Study of pre-synaptic internalisation in human schizophrenia brains

AimsEfficient synaptic communication is crucial to maintain healthy behavioural and cognitive processes. Individuals affected by schizophrenia present behavioural symptoms and alterations in decision-making, suggesting altered synaptic integrity as the support of the illness. It is currently unknown how this synaptic change is mediated in schizophrenia, but microglia have been proposed to act as the culprit, actively removing synapses pathologically. Here, we aimed to explore the interaction between microglia and synaptic uptake in human post-mortem tissue.MethodsWe assessed microglial activation and synaptic internalisation by microglia in a post-mortem human tissue of 10 control and 10 schizophrenia cases. Immunohistochemistry was performed to identify microglia (Iba1 and CD68) and the presynaptic terminals (synapsin I).ResultsWe found no difference in microglial expression, nor a difference in pre-synaptic protein level phagocyted by microglia between the two groups.ConclusionsOur findings are consistent with the brain imaging studies in schizophrenia implying that microglia play a role mainly during the early phases of the disease, by example in active synapse remodelling, which is not detected in the chronic stage of the illness.

Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunnel-junction synapses

Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (SNN) based on spike-timing-dependent plasticity (STDP) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the STDP characteristics and spike types. Here, we report the STDP characteristics implemented in ferroelectric tunnel junctions (FTJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of FTJs into account, we propose equations for simulating the STDP curve parameters of a magnitude of the conductance change (ΔGmax) and a time window (τC) from the spike parameters of a peak amplitude (Vpeak) and time durations (tp and td) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. The power consumption experiments of the STDP revealed that the power consumption under the inactive-synapse condition (spike timing |Δt| > τC) was as large as 50–82% of that under the active-synapse condition (|Δt| < τC). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an SNN implemented by using FTJs as synapses.

A GPU Optimized Technique for Scalable Spiking Neural Network Simulation

Simulation studies, in general, heavily rely upon the internal variables of the system / entity in the studies. In case of simulation study of the Spiking Neural Networks (SNNs), the major internal system variables are membrane potentials of the neurons and their respective synaptic inputs which demand to be updated at a sub-millisecond resolution. It would be very apt here to note that this requires thousands of updates to simulate one second of an activity per neuron and this factor makes it imperative to have a highly scalable model to derive some inferences from the simulation. Conventionally, high performance CPUs with high degree of multi-threading were leveraged to conduct simulations and derive inferences. With the advances in the hardware, the degree of parallelism has also increased, especially the GPUs have opened a multitude of avenues to perform SNN simulations at scale. In our pervious works [1, 2, 3], we have demonstrated how GPUs can be leveraged to achieve scalability and performance by using hybrid CPU-GPU approach which have improved the performance as compared to multi-threading on high performance CPUs. In this work, we have focused on hyper parameter tuning of some of the key parameters such as delay insensitivity, time step grouping and the active synapse grouping to achieve greater simulation speed of scalable spiking neural networks

Spike: A GPU Optimised Spiking Neural Network Simulator

ABSTRACTSpiking Neural Network (SNN) simulations require internal variables – such as the membrane voltages of individual neurons and their synaptic inputs – to be updated on a sub-millisecond resolution. As a result, a single second of simulation time requires many thousands of update calculations per neuron. Furthermore, increases in the scale of SNN models have, accordingly, led to manyfold increases in the runtime of SNN simulations. Existing solutions to this problem of scale include high performance CPU based simulators capable of multithreaded execution (“CPU parallelism”). More recent GPU based simulators have emerged, which aim to utilise GPU parallelism for SNN execution. We have identified several key speedups, which give GPU based simulators up to an order of magnitude performance increase over CPU based simulators on several benchmarks. We present the Spike simulator with three key optimisations: timestep grouping, active synapse grouping, and delay insensitivity. Combined, these optimisations massively increase the speed of executing a SNN simulation and produce a simulator which is, on a single machine, faster than currently available simulators.