Search

Background: Severe traumatic brain injury (TBI) management begins in the pre-hospital setting, but clinicians are left with limited options for stabilisation during retrieval due to time and space constraints, as well as a lack of access to monitoring equipment. Bolus osmotherapy with hypertonic substances is commonly utilised as a temporising measure for life-threatening brain herniation, but much contention persists around its use, largely stemming from a limited evidence base. Method: The authors conducted a brief review of hypertonic substance use in patients with TBI, with a particular focus on studies involving the pre-hospital and emergency department (ED) settings. We aimed to report pragmatic information useful for clinicians involved in the early management of this patient group. Results: We reviewed the literature around the pharmacology of bolus osmotherapy, commercially available agents, potential pitfalls, supporting evidence and guideline recommendations. We further reviewed what the ideal agent is, when it should be administered, dosing and treatment endpoints and/or whether it confers meaningful long-term outcome benefits. Conclusions: There is a limited evidence-based argument in support of the implementation of bolus osmotherapy in the pre-hospital or ED settings for patients who sustain a TBI. However, decades’ worth of positive clinician experiences with osmotherapy for TBI will likely continue to drive its on-going use. Choices regarding osmotherapy will likely continue to be led by local policies, individual patient characteristics and clinician preferences.

Abstract Efficiently solving combinatorial optimization problems (COPs) such as Max‐Cut is challenging because the resources required increase exponentially with the problem size. This study proposes a hardware‐friendly method for solving the Max‐Cut problem by implementing a spiking neural network (SNN)‐based Boltzmann machine (BM) in neuromorphic hardware systems. To implement the hardware‐oriented version of the spiking Boltzmann machine (sBM), the stochastic dynamics of leaky integrate‐and‐fire (LIF) neurons with random walk noise are analyzed, and an innovative algorithm based on overlapping time windows is proposed. The simulation results demonstrate the effective convergence and high accuracy of the proposed method for large‐scale Max‐Cut problems. The proposed method is validated through successful hardware implementation on a 6‐transistor/2‐resistor (6T2R) neuromorphic chip with phase change memory (PCM) synapses. In addition, as an expansion of the algorithm, several annealing techniques and bias split methods are proposed to improve convergence, along with circuit design ideas for efficient evaluation of sampling convergence using cell arrays and spiking systems. Overall, the results of the proposed methods demonstrate the potential of energy‐efficient and hardware‐implementable approaches using SNNs to solve COPs. To the best of the author's knowledge, this is the first study to solve the Max‐Cut problem using an SNN neuromorphic hardware chip.

A fully silicon‐integrated restricted Boltzmann machine (RBM) with an event‐driven contrastive divergence (eCD) training algorithm is implemented using novel stochastic leaky integrate‐and‐fire (LIF) neuron circuits and six‐transistor/2‐PCM‐resistor (6T2R) synaptic unit cells on 90 nm CMOS technology. To elaborate, designed a bidirectional, asynchronous, and parallel pulse‐signaling scheme over an analog‐weighted phase‐change memory (PCM) synapse array to enable spike‐timing‐dependent plasticity (STDP) as a local weight update rule based on eCD is designed. Building upon the initial version of this work, significantly more experimental details are added, such as the on‐chip characterization results of LIF and backward‐LIF (BLIF) and stochasticity of our random walk circuitry. The experimental characterization of these on‐chip stochastic neuron circuits shows a reasonable symmetricity between LIF and BLIF as well as the necessary stochasticity for spiking RBM operation. Fully hardware‐based image classification recorded 93% on‐chip training accuracy from 100 handwritten MNIST digit images. In addition, we experimentally demonstrated the generative characteristics of the RBM by reconstructing partial patterns on hardware. As each synapse and neuron execute its computations in an asynchronous and fully parallel fashion, the chip can perform data‐intensive machine learning (ML) tasks in a power‐efficient manner and take advantage of the sparseness of spiking.