Classical models describe primary visual cortex (V1) as a filter bank of
orientation-selective linear-nonlinear (LN) or energy models, but these models
fail to predict neural responses to natural stimuli accurately. Recent work
shows that models based on convolutional neural networks (CNNs) lead to much
more accurate predictions, but it remains unclear which features are extracted
by V1 neurons beyond orientation selectivity and phase invariance. Here we work
towards systematically studying V1 computations by categorizing neurons into
groups that perform similar computations. We present a framework to identify
common features independent of individual neurons' orientation selectivity by
using a rotation-equivariant convolutional neural network, which automatically
extracts every feature at multiple different orientations. We fit this model to
responses of a population of 6000 neurons to natural images recorded in mouse
primary visual cortex using two-photon imaging. We show that our
rotation-equivariant network not only outperforms a regular CNN with the same
number of feature maps, but also reveals a number of common features shared by
many V1 neurons, which deviate from the typical textbook idea of V1 as a bank
of Gabor filters. Our findings are a first step towards a powerful new tool to
study the nonlinear computations in V1.

本文研究了鼠视觉皮层神经元的处理方式，通过使用旋转等价卷积神经网络提取共性特征并与传统模型进行比较，发现传统线性 - 非线性模型存在不足，但卷积神经网络能够更加准确地预测自然刺激下神经元的反应，且 V1 神经元存在一些与传统 Gabor 过滤器不同的共性特征，这对研究 V1 中的非线性计算具有重要意义。

初级视皮层旋转等变卷积神经网络模型

A rotation-equivariant convolutional neural network model of primary  visual cortex

A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns.

本研究基于神经脉冲网络中的超级脉冲非线性电压三因素学习规则，探究了不同信用分配策略对多层神经网络的表现，可促进神经脉冲网络的学习和计算方式的理解。