The adaptive leaky integrate-and-fire (ALIF) model is fundamental within
computational neuroscience and has been instrumental in studying our brains
$\textit{in silico}$. Due to the sequential nature of simulating these neural
models, a commonly faced issue is the speed-accuracy trade-off: either
accurately simulate a neuron using a small discretisation time-step (DT), which
is slow, or more quickly simulate a neuron using a larger DT and incur a loss
in simulation accuracy. Here we provide a solution to this dilemma, by
algorithmically reinterpreting the ALIF model, reducing the sequential
simulation complexity and permitting a more efficient parallelisation on GPUs.
We computationally validate our implementation to obtain over a $50\times$
training speedup using small DTs on synthetic benchmarks. We also obtained a
comparable performance to the standard ALIF implementation on different
supervised classification tasks - yet in a fraction of the training time.
Lastly, we showcase how our model makes it possible to quickly and accurately
fit real electrophysiological recordings of cortical neurons, where very fine
sub-millisecond DTs are crucial for capturing exact spike timing.

通过算法重新解释自适应泄露积分 - 放电 (ALIF) 模型，我们提供了一种解决神经模型顺序仿真的速度和精确性之间权衡的方法，并实现了在 GPU 上更高效的并行化。在合成基准测试中，我们通过小时间间隔进行了计算验证，获得了超过 50 倍的训练加速度。我们还在不同的监督分类任务上获得了与标准 ALIF 实现相当的性能，但训练时间大大缩短。最后，我们展示了我们的模型如何能够快速而准确地拟合大脑皮层神经元的实际电生理记录，这里精确的微秒级时间间隔对于捕捉确切的尖峰时间至关重要。

应对自适应脉冲神经元的速度 - 准确性模拟权衡

Addressing the speed-accuracy simulation trade-off for adaptive spiking  neurons

Electrophysiological recordings of neural activity in a mouse's brain are
very popular among neuroscientists for understanding brain function. One
particular area of interest is acquiring recordings from the Purkinje cells in
the cerebellum in order to understand brain injuries and the loss of motor
functions. However, current setups for such experiments do not allow the mouse
to move freely and, thus, do not capture its natural behaviour since they have
a wired connection between the animal's head stage and an acquisition device.
In this work, we propose a lightweight neuronal-spike detection and
classification architecture that leverages on the unique characteristics of the
Purkinje cells to discard unneeded information from the sparse neural data in
real time. This allows the (condensed) data to be easily stored on a removable
storage device on the head stage, alleviating the need for wires. Our proposed
implementation shows a >95% overall classification accuracy while still
resulting in a small-form-factor design, which allows for the free movement of
mice during experiments. Moreover, the power-efficient nature of the design and
the usage of STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) as
the removable storage allows the head stage to easily operate on a tiny battery
for up to approximately 4 days.

提出一种轻量级的神经元尖峰检测和分类架构，利用浓缩的脑内神经数据的独特特征将不必要的信息丢弃，从而实现无线头戴式存储装置，允许小鼠在实验中自由运动，并通过使用 STT-RAM（自旋转磁翻转随机存取存储器）作为可拆卸存储器和设计的低功耗性质，使头戴式仪器在小型电池上运行长达大约 4 天。

一种用于实时神经突触分类的轻量级架构

A Lightweight Architecture for Real-Time Neuronal-Spike Classification

Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on visual object classification tasks. In addition, it is a useful
model for predication of neuronal responses recorded in visual system. However,
there is still no clear understanding of what CNNs learn in terms of visual
neuronal circuits. Visualizing CNN's features to obtain possible connections to
neuronscience underpinnings is not easy due to highly complex circuits from the
retina to higher visual cortex. Here we address this issue by focusing on
single retinal ganglion cells with a simple model and electrophysiological
recordings from salamanders. By training CNNs with white noise images to
predicate neural responses, we found that convolutional filters learned in the
end are resembling to biological components of the retinal circuit. Features
represented by these filters tile the space of conventional receptive field of
retinal ganglion cells. These results suggest that CNN could be used to reveal
structure components of neuronal circuits.

本研究用深度学习卷积神经网络尝试预测视神经系统中的神经元反应，结果显示，CNN 能学到视网膜电路的生物学组成，且这些特征可以显示为视网膜节细胞传统感受野的空间瓦片，为研究神经回路的结构奠定了基础。