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The hardware‐level construction of artificial synapses, mimicking the functionality of biological synapses, is widely acknowledged as a pivotal stride in artificial intelligence systems. Particularly, establishing all‐optical artificial synapses for neuromorphic computing, leveraging on‐chip optical interconnects instead of conventional wired connections, has garnered widespread attention. Here, high‐quality CsPbBr3 single crystals are synthesized, and intriguing relaxation processes associated with reversible photo‐induced phase transition (PIPT) are observed, leading to a corresponding change in the birefringence effect. The birefringence change is systematically analyzed by an optical system. The relaxation time of reversible PIPT is similar to the behavior patterns of biological synapses, enabling the device to mimic synaptic features. The device successfully mimics excitatory/inhibitory synaptic behaviors such as EPSP/IPSP, PPF, SDDP, SFDP, and SIDP by using 1310 nm signal. Furthermore, the device continues to exhibit excitatory/inhibitory synaptic functionality when 940 and 1550 nm signals are applied, achieving multi‐wavelength synaptic behaviors. The all‐optical devices have the advantages of low crosstalk, fast signaling, and wide optical bandwidth, which lays the foundation of all‐optical hardware for neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

The brain contains thousands of millions of synapses, exhibiting diverse structural, molecular, and functional characteristics. However, synapses can be classified into two primary morphological types: Gray's type I and type II, corresponding to Colonnier's asymmetric (AS) and symmetric (SS) synapses, respectively. AS and SS have a thick and thin postsynaptic density, respectively. In the cerebral cortex, since most AS are excitatory (glutamatergic), and SS are inhibitory (GABAergic), determining the distribution, size, density, and proportion of the two major cortical types of synapses is critical, not only to better understand synaptic organization in terms of connectivity, but also from a functional perspective. However, several technical challenges complicate the study of synapses. Potassium ferrocyanide has been utilized in recent volume electron microscope studies to enhance electron density in cellular membranes. However, identifying synaptic junctions, especially SS, becomes more challenging as the postsynaptic densities become thinner with increasing concentrations of potassium ferrocyanide. Here we describe a protocol employing Focused Ion Beam Milling and Scanning Electron Microscopy for studying brain tissue. The focus is on the unequivocal identification of AS and SS types. To validate SS observed using this protocol as GABAergic, experiments with immunocytochemistry for the vesicular GABA transporter were conducted on fixed mouse brain tissue sections. This material was processed with different concentrations of potassium ferrocyanide, aiming to determine its optimal concentration. We demonstrate that using a low concentration of potassium ferrocyanide (0.1%) improves membrane visualization while allowing unequivocal identification of synapses as AS or SS. [ABSTRACT FROM AUTHOR]

The brain contains thousands of millions of synapses, exhibiting diverse structural, molecular, and functional characteristics. However, synapses can be classified into two primary morphological types: Gray’s type I and type II, corresponding to Colonnier’s asymmetric (AS) and symmetric (SS) synapses, respectively. AS and SS have a thick and thin postsynaptic density, respectively. In the cerebral cortex, since most AS are excitatory (glutamatergic), and SS are inhibitory (GABAergic), determining the distribution, size, density, and proportion of the two major cortical types of synapses is critical, not only to better understand synaptic organization in terms of connectivity, but also from a functional perspective. However, several technical challenges complicate the study of synapses. Potassium ferrocyanide has been utilized in recent volume electron microscope studies to enhance electron density in cellular membranes. However, identifying synaptic junctions, especially SS, becomes more challenging as the postsynaptic densities become thinner with increasing concentrations of potassium ferrocyanide. Here we describe a protocol employing Focused Ion Beam Milling and Scanning Electron Microscopy for studying brain tissue. The focus is on the unequivocal identification of AS and SS types. To validate SS observed using this protocol as GABAergic, experiments with immunocytochemistry for the vesicular GABA transporter were conducted on fixed mouse brain tissue sections. This material was processed with different concentrations of potassium ferrocyanide, aiming to determine its optimal concentration. We demonstrate that using a low concentration of potassium ferrocyanide (0.1%) improves membrane visualization while allowing unequivocal identification of synapses as AS or SS.

Traditional computation based on von Neumann architecture is limited by time and energy consumption due to data transfer between the storage and the processing units. The von Neumann architecture is also inefficient in solving unstructured, probabilistic, and real‐time problems. To address these challenges, a new brain‐inspired neuromorphic computational architecture is required. Due to the absence of resistance–capacitance delay, high bandwidth, and low power consumption, optoelectronic artificial synaptic devices are highly attractive. Yet, stable, scalable, and complementary metal–oxide–semiconductor (CMOS)‐compatible materials exhibiting both inhibitory and excitatory optoelectronic synaptic functionalities have not been demonstrated. Here, epitaxial CMOS‐compatible scandium nitride (ScN) optoelectronic artificial synaptic devices that emulate both inhibitory and excitatory biological synaptic activities are presented. The negative and positive persistent photoconductivity of undoped and magnesium‐doped ScN is equated to the inhibitory and excitatory synaptic plasticity, respectively, which leads to functionalities like learning–forgetting, frequency‐selective optical filtering, frequency‐dependent potentiation and depression, Hebbian learning, and logic‐gate operations. Temperature‐dependent photoresponse and photo‐Hall measurements reveal that scattering of photogenerated carriers from charged defect centers results in negative photoconductivity in undoped degenerate ScN. This work opens up the possibility of utilizing a group‐III epitaxial semiconducting nitride material with inhibitory and excitatory optoelectronic synaptic functionalities for practical neuromorphic applications. [ABSTRACT FROM AUTHOR]

Abstract Traditional computation based on von Neumann architecture is limited by time and energy consumption due to data transfer between the storage and the processing units. The von Neumann architecture is also inefficient in solving unstructured, probabilistic, and real‐time problems. To address these challenges, a new brain‐inspired neuromorphic computational architecture is required. Due to the absence of resistance–capacitance delay, high bandwidth, and low power consumption, optoelectronic artificial synaptic devices are highly attractive. Yet, stable, scalable, and complementary metal–oxide–semiconductor (CMOS)‐compatible materials exhibiting both inhibitory and excitatory optoelectronic synaptic functionalities have not been demonstrated. Here, epitaxial CMOS‐compatible scandium nitride (ScN) optoelectronic artificial synaptic devices that emulate both inhibitory and excitatory biological synaptic activities are presented. The negative and positive persistent photoconductivity of undoped and magnesium‐doped ScN is equated to the inhibitory and excitatory synaptic plasticity, respectively, which leads to functionalities like learning–forgetting, frequency‐selective optical filtering, frequency‐dependent potentiation and depression, Hebbian learning, and logic‐gate operations. Temperature‐dependent photoresponse and photo‐Hall measurements reveal that scattering of photogenerated carriers from charged defect centers results in negative photoconductivity in undoped degenerate ScN. This work opens up the possibility of utilizing a group‐III epitaxial semiconducting nitride material with inhibitory and excitatory optoelectronic synaptic functionalities for practical neuromorphic applications.

Background: Neural circuits are subjected to refinement throughout life. The dynamic addition and elimination (pruning) of synapses are necessary for maturation of neural circuits and synaptic plasticity. Due to their phagocytic nature, microglia have been considered as the primary mediators of synaptic pruning. Synaptic pruning can strengthen an active synapse by removing excess weaker synapses during development. Inappropriate synaptic pruning can often influence a disease outcome or an injury response., Summary: This review offers a focused discussion on microglial roles in synaptic pruning, based on the evidence gathered from genetic manipulations in mice. Genetically labeled microglia and synapses often allow assessment of their interactions in real time. Further manipulations involving synaptically localized molecules, neuronally or glial-derived diffusible factors, and their respective cognate receptors in microglia provide critical evidence in support of a direct role of microglia in synaptic pruning., Key Message: We discuss microglial contact-dependent "eat-me," "don't-eat-me," and "find-me" signals, as well as recently identified noncontact pruning, under the contexts of neural circuit, brain region, developmental window, and an injury or a disease state., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs).Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified.Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present.Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study. [ABSTRACT FROM AUTHOR]

The brain is a complex system consisting of a large number of interacting neurons. Recently, a simple nonlinear biological model has been proposed for the up and down state transitions in the network of excitatory and inhibitory neurons. In this paper, we study the dynamical behavior of this model by calculating the Lyapunov exponents and bifurcation diagrams for various values of synaptic connections. We show that varying the synaptic strength values has a considerable effect on the bifurcations in the model. Furthermore, we show that the model can exhibit chaotic firing for certain values of the excitatory–excitatory synaptic strength. [ABSTRACT FROM AUTHOR]

Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions.

Time-delay-induced synchronous behaviors and synchronization transitions have been widely investigated for coupled neurons, and they play important roles for physiological functions. In the present study, time-delay-induced synchronized subthreshold oscillations were simulated, and the bifurcations underlying the synchronized behaviors were identified for a pair of coupled FitzHugh–Nagumo neurons. Multiple transitions between in-phase and anti-phase synchronizations induced by the time delay were simulated for the inhibitory and excitatory couplings. Subcritical or supercritical Hopf bifurcations and the stability of the Hopf-bifurcating periodic subthreshold oscillations were acquired using center manifold reduction and normal form theory. The in-phase or anti-phase synchronizations of the stable periodic subthreshold oscillations, which appear for multiple values of the time delay, were interpreted with the related eigenspace. The distributions of the different dynamical behaviors, including the synchronizations and bifurcations in the two-parameter plane of the time delay and coupling strength, were acquired for both types of synapses, and the different roles of the inhibitory and excitatory couplings on the synchronization transitions were compared. [ABSTRACT FROM AUTHOR]

To generate rhythmic motor behaviors, both single neurons and neural circuits require a balance between excitatory inputs that trigger action potentials and inhibitory inputs that promote a stable resting potential (E/I balance). Previous studies have focused on individual neurons and have shown that, over a short spatial scale, excitatory and inhibitory (E/I) synapses tend to form structured territories with inhibitory inputs enriched on cell bodies and proximal dendrites and excitatory inputs on distal dendrites. However, systems-level E/I patterns, at spatial scales larger than single neurons, are largely uncharted. We used immunostaining for PSD-95 and gephyrin postsynaptic scaffolding proteins as proxies for excitatory and inhibitory synapses, respectively, to quantify the numbers and map the distributions of E/I synapses in zebrafish spinal cord at both an embryonic stage and a larval stage. At the embryonic stage, we found that PSD-95 puncta outnumber gephyrin puncta, with the number of gephyrin puncta increasing to match that of PSD-95 puncta at the larval stage. At both stages, PSD-95 puncta are enriched in the most lateral neuropil corresponding to distal dendrites while gephyrin puncta are enriched on neuronal somata and in the medial neuropil. Significantly, similar to synaptic puncta, neuronal processes also exhibit medial-lateral territories at both developmental stages with enrichment of glutamatergic (excitatory) processes laterally and glycinergic (inhibitory) processes medially. This establishment of neuropil excitatory-inhibitory structure largely precedes dendritic arborization of primary motor neurons, suggesting that the structured neuropil could provide a framework for the development of E/I balance at the cellular level. J. Comp. Neurol. 525:1649-1667, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The immune system plays important functional roles in regeneration after injury to the mammalian central and peripheral nervous systems. After damage to the peripheral nerve several types of immune cells, invade the nerve within hours after the injury. To gain insights into the contribution of T- and B-lymphocytes to recovery from injury we used the mouse femoral nerve injury paradigm. RAG2 −/− mice lacking mature T- and B-lymphocytes due to deletion of the recombination activating gene 2 were subjected to resection and surgical reconstruction of the femoral nerve, with the wild-type mice of the same inbred genetic background serving as controls. According to single frame motion analyses, RAG2 −/− mice showed better motor recovery in comparison to control mice at four and eight weeks after injury. Retrograde tracing of regrown/sprouted axons of spinal motoneurons showed increased numbers of correctly projecting motoneurons in the lumbar spinal cord of RAG2 −/− mice compared with controls. Whereas there was no difference in the motoneuron soma size between genotypes, RAG2 −/− mice displayed fewer cholinergic and inhibitory synaptic terminals around somata of spinal motoneurons both prior to and after injury, compared with wild-type mice. Extent of myelination of regrown axons in the motor branch of the femoral nerve measured as g-ratio was more extensive in RAG2 −/− than in control mice eight weeks after injury. We conclude that activated T- and B-lymphocytes restrict motor recovery after femoral nerve injury, associated with the increased survival of motoneurons and improved remyelination. [ABSTRACT FROM AUTHOR]

In previous work, we found that the protein messenger Jacob is involved in N-methyl- d-aspartate receptor (NMDAR) signaling to the nucleus and cAMP response element-binding protein (CREB) mediated gene expression in hippocampal primary neurons. Particularly, extrasynaptic NMDAR activation drives Jacob efficiently into the nucleus where it then induces gene expression that promotes neurodegeneration. However, the protein also translocates to the nucleus in CA1 neurons after Schaffer collateral long-term potentiation (LTP) but not long-term depression (LTD), suggesting that Jacob might be involved in hippocampal and LTP-dependent learning and memory processes. Not much is known about the cellular and subcellular distribution of the protein in brain. In this paper, we provide an overview of the expression of Jacob in rat brain with special emphasis on the hippocampus. We show that Jacob is abundant in hippocampal pyramidal neurons and interneurons but absent from astrocytes and microglia. Interestingly, we found that Jacob is also present in mossy fiber axons. Double immunofluorescence confocal laser scans with presynaptic markers demonstrate that Jacob is indeed found at excitatory but not inhibitory presynaptic sites. Accordingly, we found no substantial co-localization of Jacob with a postsynaptic marker of inhibitory synapses, gephyrin. In contrast, almost all postsynaptic density protein 95 (PSD-95) positive excitatory postsynaptic sites also exhibited strong Jacob-immunofluorescence. Taken together, these data support a synaptic and nuclear role of Jacob that implicates long-distance NMDAR signaling to the nucleus in excitatory neurons. [ABSTRACT FROM AUTHOR]

Abstract: An electron microscopic analysis has been carried out on the effects of age on the numerical density of both excitatory (asymmetric) and inhibitory (symmetric) synapses in the neuropil of layers 2/3 and of layer 5 in area 46 from the frontal cortex of behaviorally tested rhesus monkeys. There is no change in the lengths of synaptic junctions with age or in the percentage distribution of synapses relative to the postsynaptic spines and dendritic shafts. However, in layers 2/3 there is an overall loss of about 30% of synapses from 5 to 30 years of age, and both asymmetric and symmetric synapses are lost at the same rate. In layer 5 the situation is different; the overall loss of synapses is only 20% and this is almost entirely due to a loss of asymmetric synapses, since there is no significant loss of symmetric synapses from this layer with age. When the synapse data are correlated with the overall cognitive impairment shown by the monkeys, it is found that there is a strong correlation between the numerical density of asymmetric synapses in layers 2/3 and cognitive impairment, with a weaker correlation between symmetric synapse loss and cognitive impairment. In layer 5 on the other hand there is no correlation between synapse loss and cognitive impairment. However synapse loss is not the only factor causing cognitive impairment, since in previous studies of area 46 we have found that age-related alteration in myelin in this frontal area also significantly contributes to cognitive decline. The synapse loss is also considered in light of earlier studies, which show that the frequency of spontaneous excitatory synaptic responses is reduced with age in layers 2/3 neurons. [Copyright &y& Elsevier]

Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions. [ABSTRACT FROM AUTHOR]

The K-Cl co-transporter KCC2 plays multiple roles in the physiology of central neurons and alterations of its function and/or expression are associated with several neurological conditions. By regulating intraneuronal chloride homeostasis, KCC2 strongly influences the efficacy and polarity of the chloride-permeable γ-aminobutyric acid (GABA) type A and glycine receptor (GlyR) mediated synaptic transmission. This appears particularly critical for the development of neuronal circuits as well as for the dynamic control of GABA and glycine signaling in mature networks. The activity of the transporter is also associated with transmembrane water fluxes which compensate solute fluxes associated with synaptic activity. Finally, KCC2 interaction with the actin cytoskeleton appears critical both for dendritic spine morphogenesis and the maintenance of glutamatergic synapses. In light of the pivotal role of KCC2 in the maturation and function of central synapses, it is of particular importance to understand the cellular and molecular mechanisms underlying its regulation. These include development and activity-dependent modifications both at the transcriptional and post-translational levels. We emphasize the importance of post-translational mechanisms such as phosphorylation and dephosphorylation, oligomerization, cell surface stability, clustering and membrane diffusion for the rapid and dynamic regulation of KCC2 function.