Imaging attractor dynamics in the neural compass

单个神经元在短时间尺度上工作;从1毫秒的动作电位持续时间到仅持续10毫秒或100毫秒的突触电流。然而，嵌入网络中的神经元能够在没有外部刺激的情况下保持持续的活动。一个著名的计算理论使用“吸引子动力学”，即网络的连通性在没有额外输入的情况下稳定活动。一般来说，这是在网络中实现的，该网络由编码相似特征的神经元之间的经常性兴奋性连接沿着前馈抑制性连接组成，以使编码非相似特征的神经元的活动沉默。这就创建了一个竞争网络，它将在没有任何额外输入的情况下维持其当前活动，并且不会改变，直到新的输入足够强以改变“吸引子状态”。重要的是，这个框架可以编码离散和连续的“模拟”变量。在这里，我们建议寻找证据的吸引动力学的头部方向（HD）的细胞网络。

Individual neurons work on short timescales; from an action potential duration of 1 ms to synaptic currents that persist for just 10 ms or 100 ms of milliseconds. Yet, neurons embedded within a network are capable of maintaining persistent activity in the absence of external stimuli. One prominent computational theory uses 'attractor dynamics', whereby the connectivity of the network stabilizes activity without additional input to the network. In general, this is implemented in network that consists of recurrent excitatory connections between neurons that code for similar features along with feedforward inhibitory connectivity to silence the activity of neurons coding for non-similar features. This creates a competitive network that will sustain its current activity without any additional input, and will not change until a new input is strong enough to change the ‘attractor state’. Importantly, this framework can encode discrete and continuous ‘analog’ variables. Here we propose to look for evidence of attractor dynamics in the head direction (HD) cell network.?