[论文解读] ORGaNICs: A Theory of Working Memory in Brains and Machines

David J. Heeger, Wayne E. Mackey|arXiv (Cornell University)|Mar 16, 2018

Neural dynamics and brain function参考文献 106被引用 3

一句话总结

ORGaNICs 提出了一种生物物理上合理的循环神经回路模型——振荡性循环门控神经积分器回路（Oscillatory Recurrent Gated Neural Integrator Circuits），用以解释前额叶皮层和顶叶皮层中的工作记忆动态。通过将长短期记忆（LSTM）单元适配到基于生物基础的框架中，并利用丘脑皮层环路实现侧向抑制，ORGaNICs 实现了可分析处理的、动态可变的信息编码与读出，为大脑和机器提供了一个标准计算框架。

ABSTRACT

Working memory is a cognitive process that is responsible for temporarily holding and manipulating information. Most of the empirical neuroscience research on working memory has focused on measuring sustained activity in prefrontal cortex (PFC) and/or parietal cortex during simple delayed-response tasks, and most of the models of working memory have been based on neural integrators. But working memory means much more than just holding a piece of information online. We describe a new theory of working memory, based on a recurrent neural circuit that we call ORGaNICs (Oscillatory Recurrent GAted Neural Integrator Circuits). ORGaNICs are a variety of Long Short Term Memory units (LSTMs), imported from machine learning and artificial intelligence. ORGaNICs can be used to explain the complex dynamics of delay-period activity in prefrontal cortex (PFC) during a working memory task. The theory is analytically tractable so that we can characterize the dynamics, and the theory provides a means for reading out information from the dynamically varying responses at any point in time, in spite of the complex dynamics. ORGaNICs can be implemented with a biophysical (electrical circuit) model of pyramidal cells, combined with shunting inhibition via a thalamocortical loop. Although introduced as a computational theory of working memory, ORGaNICs are also applicable to models of sensory processing, motor preparation and motor control. ORGaNICs offer computational advantages compared to other varieties of LSTMs that are commonly used in AI applications. Consequently, ORGaNICs are a framework for canonical computation in brains and machines.

研究动机与目标

• 开发一种超越持续活动模型的生物合理工作记忆理论。
• 解决传统神经积分器模型在解释前额叶皮层复杂延迟期动态方面的局限性。
• 在单一计算框架下统一工作记忆、感觉处理与运动控制。
• 提供一个可分析处理的模型，能够从时变神经反应中读出信息。

提出的方法

• ORGaNICs 是一种针对生物合理性进行调整的长短期记忆（LSTM）单元变体。
• 该模型利用振荡性循环动态，支持瞬时与持续活动模式。
• 通过丘脑皮层环路引入侧向抑制，以调节兴奋性并实现动态门控。
• 该电路使用锥体细胞和突触整合的生物物理模型实现。
• 理论分析可表征任意时间点的神经动态与信息读出。
• 该框架支持从非平稳神经反应中实时解码信息。

实验结果

研究问题

• RQ1 如何利用生物合理的神经回路建模工作记忆，以支持复杂且时变的活动模式？
• RQ2 在工作记忆任务中，前额叶皮层如何实现动态信息编码与读出？
• RQ3 LSTM类单元如何被调整以反映已知的皮层微环路结构与生物物理特性？
• RQ4 单一计算框架能否解释工作记忆、感觉处理与运动控制？
• RQ5 ORGaNICs 在神经科学与人工智能应用中相较于标准LSTM具有哪些优势？

主要发现

• ORGaNICs 成功再现了在工作记忆任务中观察到的前额叶皮层复杂的延迟期活动模式。
• 该模型实现了对神经动态的可分析表征，包括瞬时与振荡性反应。
• 即使在非平稳活动下，也能在任意时间点从动态变化的神经反应中读出信息。
• 该框架在统一架构中支持多种认知功能——工作记忆、感觉处理与运动控制。
• ORGaNICs 提供了一种基于生物物理原理的替代方案，相较于标准LSTM，具有潜在的可解释性与计算效率优势。