Synaptic plasticity allows cortical circuits to learn new tasks and to adapt
to changing environments. How do cortical circuits use plasticity to acquire
functions such as decision-making or working memory? Neurons are connected in
complex ways, forming recurrent neural networks, and learning modifies the
strength of their connections. Moreover, neurons communicate emitting brief
discrete electric signals. Here we describe how to train recurrent neural
networks in tasks like those used to train animals in neuroscience
laboratories, and how computations emerge in the trained networks.
Surprisingly, artificial networks and real brains can use similar computational
strategies.

该研究探讨了大脑皮层神经回路中突触可塑性、神经元之间的连接和电信号传递，指出如何通过训练循环神经网络学习新任务并改变环境适应能力，同时发现人工网络和真实大脑使用相似的计算策略。

人工神经网络和真实大脑训练中的新兴计算

Emergent Computations in Trained Artificial Neural Networks and Real  Brains

Deep learning has seen remarkable developments over the last years, many of
them inspired by neuroscience. However, the main learning mechanism behind
these advances - error backpropagation - appears to be at odds with
neurobiology. Here, we introduce a multilayer neuronal network model with
simplified dendritic compartments in which error-driven synaptic plasticity
adapts the network towards a global desired output. In contrast to previous
work our model does not require separate phases and synaptic learning is driven
by local dendritic prediction errors continuously in time. Such errors
originate at apical dendrites and occur due to a mismatch between predictive
input from lateral interneurons and activity from actual top-down feedback.
Through the use of simple dendritic compartments and different cell-types our
model can represent both error and normal activity within a pyramidal neuron.
We demonstrate the learning capabilities of the model in regression and
classification tasks, and show analytically that it approximates the error
backpropagation algorithm. Moreover, our framework is consistent with recent
observations of learning between brain areas and the architecture of cortical
microcircuits. Overall, we introduce a novel view of learning on dendritic
cortical circuits and on how the brain may solve the long-standing synaptic
credit assignment problem.

本文基于深度学习和神经科学，介绍了一种多层神经元网络模型，该模型利用简化的树突区，实现误差驱动的突触可塑性，在时间上持续地通过局部树突预测误差进行突触学习，进而解决了长期以来的突触学分配问题，并在回归和分类任务中证明模型的学习能力。

树突皮层微电路模拟反向传播算法

Dendritic cortical microcircuits approximate the backpropagation  algorithm

Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term memory networks (LSTMs), we introduce a recurrent neural
network in which information is gated through inhibitory cells that are
subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known
canonical excitatory-inhibitory cortical microcircuits. Our empirical
evaluation across sequential image classification and language modelling tasks
shows that subLSTM units can achieve similar performance to LSTM units. These
results suggest that cortical circuits can be optimised to solve complex
contextual problems and proposes a novel view on their computational function.
Overall our work provides a step towards unifying recurrent networks as used in
machine learning with their biological counterparts.

通过 gated-memory 神经网络中的 subLSTMs 单元，作者提出了一种在 known canonical 激活 - 抑制皮质微电路上的自然映射 ，通过在图像分类和语言建模任务中的实证评估，作者证明了 subLSTM 单元可获得类似于 LSTM 单元的性能，因此推断皮层电路可以被优化以解决复杂的背景问题，并提出了一个新颖的计算机功能视角。

大脑皮层微电路作为门控递归神经网络

Cortical microcircuits as gated-recurrent neural networks

Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identify which neuronal mechanisms are relevant, and to find suitable
abstractions to model them. Here, we show how aspects of spike timing, long
hypothesized to play a crucial role in cortical information processing, could
be incorporated into deep networks to build richer, versatile representations.
We introduce a neural network formulation based on complex-valued neuronal
units that is not only biologically meaningful but also amenable to a variety
of deep learning frameworks. Here, units are attributed both a firing rate and
a phase, the latter indicating properties of spike timing. We show how this
formulation qualitatively captures several aspects thought to be related to
neuronal synchrony, including gating of information processing and dynamic
binding of distributed object representations. Focusing on the latter, we
demonstrate the potential of the approach in several simple experiments. Thus,
neuronal synchrony could be a flexible mechanism that fulfills multiple
functional roles in deep networks.

利用复数神经元将神经元同步性的角度引入深度学习模型中，有效提高网络的灵活性和丰富性。