Chen Yanqing
The Neurosciences InstituteLa Jolla, CA, USA.
Front Comput Neurosci. 2017 Apr 21;11:20. doi: 10.3389/fncom.2017.00020. eCollection 2017.
A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.
中枢神经系统的一个主要功能是区分不同类别或类型的感觉输入。神经网络通过在神经层次结构的几个阶段学习不同的感觉图谱来完成此类任务，这样不同的神经元会选择性地放电，以反映不同的内部或外部模式和状态。大脑中这种图谱形成过程的确切机制尚未完全理解。在此，我们研究一个简单的递归/折返式神经元网络对不同输入进行群体选择和区分以生成感觉图谱的机制。我们描述了从节律性癫痫状态（其中所有神经元对任何输入进行同步且无差别放电）转变为胜者全得状态（其中仅一部分神经元对特定输入放电）的条件和机制。我们证明了一个解析条件，在该条件下，具有不同兴奋性和抑制性群体的三层脉冲网络中的局部递归兴奋 - 抑制相互作用能够产生稳定的凸起解和胜者全得状态，并证明了周围抑制连接拓扑对脉冲神经网络中动态模式稳定性的重要性。
