Feed-forward convolutional neural networks (CNNs) are currently
state-of-the-art for object classification tasks such as ImageNet. Further,
they are quantitatively accurate models of temporally-averaged responses of
neurons in the primate brain's visual system. However, biological visual
systems have two ubiquitous architectural features not shared with typical
CNNs: local recurrence within cortical areas, and long-range feedback from
downstream areas to upstream areas. Here we explored the role of recurrence in
improving classification performance. We found that standard forms of
recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the
ImageNet task. In contrast, novel cells that incorporated two structural
features, bypassing and gating, were able to boost task accuracy substantially.
We extended these design principles in an automated search over thousands of
model architectures, which identified novel local recurrent cells and
long-range feedback connections useful for object recognition. Moreover, these
task-optimized ConvRNNs matched the dynamics of neural activity in the primate
visual system better than feedforward networks, suggesting a role for the
brain's recurrent connections in performing difficult visual behaviors.

通过引入前馈卷积神经网络与反馈式局部循环连接设计，提高图像识别准确性，更加贴近灵长类动物视觉系统的神经活动，为当代物体识别任务的研究提供了新的思路。

任务驱动的卷积循环视觉系统模型

Task-Driven Convolutional Recurrent Models of the Visual System

Based on the predictive coding theory in neuroscience, we designed a
bi-directional and recurrent neural net, namely deep predictive coding networks
(PCN). It has feedforward, feedback, and recurrent connections. Feedback
connections from a higher layer carry the prediction of its lower-layer
representation; feedforward connections carry the prediction errors to its
higher-layer. Given image input, PCN runs recursive cycles of bottom-up and
top-down computation to update its internal representations and reduce the
difference between bottom-up input and top-down prediction at every layer.
After multiple cycles of recursive updating, the representation is used for
image classification. With benchmark data (CIFAR-10/100, SVHN, and MNIST), PCN
was found to always outperform its feedforward-only counterpart: a model
without any mechanism for recurrent dynamics. Its performance tended to improve
given more cycles of computation over time. In short, PCN reuses a single
architecture to recursively run bottom-up and top-down processes. As a
dynamical system, PCN can be unfolded to a feedforward model that becomes
deeper and deeper over time, while refining it representation towards more
accurate and definitive object recognition.

本研究利用预测编码理论设计了双向、递归的深度预测编码网络，在图像分类中具有较高性能表现，与其单向前馈模型相比，其性能随着递归周期的增加而提高，并逐步准确地识别物体。

深度预测编码网络在物体识别中的应用

Deep Predictive Coding Network for Object Recognition

Brains need to predict how the body reacts to motor commands. It is an open
question how networks of spiking neurons can learn to reproduce the non-linear
body dynamics caused by motor commands, using local, online and stable learning
rules. Here, we present a supervised learning scheme for the feedforward and
recurrent connections in a network of heterogeneous spiking neurons. The error
in the output is fed back through fixed random connections with a negative
gain, causing the network to follow the desired dynamics, while an online and
local rule changes the weights. The rule for Feedback-based Online Local
Learning Of Weights (FOLLOW) is local in the sense that weight changes depend
on the presynaptic activity and the error signal projected onto the
postsynaptic neuron. We provide examples of learning linear, non-linear and
chaotic dynamics, as well as the dynamics of a two-link arm. Using the Lyapunov
method, and under reasonable assumptions and approximations, we show that
FOLLOW learning is stable uniformly, with the error going to zero
asymptotically.

提出一种基于前馈和反馈连接的异质性尖峰神经元网络的监督学习方案，采用负收益的反馈固定随机连接，与在线和本地规则同时改变权重，在线学习线性，非线性和混沌动力学以及两段臂的动力学。