Brain representations must strike a balance between generalizability and
adaptability. Neural codes capture general statistical regularities in the
world, while dynamically adjusting to reflect current goals. One aspect of this
adaptation is stochastically co-modulating neurons' gains based on their task
relevance. These fluctuations then propagate downstream to guide
decision-making. Here, we test the computational viability of such a scheme in
the context of multi-task learning. We show that fine-tuning convolutional
networks by stochastic gain modulation improves on deterministic gain
modulation, achieving state-of-the-art results on the CelebA dataset. To better
understand the mechanisms supporting this improvement, we explore how
fine-tuning performance is affected by architecture using Cifar-100. Overall,
our results suggest that stochastic comodulation can enhance learning
efficiency and performance in multi-task learning, without additional learnable
parameters. This offers a promising new direction for developing more flexible
and robust intelligent systems.

脑表示必须在一般性和适应性之间取得平衡，神经编码捕捉世界的一般统计规律，同时根据当前目标动态调整，其中一方面的适应性是基于任务相关性随机共调制神经元的增益，这些波动然后向下传播以指导决策。我们通过随机增益调制对卷积网络进行微调，显示优于确定性增益调制，在 CelebA 数据集上达到最先进的结果。为了更好地理解支持此改进的机制，我们使用 Cifar-100 研究了微调性能如何受到架构的影响。总体而言，我们的结果表明随机共调制可以增强多任务学习中的学习效率和性能，无需额外的可学习参数，为开发更具灵活性和鲁棒性的智能系统提供了一个有希望的新方向。

通过生物启发的随机组调整进行任务适应

Task adaption by biologically inspired stochastic comodulation

A central question for neuroscience is how to characterize brain
representations of perceptual and cognitive content. An ideal characterization
should distinguish different functional regions with robustness to noise and
idiosyncrasies of individual brains that do not correspond to computational
differences. Previous studies have characterized brain representations by their
representational geometry, which is defined by the representational
dissimilarity matrix (RDM), a summary statistic that abstracts from the roles
of individual neurons (or responses channels) and characterizes the
discriminability of stimuli. Here we explore a further step of abstraction:
from the geometry to the topology of brain representations. We propose
topological representational similarity analysis (tRSA), an extension of
representational similarity analysis (RSA) that uses a family of
geo-topological summary statistics that generalizes the RDM to characterize the
topology while de-emphasizing the geometry. We evaluate this new family of
statistics in terms of the sensitivity and specificity for model selection
using both simulations and functional MRI (fMRI) data. In the simulations, the
ground truth is a data-generating layer representation in a neural network
model and the models are the same and other layers in different model instances
(trained from different random seeds). In fMRI, the ground truth is a visual
area and the models are the same and other areas measured in different
subjects. Results show that topology-sensitive characterizations of population
codes are robust to noise and interindividual variability and maintain
excellent sensitivity to the unique representational signatures of different
neural network layers and brain regions.

探索了拓扑脑表示相似性分析（tRSA），一种推广了脑表示相似性分析（RSA）的几何与拓扑摘要统计的家族，该方法可用于表征脑表示的拓扑特征并在噪音和个体变异性方面具有鲁棒性，在模拟和功能性磁共振成像（fMRI）数据上进行了评估。