An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.

开发并测试了一种逆向程序，以从大脑活动的全局信号中恢复功能和结构信息。该方法假设具有通过有向网络耦合的兴奋性和抑制性神经元的泄漏积分和激发模型。神经元被赋予了一个异质的电流值，这设置了它们相关的动态机制。通过利用异质平均场近似，该方法试图从全局活动模式中重建兴奋性和抑制性神经元的传入度分布，以及指定电流的分布。提议的逆方案首先针对合成数据进行验证。然后，用双光子光片显微镜记录的斑马鱼幼虫的延时采集被用作重建算法的输入。发现了兴奋性神经元传入连接的幂律分布。局部度分布也通过将整个大脑分割成从带注释的地图集追踪的子区域来计算。