated depolarization-induced dendritic Ca 2+ entry and impaired NO-LTD. Extracellular uncaging of glutamate and intracellular uncaging of cGMP suggested that this Ca 2+ -dependent regulation of PDE1 activity allowed for local regulation of dendritic NO signaling. This inference was supported by simulation of SPN dendritic integration, which revealed that dendritic spikes engaged PDE1 in a branch-specific manner. In a mouse model of Parkinson's disease (PD), NO-LTD was absent not because of a postsynaptic deficit in NO signaling machinery, but rather due to impaired interneuronal NO release. Re-balancing intrastriatal neuromodulatory signaling in the PD model restored NO release and NO-LTD. Taken together, these studies provide novel insights into the mechanisms governing NO-LTD in SPN and its role in psychomotor disorders, like PD.

Distinct GABAergic modulation of timing-dependent LTP in CA1 pyramidal neurons along the longitudinal axis of the mouse hippocampus

Synaptic plasticity in the hippocampus underlies episodic memory formation, with dorsal hippocampus being instrumental for spatial memory whereas ventral hippocampus is crucial for emotional learning. Here, we studied how GABAergic inhibition regulates physiologically relevant low repeat spike timing-dependent LTP (t-LTP) at Schaffer collateral-CA1 synapses along the dorsoventral hippocampal axis. We used two t-LTP protocols relying on only 6 repeats of paired spike-firing in pre- and postsynaptic cells within 10 s that differ in postsynaptic firing patterns. GABAA receptor mechanisms played a greater role in blocking 6× 1:1 t-LTP that recruits single postsynaptic action potentials. 6× 1:4 t-LTP that depends on postsynaptic burst-firing unexpectedly required intact GABAB receptor signaling. The magnitude of both t-LTP-forms decreased along the dorsoventral axis, despite increasing excitability and basal synaptic strength in this direction. This suggests that GABAergic inhibition contributes to the distinct roles of dorsal and ventral hippocampus in memory formation.

Tauroursodeoxycholic acid: a bile acid that may be used for the prevention and treatment of Alzheimer's disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disease that has become one of the main factors affecting human health. It has serious impacts on individuals, families, and society. With the development of population aging, the incidence of AD will further increase worldwide. Emerging evidence suggests that many physiological metabolic processes, such as lipid metabolism, are implicated in the pathogenesis of AD. Bile acids, as the main undertakers of lipid metabolism, play an important role in the occurrence and development of Alzheimer's disease. Tauroursodeoxycholic acid, an endogenous bile acid, has been proven to possess therapeutic effects in different neurodegenerative diseases, including Alzheimer's disease. This review tries to find the relationship between bile acid metabolism and AD, as well as explore the therapeutic potential of bile acid taurocursodeoxycholic acid for this disease. The potential mechanisms of taurocursodeoxycholic acid may include reducing the deposition of Amyloid-β protein, regulating apoptotic pathways, preventing tau hyperphosphorylation and aggregation, protecting neuronal synapses, exhibiting anti-inflammatory properties, and improving metabolic disorders. The objective of this study is to shed light on the use of tauroursodeoxycholic acid preparations in the prevention and treatment of AD, with the aim of identifying effective treatment targets and clarifying various treatment mechanisms involved in this disease.

Heterosynaptic plasticity-induced modulation of synapses

Plasticity is a common feature of synapses that is stated in different ways and occurs through several mechanisms. The regular action of the brain needs to be balanced in several neuronal and synaptic features, one of which is synaptic plasticity. The different homeostatic processes, including the balance between excitation/inhibition or homeostasis of synaptic weights at the single-neuron level, may obtain this. Homosynaptic Hebbian-type plasticity causes associative alterations of synapses. Both homosynaptic and heterosynaptic plasticity characterize the corresponding aspects of adjustable synapses, and both are essential for the regular action of neural systems and their plastic synapses.In this review, we will compare homo- and heterosynaptic plasticity and the main factors affecting the direction of plastic changes. This review paper will also discuss the diverse functions of the different kinds of heterosynaptic plasticity and their properties. We argue that a complementary system of heterosynaptic plasticity demonstrates an essential cellular constituent for homeostatic modulation of synaptic weights and neuronal activity.

BK channels sustain neuronal Ca2+ oscillations to support hippocampal long-term potentiation and memory formation

Mutations of large conductance Ca2+- and voltage-activated K+ channels (BK) are associated with cognitive impairment. Here we report that CA1 pyramidal neuron-specific conditional BK knock-out (cKO) mice display normal locomotor and anxiety behavior. They do, however, exhibit impaired memory acquisition and retrieval in the Morris Water Maze (MWM) when compared to littermate controls (CTRL). In line with cognitive impairment in vivo, electrical and chemical long-term potentiation (LTP) in cKO brain slices were impaired in vitro. We further used a genetically encoded fluorescent K+ biosensor and a Ca2+-sensitive probe to observe cultured hippocampal neurons during chemical LTP (cLTP) induction. cLTP massively reduced intracellular K+ concentration ([K+]i) while elevating L-Type Ca2+ channel- and NMDA receptor-dependent Ca2+ oscillation frequencies. Both, [K+]i decrease and Ca2+ oscillation frequency increase were absent after pharmacological BK inhibition or in cells lacking BK. Our data suggest that L-Type- and NMDAR-dependent BK-mediated K+ outflow significantly contributes to hippocampal LTP, as well as learning and memory.

Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition

Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses and neurons will facilitate integration density and computility. Two-dimensional (2D) materials exhibit potential for artificial synapses, including diverse biomimetic plasticity and efficient computing. Considering the complexity of neuron circuits and the maturity of complementary metal-oxide-semiconductor (CMOS), hybrid integration is attractive. Here, we demonstrate a hybrid neuromorphic hardware with 2D MoS2 synaptic arrays and CMOS neural circuitry integrated on board. With the joint benefit of hybrid integration, frequency coding and feature extraction, a total cost of twelve MoS2 synapses, three CMOS neurons, combined with digital/analogue converter enables alphabetic and numeric recognition. MoS2 synapses exhibit progressively tunable weight plasticity, CMOS neurons integrate and fire frequency-encoded spikes to display the target characters. The synapse- and neuron-saving hybrid hardware exhibits a competitive accuracy of 98.8% and single recognition energy consumption of 11.4 μW. This work provides a viable solution for building neuromorphic hardware with high compactness and computility.

Cellular rules underlying psychedelic control of prefrontal pyramidal neurons

Classical psychedelic drugs are thought to increase excitability of pyramidal cells in prefrontal cortex via activation of serotonin 2A receptors (5-HT2ARs). Here, we instead find that multiple classes of psychedelics dose-dependently suppress intrinsic excitability of pyramidal neurons, and that extracellular delivery of psychedelics decreases excitability significantly more than intracellular delivery. A previously unknown mechanism underlies this psychedelic drug action: enhancement of ubiquitously expressed potassium "M-current" channels that is independent of 5-HT2R activation. Using machine-learning-based data assimilation models, we show that M-current activation interacts with previously described mechanisms to dramatically reduce intrinsic excitability and shorten working memory timespan. Thus, psychedelic drugs suppress intrinsic excitability by modulating ion channels that are expressed throughout the brain, potentially triggering homeostatic adjustments that can contribute to widespread therapeutic benefits.

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.

Anti-Hebbian plasticity in the motor cortex promotes defensive freezing

Regional brain activity often decreases from baseline levels in response to external events, but how neurons develop such negative responses is unclear. To study this, we leveraged the negative response that develops in the primary motor cortex (M1) after classical fear learning. We trained mice with a fear conditioning paradigm while imaging their brains with standard two-photon microscopy. This enabled monitoring changes in neuronal responses to the tone with synaptic resolution through learning. We found that M1 layer 5 pyramidal neurons (L5 PNs) developed negative tone responses within an hour after conditioning, which depended on the weakening of their dendritic spines that were active during training. Blocking this form of anti-Hebbian plasticity using an optogenetic manipulation of CaMKII activity disrupted negative tone responses and freezing. Therefore, reducing the strength of spines active at the time of memory encoding leads to negative responses of L5 PNs. In turn, these negative responses curb M1's capacity for promoting movement, thereby aiding freezing. Collectively, this work provides a mechanistic understanding of how area-specific negative responses to behaviorally relevant cues can be achieved.

The impact of Hodgkin-Huxley models on dendritic research

For the past seven decades, the Hodgkin-Huxley (HH) formalism has been an invaluable tool in the arsenal of neuroscientists, allowing for robust and reproducible modelling of ionic conductances and the electrophysiological phenomena they underlie. Despite its apparent age, its role as a cornerstone of computational neuroscience has not waned. The discovery of dendritic regenerative events mediated by ionic and synaptic conductances has solidified the importance of HH-based models further, yielding new predictions concerning dendritic integration, synaptic plasticity and neuronal computation. These predictions are often validated through in vivo and in vitro experiments, advancing our understanding of the neuron as a biological system and emphasizing the importance of HH-based detailed computational models as an instrument of dendritic research. In this article, we discuss recent studies in which the HH formalism is used to shed new light on dendritic function and its role in neuronal phenomena.

Umbelliferone Ameliorates Memory Impairment and Enhances Hippocampal Synaptic Plasticity in Scopolamine-Induced Rat Model

Alzheimer's disease (AD) is a neurodegenerative disorder, characterized by memory loss and cognitive decline. Among the suggested pathogenic mechanisms of AD, the cholinergic hypothesis proposes that AD symptoms are a result of reduced synthesis of acetylcholine (ACh). A non-selective antagonist of the muscarinic ACh receptor, scopolamine (SCOP) induced cognitive impairment in rodents. Umbelliferone (UMB) is a Apiaceae-family-derived 7-hydeoxycoumarin known for its antioxidant, anti-tumor, anticancer, anti-inflammatory, antibacterial, antimicrobial, and antidiabetic properties. However, the effects of UMB on the electrophysiological and ultrastructure morphological aspects of learning and memory are still not well-established. Thus, we investigated the effect of UMB treatment on cognitive behaviors and used organotypic hippocampal slice cultures for long-term potentiation (LTP) and the hippocampal synaptic ultrastructure. A hippocampal tissue analysis revealed that UMB attenuated a SCOP-induced blockade of field excitatory post-synaptic potential (fEPSP) activity and ameliorated the impairment of LTP by the NMDA and AMPA receptor antagonists. UMB also enhanced the hippocampal synaptic vesicle density on the synaptic ultrastructure. Furthermore, behavioral tests on male SD rats (7-8 weeks old) using the Y-maze test, passive avoidance test (PA), and Morris water maze test (MWM) showed that UMB recovered learning and memory deficits by SCOP. These cognitive improvements were in association with the enhanced expression of BDNF, TrkB, and the pCREB/CREB ratio and the suppression of acetylcholinesterase activity. The current findings indicate that UMB may be an effective neuroprotective reagent applicable for improving learning and memory against AD.

Cortical interneurons: fit for function and fit to function? Evidence from development and evolution

Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labor, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalyzed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Infralimbic YTHDF1 is necessary for the beneficial effects of acute mild exercise on auditory fear extinction retention

Exposure therapy is the most effective approach of behavioral therapy for anxiety and post-traumatic stress disorder (PTSD). But fear is easy to reappear even after successful extinction. So, identifying novel strategies for augmenting exposure therapy is rather important. It was reported that exercise had beneficial effects on cognitive and memory deficits. However, whether exercise could affect fear memory, especially for fear extinction remained elusive. Here, our results showed that exposure to acute mild exercise 1 or 2 h before extinction training can augment recent fear extinction retention and 2 h for the remote fear extinction retention. These beneficial effects could be attributed to increased YTHDF1 expression in medial prefrontal cortex (mPFC). Furthermore, by using an AAV-shRNA-based approach to silence YTHDF1 expression via stereotactic injection in prelimbic cortex (PL) or infralimbic cortex (IL), respectively, we demonstrated that silence YTHDF1 in IL, but not in PL, blunted augmentation of exposure therapy induced by acute mild exercise and accompanied with decreased NR2B and GluR1 expression. Moreover, YTHDF1 modulated dendritic spines remodeling of pyramidal neuron in IL. Collectively, our findings suggested that acute mild exercise acted as an effective strategy in augmenting exposure therapy with possible implications for understanding new treatment underlying PTSD.

Modulation of neuronal excitability by binge alcohol drinking

Drug use poses a serious threat to health systems throughout the world. The number of consumers rises every year being alcohol the drug of abuse most consumed causing 3 million deaths (5.3% of all deaths) worldwide and 132.6 million disability-adjusted life years. In this review, we present an up-to-date summary about what is known regarding the global impact of binge alcohol drinking on brains and how it affects the development of cognitive functions, as well as the various preclinical models used to probe its effects on the neurobiology of the brain. This will be followed by a detailed report on the state of our current knowledge of the molecular and cellular mechanisms underlying the effects of binge drinking on neuronal excitability and synaptic plasticity, with an emphasis on brain regions of the meso-cortico limbic neurocircuitry.

The plasticitome of cortical interneurons

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.

Heterogeneous recurrent spiking neural network for spatio-temporal classification

Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). We observed an accuracy of 94.32% for the KTH dataset, 79.58% and 77.53% for the UCF11 and UCF101 datasets, respectively, and an accuracy of 96.54% on the event-based DVS Gesture dataset using the novel unsupervised HRSNN model. The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.

Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems

Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.

Electrophysiology of Dendritic Spines: Information Processing, Dynamic Compartmentalization, and Synaptic Plasticity

For many years, synaptic transmission was considered as information transfer between presynaptic neuron and postsynaptic cell. At the synaptic level, it was thought that dendritic arbors were only receiving and integrating all information flow sent along to the soma, while axons were primarily responsible for point-to-point information transfer. However, it is important to highlight that dendritic spines play a crucial role as postsynaptic components in central nervous system (CNS) synapses, not only integrating and filtering signals to the soma but also facilitating diverse connections with axons from many different sources. The majority of excitatory connections from presynaptic axonal terminals occurs on postsynaptic spines, although a subset of GABAergic synapses also targets spine heads. Several studies have shown the vast heterogeneous morphological, biochemical, and functional features of dendritic spines related to synaptic processing. In this chapter (adding to the relevant data on the biophysics of spines described in Chap. 1 of this book), we address the up-to-date functional dendritic characteristics assessed through electrophysiological approaches, including backpropagating action potentials (bAPs) and synaptic potentials mediated in dendritic and spine compartmentalization, as well as describing the temporal and spatial dynamics of glutamate receptors in the spines related to synaptic plasticity.

Synergies between synaptic and HCN channel plasticity dictates firing rate homeostasis and mutual information transfer in hippocampal model neuron

Homeostasis is a precondition for any physiological system of any living organism. Nonetheless, models of learning and memory that are based on processes of synaptic plasticity are unstable by nature according to Hebbian rules, and it is not fully clear how homeostasis is maintained during these processes. This is where theoretical and computational frameworks can help in gaining a deeper understanding of the various cellular processes that enable homeostasis in the face of plasticity. A previous simplistic single compartmental model with a single synapse showed that maintaining input/output response homeostasis and stable synaptic learning could be enabled by introducing a linear relationship between synaptic plasticity and HCN conductance plasticity. In this study, we aimed to examine whether this approach could be extended to a more morphologically realistic model that entails multiple synapses and gradients of various VGICs. In doing so, we found that a linear relationship between synaptic plasticity and HCN conductance plasticity was able to maintain input/output response homeostasis in our morphologically realistic model, where the slope of the linear relationship was dependent on baseline HCN conductance and synaptic permeability values. An increase in either baseline HCN conductance or synaptic permeability value led to a decrease in the slope of the linear relationship. We further show that in striking contrast to the single compartment model, here linear relationship was insufficient in maintaining stable synaptic learning despite maintaining input/output response homeostasis. Additionally, we showed that homeostasis of input/output response profiles was at the expense of decreasing the mutual information transfer due to the increase in noise entropy, which could not be fully rescued by optimizing the linear relationship between synaptic and HCN conductance plasticity. Finally, we generated a place cell model based on theta oscillations and show that synaptic plasticity disrupts place cell activity. Whereas synaptic plasticity accompanied by HCN conductance plasticity through linear relationship maintains the stability of place cell activity. Our study establishes potential differences between a single compartmental model and a morphologically realistic model.

Morphological Features of Human Dendritic Spines

Dendritic spine features in human neurons follow the up-to-date knowledge presented in the previous chapters of this book. Human dendrites are notable for their heterogeneity in branching patterns and spatial distribution. These data relate to circuits and specialized functions. Spines enhance neuronal connectivity, modulate and integrate synaptic inputs, and provide additional plastic functions to microcircuits and large-scale networks. Spines present a continuum of shapes and sizes, whose number and distribution along the dendritic length are diverse in neurons and different areas. Indeed, human neurons vary from aspiny or "relatively aspiny" cells to neurons covered with a high density of intermingled pleomorphic spines on very long dendrites. In this chapter, we discuss the phylogenetic and ontogenetic development of human spines and describe the heterogeneous features of human spiny neurons along the spinal cord, brainstem, cerebellum, thalamus, basal ganglia, amygdala, hippocampal regions, and neocortical areas. Three-dimensional reconstructions of Golgi-impregnated dendritic spines and data from fluorescence microscopy are reviewed with ultrastructural findings to address the complex possibilities for synaptic processing and integration in humans. Pathological changes are also presented, for example, in Alzheimer's disease and schizophrenia. Basic morphological data can be linked to current techniques, and perspectives in this research field include the characterization of spines in human neurons with specific transcriptome features, molecular classification of cellular diversity, and electrophysiological identification of coexisting subpopulations of cells. These data would enlighten how cellular attributes determine neuron type-specific connectivity and brain wiring for our diverse aptitudes and behavior.

High synaptic threshold for dendritic NMDA spike generation in human layer 2/3 pyramidal neurons

Neurons receive synaptic input primarily onto their dendrites. While we know much about the electrical properties of dendrites in rodents, we have only just started to describe their properties in the human brain. Here, we investigate the capacity of human dendrites to generate NMDA-receptor-dependent spikes (NMDA spikes). Using dendritic glutamate iontophoresis, as well as local dendritic synaptic stimulation, we find that human layer 2/3 pyramidal neurons can generate dendritic NMDA spikes. The capacity to evoke NMDA spikes in human neurons, however, was significantly reduced compared with that in rodents. Simulations in morphologically realistic and simplified models indicated that human neurons have a higher synaptic threshold for NMDA spike generation primarily due to the wider diameter of their dendrites. In summary, we find reduced NMDA spike generation in human compared with rodent layer 2/3 pyramidal neurons and provide evidence that this is due to the wider diameter of human dendrites.

Stress-induced reduction of Na+/H+ exchanger isoform 1 promotes maladaptation of neuroplasticity and exacerbates depressive behaviors

Major depression disorder (MDD) is a neuropsychiatric disorder characterized by abnormal neuronal activity in specific brain regions. A factor that is crucial in maintaining normal neuronal functioning is intracellular pH (pHi) homeostasis. In this study, we show that chronic stress, which induces depression-like behaviors in animal models, down-regulates the expression of the hippocampal Na⁺/H⁺ exchanger isoform 1, NHE1, a major determinant of pHi in neurons. Knockdown of NHE1 in CA1 hippocampal pyramidal neurons leads to intracellular acidification, promotes dendritic spine loss, lowers excitatory synaptic transmission, and enhances the susceptibility to stress exposure in rats. Moreover, E3 ubiquitin ligase cullin4A may promote ubiquitination and degradation of NHE1 to induce these effects of an unbalanced pHi on synaptic processes. Electrophysiological data further suggest that the abnormal excitability of hippocampal neurons caused by maladaptation of neuroplasticity may be involved in the pathogenesis of this disease. These findings elucidate a mechanism for pHi homeostasis alteration as related to MDD.

Synaptic Plasticity Is Predicted by Spatiotemporal Firing Rate Patterns and Robust to In Vivo-like Variability

Synaptic plasticity, the experience-induced change in connections between neurons, underlies learning and memory in the brain. Most of our understanding of synaptic plasticity derives from in vitro experiments with precisely repeated stimulus patterns; however, neurons exhibit significant variability in vivo during repeated experiences. Further, the spatial pattern of synaptic inputs to the dendritic tree influences synaptic plasticity, yet is not considered in most synaptic plasticity rules. Here, we investigate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a plasticity rule based on calcium dynamics. Using in vivo spike train recordings as inputs to different size clusters of spines, we show that plasticity is strongly robust to trial-to-trial variability of spike timing. In addition, we derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity. Synapses that strongly potentiated have greater firing rates and calcium concentration later in the trial, whereas strongly depressing synapses have hiring firing rates early in the trial. The neighboring synaptic activity influences the direction and magnitude of synaptic plasticity, with small clusters of spines producing the greatest increase in synaptic strength. Together, our results reveal that calcium dynamics can unify diverse plasticity rules and reveal how spatiotemporal firing rate patterns control synaptic plasticity.

Milk-Ta2O5 Hybrid Memristors with Crossbar Array Structure for Bio-Organic Neuromorphic Chip Applications

In this study, a high-performance bio-organic memristor with a crossbar array structure using milk as a resistive switching layer (RSL) is proposed. To ensure compatibility with the complementary metal oxide semiconductor process of milk RSL, a high-k Ta₂O₅ layer was deposited as a capping layer; this layer enables high-density, integration-capable, photolithography processes. The fabricated crossbar array memristors contain milk-Ta₂O₅ hybrid membranes, and they exhibit bipolar resistance switching behavior and uniform resistance distribution across hundreds of repeated test cycles. In terms of the artificial synaptic behavior and synaptic weight changes, milk-Ta₂O₅ hybrid crossbar array memristors have a stable analog RESET process, and the memristors are highly responsive to presynaptic stimulation via paired-pulse facilitation excitatory post-synaptic current. Moreover, spike-timing-dependent plasticity and potentiation and depression behaviors, which closely emulate long-term plasticity and modulate synaptic weights, were evaluated. Finally, an artificial neural network was designed and trained to recognize the pattern of the Modified National Institute of Standards and Technology (MNIST) digits to evaluate the capability of the neuromorphic computing system. Consequently, a high recognition rate of over 88% was achieved. Thus, the milk-Ta₂O₅ hybrid crossbar array memristor is a promising electronic platform for in-memory computing systems.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures

Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we report a multiresponsive synapse device that integrates synaptic and optical-sensing functions. The device adopts vertically stacked graphene/h-BN/WSe₂ heterostructures, including an ultrahigh-mobility readout layer, a weight-control layer, and a dual-stimuli-responsive layer. The unique structure endows synapse devices with excellent synaptic plasticity, short response time (3 μs), and excellent optical responsivity (10⁵ A/W). To demonstrate the application in neuromorphic computing, handwritten digit recognition was simulated based on an unsupervised spiking neural network (SNN) with a precision of 90.89%, well comparable with the state-of-the-art results. Furthermore, multiterminal neuromorphic devices are demonstrated to mimic dendritic integration and photoswitching logic. Different from other synaptic devices, the research work validates multifunctional integration in synaptic devices, supporting the potential fusion of sensing and self-learning in neuromorphic networks.

Neural Information Processing and Computations of Two-Input Synapses

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

Immune-Triggered Forms of Plasticity Across Brain Regions

Immune cells play numerous roles in the host defense against the invasion of microorganisms and pathogens, which induces the release of inflammatory mediators (e.g., cytokines and chemokines). In the CNS, microglia is the major resident immune cell. Recent efforts have revealed the diversity of the cell types and the heterogeneity of their functions. The refinement of the synapse structure was a hallmark feature of the microglia, while they are also involved in the myelination and capillary dynamics. Another promising feature is the modulation of the synaptic transmission as synaptic plasticity and the intrinsic excitability of neurons as non-synaptic plasticity. Those modulations of physiological properties of neurons are considered induced by both transient and chronic exposures to inflammatory mediators, which cause behavioral disorders seen in mental illness. It is plausible for astrocytes and pericytes other than microglia and macrophage to induce the immune-triggered plasticity of neurons. However, current understanding has yet achieved to unveil what inflammatory mediators from what immune cells or glia induce a form of plasticity modulating pre-, post-synaptic functions and intrinsic excitability of neurons. It is still unclear what ion channels and intracellular signaling of what types of neurons in which brain regions of the CNS are involved. In this review, we introduce the ubiquitous modulation of the synaptic efficacy and the intrinsic excitability across the brain by immune cells and related inflammatory cytokines with the mechanism for induction. Specifically, we compare neuro-modulation mechanisms by microglia of the intrinsic excitability of cerebellar Purkinje neurons with cerebral pyramidal neurons, stressing the inverted directionality of the plasticity. We also discuss the suppression and augmentation of the extent of plasticity by inflammatory mediators, as the meta-plasticity by immunity. Lastly, we sum up forms of immune-triggered plasticity in the different brain regions with disease relevance. Together, brain immunity influences our cognition, sense, memory, and behavior via immune-triggered plasticity.

Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

Differences between functional properties of pre- or postsynaptically expressed long-term plasticity have not yet been explored in much detail. In this paper, we used minimalist models of STDP with different expression loci, in search of fundamental functional consequences. Biologically, presynaptic expression acts mostly on neurotransmitter release, thereby altering short-term synaptic dynamics, whereas postsynaptic expression affects mainly synaptic gain. We compared models where plasticity was expressed only presynaptically or postsynaptically, or in both ways. We found that postsynaptic plasticity had a bigger impact over response times, while both pre- and postsynaptic plasticity were similarly capable of detecting correlated inputs. A model with biologically tuned expression of plasticity achieved the same outcome over a range of frequencies. Also, postsynaptic spiking frequency was not directly affected by presynaptic plasticity of short-term plasticity alone, however in combination with a postsynaptic component, it helped restrain positive feedback, contributing to activity homeostasis. In conclusion, expression locus may determine affinity for distinct coding schemes while also contributing to keep activity within bounds. Our findings highlight the importance of carefully implementing expression of plasticity in biological modelling, since the locus of expression may affect functional outcomes in simulations.

Anxiety and hippocampal neuronal activity: Relationship and potential mechanisms

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

Dominant role of adult neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in dentate gyrus granule cells

Neurons and synapses manifest pronounced variability in the amount of plasticity induced by identical activity patterns. The mechanisms underlying such plasticity heterogeneity, which have been implicated in context‐specific resource allocation during encoding, have remained unexplored. Here, we employed a systematic physiologically constrained parametric search to identify the cellular mechanisms behind plasticity heterogeneity in dentate gyrus granule cells. We used heterogeneous model populations to ensure that our conclusions were not biased by parametric choices in a single hand‐tuned model. We found that each of intrinsic, synaptic, and structural heterogeneities independently yielded heterogeneities in synaptic plasticity profiles obtained with two different induction protocols. However, among the disparate forms of neural‐circuit heterogeneities, our analyses demonstrated the dominance of neurogenesis‐induced structural heterogeneities in driving plasticity heterogeneity in granule cells. We found that strong relationships between neuronal intrinsic excitability and plasticity emerged only when adult neurogenesis‐induced heterogeneities in neural structure were accounted for. Importantly, our analyses showed that it was not imperative that the manifestation of neural‐circuit heterogeneities must translate to heterogeneities in plasticity profiles. Specifically, despite the expression of heterogeneities in structural, synaptic, and intrinsic neuronal properties, similar plasticity profiles were attainable across all models through synergistic interactions among these heterogeneities. We assessed the parametric combinations required for the manifestation of such degeneracy in the expression of plasticity profiles. We found that immature cells showed physiological plasticity profiles despite receiving afferent inputs with weak synaptic strengths. Thus, the high intrinsic excitability of immature granule cells was sufficient to counterbalance their low excitatory drive in the expression of plasticity profile degeneracy. Together, our analyses demonstrate that disparate forms of neural‐circuit heterogeneities could mechanistically drive plasticity heterogeneity, but also caution against treating neural‐circuit heterogeneities as proxies for plasticity heterogeneity. Our study emphasizes the need for quantitatively characterizing the relationship between neural‐circuit and plasticity heterogeneities across brain regions.

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

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

Intrinsic excitability mechanisms of neuronal ensemble formation

Neuronal ensembles are coactive groups of cortical neurons, found in spontaneous and evoked activity, that can mediate perception and behavior. To understand the mechanisms that lead to the formation of ensembles, we co-activated layer 2/3 pyramidal neurons in brain slices from mouse visual cortex, in animals of both sexes, replicating in vitro an optogenetic protocol to generate ensembles in vivo. Using whole-cell and perforated patch-clamp pair recordings we found that, after optogenetic or electrical stimulation, coactivated neurons increased their correlated activity, a hallmark of ensemble formation. Coactivated neurons showed small biphasic changes in presynaptic plasticity, with an initial depression followed by a potentiation after a recovery period. Optogenetic and electrical stimulation also induced significant increases in frequency and amplitude of spontaneous EPSPs, even after single-cell stimulation. In addition, we observed unexpected strong and persistent increases in neuronal excitability after stimulation, with increases in membrane resistance and reductions in spike threshold. A pharmacological agent that blocks changes in membrane resistance reverted this effect. These significant increases in excitability can explain the observed biphasic synaptic plasticity. We conclude that cell-intrinsic changes in excitability are involved in the formation of neuronal ensembles. We propose an ‘iceberg’ model, by which increased neuronal excitability makes subthreshold connections suprathreshold, enhancing the effect of already existing synapses, and generating a new neuronal ensemble.

In the brain, groups of neurons that are activated together – also known as neuronal ensembles – are the basic units that underpin perception and behavior. Yet, exactly how these coactive circuits are established remains under investigation.

In 1949, Canadian psychologist Donald Hebb proposed that, when brains learn something new, the neurons which are activated together connect to form ensembles, and their connections become stronger each time this specific piece of knowledge is recalled. This idea that ‘neurons that fire together, wire together’ can explain how memories are acquired and recalled, by strengthening their wiring.

However, recent studies have questioned whether strengthening connections is the only mechanism by which neural ensembles can be created. Changes in the excitability of neurons (how easily they are to fire and become activated) may also play a role. In other words, ensembles could emerge because certain neurons become more excitable and fire more readily.

To solve this conundrum, Alejandre-García et al. examined both hypotheses in the same system. Neurons in slices of the mouse visual cortex were stimulated electrically or optically, via a technique that controls neural activity with light. The activity of individual neurons and their connections was then measured with electrodes.

Spontaneous activity among connected neurons increased after stimulation, indicative of the formation of neuronal ensembles. Connected neurons also showed small changes in the strength of their connections, which first decreased and then rebounded after an initial recovery period.

Intriguingly, cells also showed unexpected strong and persistent increases in neuronal excitability after stimulation, such that neurons fired more readily to the same stimulus. In other words, neurons maintained a cellular memory of having been stimulated. The authors conclude that ensembles form because connected neurons become more excitable, which in turn, may strengthen connections of the circuit at a later stage.

These results provide fresh insights about the neural circuits underpinning learning and memory. In time, the findings could also help to understand disorders such as Alzheimer’s disease and schizophrenia, which are characterised by memory impairments and disordered thinking.

Self-Adaptive Multicompartment: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory

Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is instrumental to advanced cognitive intelligence. However, it is hard to endow a simple neuron model with working memory, and to understand the biological mechanisms that have resulted in such a powerful ability at the neuronal level. This article presents a novel self-adaptive multicompartment spiking neuron model, referred to as SAM, for spike-based learning with working memory. SAM integrates four major biological principles including sparse coding, dendritic non-linearity, intrinsic self-adaptive dynamics, and spike-driven learning. We first describe SAM’s design and explore the impacts of critical parameters on its biological dynamics. We then use SAM to build spiking networks to accomplish several different tasks including supervised learning of the MNIST dataset using sequential spatiotemporal encoding, noisy spike pattern classification, sparse coding during pattern classification, spatiotemporal feature detection, meta-learning with working memory applied to a navigation task and the MNIST classification task, and working memory for spatiotemporal learning. Our experimental results highlight the energy efficiency and robustness of SAM in these wide range of challenging tasks. The effects of SAM model variations on its working memory are also explored, hoping to offer insight into the biological mechanisms underlying working memory in the brain. The SAM model is the first attempt to integrate the capabilities of spike-driven learning and working memory in a unified single neuron with multiple timescale dynamics. The competitive performance of SAM could potentially contribute to the development of efficient adaptive neuromorphic computing systems for various applications from robotics to edge computing.

Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning

The hippocampus plays a critical role in memory consolidation, mediated by coordinated network activity during sharp-wave ripple (SWR) events. Despite the link between SWRs and hippocampal plasticity, little is known about how network state affects information processing in dendrites, the primary sites of synaptic input integration and plasticity. Here, we monitored somatic and basal dendritic activity in CA1 pyramidal cells in behaving mice using longitudinal two-photon calcium imaging integrated with simultaneous local field potential recordings. We found immobility was associated with an increase in dendritic activity concentrated during SWRs. Coincident dendritic and somatic activity during SWRs predicted increased coupling during subsequent exploration of a novel environment. In contrast, somatic-dendritic coupling and SWR recruitment varied with cells' tuning distance to reward location during a goal-learning task. Our results connect SWRs with the stabilization of information processing within CA1 neurons and suggest that these mechanisms may be dynamically biased by behavioral demands.

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

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

Striatal synaptic adaptations in Parkinson's disease

The striatum is densely innervated by mesencephalic dopaminergic neurons that modulate acquisition and vigor of goal-directed actions and habits. This innervation is progressively lost in Parkinson's disease (PD), contributing to the defining movement deficits of the disease. Although boosting dopaminergic signaling with levodopa early in the course of the disease alleviates these deficits, later this strategy leads to the emergence of debilitating dyskinesia. Here, recent advances in our understanding of how striatal cells and circuits adapt to this progressive de-innervation and to levodopa therapy are discussed. First, we discuss how dopamine (DA) depletion triggers cell type-specific, homeostatic changes in spiny projection neurons (SPNs) that tend to normalize striatal activity but also lead to disruption of the synaptic architecture sculpted by experience. Second, we discuss the roles played by cholinergic and nitric oxide-releasing interneurons in these adaptations. Third, we examine recent work in freely moving mice suggesting that alterations in the spatiotemporal dynamics of striatal ensembles contributes to PD movement deficits. Lastly, we discuss recently published evidence from a progressive model of PD suggesting that contrary to the classical model, striatal pathway imbalance is necessary but not sufficient to produce frank parkinsonism.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.

Endoplasmic reticulum-mitochondria signaling in neurons and neurodegenerative diseases

Recent advances have revealed common pathological changes in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis with related frontotemporal dementia (ALS/FTD). Many of these changes can be linked to alterations in endoplasmic reticulum (ER)-mitochondria signaling, including dysregulation of Ca2+ signaling, autophagy, lipid metabolism, ATP production, axonal transport, ER stress responses and synaptic dysfunction. ER-mitochondria signaling involves specialized regions of ER, called mitochondria-associated membranes (MAMs). Owing to their role in neurodegenerative processes, MAMs have gained attention as they appear to be associated with all the major neurodegenerative diseases. Furthermore, their specific role within neuronal maintenance is being revealed as mutant genes linked to major neurodegenerative diseases have been associated with damage to these specialized contacts. Several studies have now demonstrated that these specialized contacts regulate neuronal health and synaptic transmission, and that MAMs are damaged in patients with neurodegenerative diseases. This Review will focus on the role of MAMs and ER-mitochondria signaling within neurons and how damage of the ER-mitochondria axis leads to a disruption of vital processes causing eventual neurodegeneration.

Better language through chemistry: Augmenting speech-language therapy with pharmacotherapy in the treatment of aphasia

Speech and language therapy is the standard treatment of aphasia. However, many individuals have barriers in seeking this measure of extensive rehabilitation treatment. Investigating ways to augment therapy is key to improving poststroke language outcomes for all patients with aphasia, and pharmacotherapies provide one such potential solution. Although no medications are currently approved for the treatment of aphasia by the United States Food and Drug Administration, numerous candidate mechanisms for pharmaceutical manipulation continue to be identified based on our evolving understanding of the neurometabolic experience of stroke recovery across molecular, cellular, and functional levels of inquiry. This chapter will review evidence for catecholaminergic, glutamatergic, cholinergic, and serotonergic drug therapies and discuss future directions for both candidate drug selection and pharmacotherapy practice in people with aphasia.

Effect of Pulse Duration and Direction on Plasticity Induced by 5 Hz Repetitive Transcranial Magnetic Stimulation in Correlation With Neuronal Depolarization

Introduction: High frequency repetitive transcranial magnetic stimulation applied to the motor cortex causes an increase in the amplitude of motor evoked potentials (MEPs) that persists after stimulation. Here, we focus on the aftereffects generated by high frequency controllable pulse TMS (cTMS) with different directions, intensities, and pulse durations.

Objectives: To investigate the influence of pulse duration, direction, and amplitude in correlation to induced depolarization on the excitatory plastic aftereffects of 5 Hz repetitive transcranial magnetic stimulation (rTMS) using bidirectional cTMS pulses.

Methods: We stimulated the hand motor cortex with 5 Hz rTMS applying 1,200 bidirectional pulses with the main component durations of 80, 100, and 120 μs using a controllable pulse stimulator TMS (cTMS). Fourteen healthy subjects were investigated in nine sessions with 80% resting motor threshold (RMT) for posterior-anterior (PA) and 80 and 90% RMT anterior-posterior (AP) induced current direction. We used a model approximating neuronal membranes as a linear first order low-pass filter to estimate the strength–duration time constant and to simulate the membrane polarization produced by each waveform.

Results: PA and AP 5 Hz rTMS at 80% RMT produced no significant excitation. An exploratory analysis indicated that 90% RMT AP stimulation with 100 and 120 μs pulses but not 80 μs pulses led to significant excitation. We found a positive correlation between the plastic outcome of each session and the simulated peak neural membrane depolarization for time constants >100 μs. This correlation was strongest for neural elements that are depolarized by the main phase of the AP pulse, suggesting the effects were dependent on pulse direction.

Conclusions: Among the tested conditions, only 5 Hz rTMS with higher intensity and wider pulses appeared to produce excitatory aftereffects. This correlated with the greater depolarization of neural elements with time constants slower than the directly activated neural elements responsible for producing the motor output (e.g., somatic or dendritic membrane).

Significance: Higher intensities and wider pulses seem to be more efficient in inducing excitation. If confirmed, this observation could lead to better results in future clinical studies performed with wider pulses.