Stratton Peter, Wyeth Gordon, Wiles Janet
School of Information Technology and Electrical Engineering, The University of Queensland, Queensland, Brisbane, Australia.
J Comput Neurosci. 2010 Jun;28(3):527-38. doi: 10.1007/s10827-010-0234-7. Epub 2010 Mar 31.
Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions.
连续吸引子网络需要校准。大鼠头部方向（HD）系统的计算模型通常假设维持HD神经元活动的连接是预先连接且静态的。这些模型中的持续活动依赖于精确的连续吸引子动力学。目前尚不清楚这样的连接如何能够如此精确地布线，以及在面对持续的噪声和扰动时如何保持准确的校准。我们使用对称角头速度（AHV）细胞作为训练信号的HD系统自适应吸引子模型表明，HD系统可以从不稳定的起始条件中学习以支持稳定的放电模式。所提出的校准机制表明需要对称AHV细胞，其存在此前一直无法解释，并预测对称和不对称AHV细胞因其明显不同的功能而在形态、突触靶点和/或对突触后HD细胞的作用方式上应明显不同。
