Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems

Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task

Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequences of future actions? Recent work has shown that recurrent neural networks trained on goal based tasks can develop representations resembling those found in the brain, entorhinal cortex grid cells, for instance. Here we explore the evolution of the dynamics of their internal representations and compare this with experimental data. We observe that once a recurrent network is trained to learn the structure of its envir…

规划作为动力学松弛：海马递归网络实现最优目标导向导航

Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation

本研究提出海马递归网络通过松弛动力学实现最优目标导向导航，其活动场等价于LMDP期望场，理论分析与模拟证明其在复杂障碍环境中高效鲁棒，且支持局部环境变化的低秩更新。

Implementing Inductive bias for different navigation tasks through diverse RNN attractors

Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map. The precise form of this representation is often considered to be a metric representation of space. An internal representation, however, is judged by its contribution to performance on a given task, and may thus vary between different types of navigation tasks. Here we train a recurrent neural network that controls an agent performing several navigation tasks in a simple environment. To focus on internal representations, we split learning into a ta…

Learning to infer in recurrent biological networks

A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most hypotheses of how the brain might learn these models assume that neurons in a population are conditionally independent given their common inputs. This simplification is likely not compatible with the type of local recurrence observed in the brain. Seeking an alternative that is compatible with complex inter-dependencies yet consistent with known biology, we argue here that the cortex may learn with an adversarial al…

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that gri…

发现循环神经网络中超越简单性偏好的替代解

Discovering alternative solutions beyond the simplicity bias in recurrent neural networks

针对RNN任务训练中的简单性偏好导致解趋同的问题，提出迭代神经相似性消减（INSD）方法，通过惩罚神经活动线性可预测性发现替代解，这些解在多种分析中独特且有时性能更优，有助于生成更丰富的神经计算假设。