Numerical Simulation: Fluctuation in Background Synaptic Activity Regulates Synaptic Plasticity

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain.

Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS

Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripartite synapse model that includes a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell is presented. In this model, the astrocyte acts as a controller module for neurons and can regulates the spiking activity of them. Simulation results show that by regulating the coupling coefficients of astrocytes, spiking frequency of neurons can be reduced and the activity of neuronal cells is modulated.

Microtubule assembly by soluble tau impairs vesicle endocytosis and excitatory neurotransmission via dynamin sequestration in Alzheimer's disease synapse model

Elevation of soluble wild-type (WT) tau occurs in synaptic compartments in Alzheimer's disease. We addressed whether tau elevation affects synaptic transmission at the calyx of Held in brainstem slices. Whole-cell loading of WT human tau (h-tau) in presynaptic terminals at 10-20 μM caused microtubule (MT) assembly and activity-dependent rundown of excitatory neurotransmission. Capacitance measurements revealed that the primary target of WT h-tau is vesicle endocytosis. Blocking MT assembly using nocodazole prevented tau-induced impairments of endocytosis and neurotransmission. Immunofluorescence imaging analyses revealed that MT assembly by WT h-tau loading was associated with an increased bound fraction of the endocytic protein dynamin. A synthetic dodecapeptide corresponding to dynamin-1-pleckstrin-homology domain inhibited MT-dynamin interaction and rescued tau-induced impairments of endocytosis and neurotransmission. We conclude that elevation of presynaptic WT tau induces de novo assembly of MTs, thereby sequestering free dynamins. As a result, endocytosis and subsequent vesicle replenishment are impaired, causing activity-dependent rundown of neurotransmission.

Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

Microglia as hackers of the matrix: sculpting synapses and the extracellular space

Microglia shape the synaptic environment in health and disease, but synapses do not exist in a vacuum. Instead, pre- and postsynaptic terminals are surrounded by extracellular matrix (ECM), which together with glia comprise the four elements of the contemporary tetrapartite synapse model. While research in this area is still just beginning, accumulating evidence points toward a novel role for microglia in regulating the ECM during normal brain homeostasis, and such processes may, in turn, become dysfunctional in disease. As it relates to synapses, microglia are reported to modify the perisynaptic matrix, which is the diffuse matrix that surrounds dendritic and axonal terminals, as well as perineuronal nets (PNNs), specialized reticular formations of compact ECM that enwrap neuronal subsets and stabilize proximal synapses. The interconnected relationship between synapses and the ECM in which they are embedded suggests that alterations in one structure necessarily affect the dynamics of the other, and microglia may need to sculpt the matrix to modify the synapses within. Here, we provide an overview of the microglial regulation of synapses, perisynaptic matrix, and PNNs, propose candidate mechanisms by which these structures may be modified, and present the implications of such modifications in normal brain homeostasis and in disease.

Effects of Excess Brain-Derived Human α-Synuclein on Synaptic Vesicle Trafficking

α-Synuclein is a presynaptic protein that regulates synaptic vesicle trafficking under physiological conditions. However, in several neurodegenerative diseases, including Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy, α-synuclein accumulates throughout the neuron, including at synapses, leading to altered synaptic function, neurotoxicity, and motor, cognitive, and autonomic dysfunction. Neurons typically contain both monomeric and multimeric forms of α-synuclein, and it is generally accepted that disrupting the balance between them promotes aggregation and neurotoxicity. However, it remains unclear how distinct molecular species of α-synuclein affect synapses where α-synuclein is normally expressed. Using the lamprey reticulospinal synapse model, we previously showed that acute introduction of excess recombinant monomeric or dimeric α-synuclein impaired distinct stages of clathrin-mediated synaptic vesicle endocytosis, leading to a loss of synaptic vesicles. Here, we expand this knowledge by investigating the effects of native, physiological α-synuclein isolated from the brain of a neuropathologically normal human subject, which comprised predominantly helically folded multimeric α-synuclein with a minor component of monomeric α-synuclein. After acute introduction of excess brain-derived human α-synuclein, there was a moderate reduction in the synaptic vesicle cluster and an increase in the number of large, atypical vesicles called “cisternae.” In addition, brain-derived α-synuclein increased synaptic vesicle and cisternae sizes and induced atypical fusion/fission events at the active zone. In contrast to monomeric or dimeric α-synuclein, the brain-derived multimeric α-synuclein did not appear to alter clathrin-mediated synaptic vesicle endocytosis. Taken together, these data suggest that excess brain-derived human α-synuclein impairs intracellular vesicle trafficking and further corroborate the idea that different molecular species of α-synuclein produce distinct trafficking defects at synapses. These findings provide insights into the mechanisms by which excess α-synuclein contributes to synaptic deficits and disease phenotypes.

Graphene-based photonic synapse for multi wavelength neural networks

A synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

ABSTRACTClosed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.