The assumption that during cortical embryogenesis neurons and synaptic connections are selected to form an ensemble maximising synchronous oscillation explains mesoscopic cortical development, and a mechanism for cortical information processing is implied by consistency with the Free Energy Principle and Dynamic Logic.A heteroclinic network emerges, with stable and unstable fixed points of oscillation corresponding to activity in symmetrically connected, versus asymmetrically connected, sets of neurons. Simulations of growth explain a wide range of anatomical observations for columnar and non-columnar cortex, superficial patch connections, and the organization and dynamic interactions of neurone response properties. An antenatal scaffold is created, upon which postnatal learning can establish continuously ordered neuronal representations, permitting matching of co-synchronous fields in multiple cortical areas to solve optimization problems as in Dynamic Logic. Fast synaptic competition partitions equilibria, minimizing “the curse of dimensionality”, while perturbations between imperfectly partitioned synchronous fields, under internal reinforcement, enable the cortex to become adaptively self-directed. As learning progresses variational free energy is minimized and entropy bounded.

在皮层胚胎发生过程中选择神经元和突触连接以形成一个最大化同步振荡的整体的假设解释了介观皮层发育，并且与自由能原理和动态逻辑的一致性暗示了皮层信息处理的机制。出现了异宿网络，其中稳定和不稳定的振荡固定点对应于对称连接和不对称连接的神经元组的活动。生长模拟解释了对柱状和非柱状皮层、表面斑块连接以及神经元反应特性的组织和动态相互作用的广泛解剖观察。创建了一个产前支架，产后学习可以在其上建立连续有序的神经元表征，允许在多个皮层区域匹配同步场以解决动态逻辑中的优化问题。快速突触竞争划分平衡，最小化“维度灾难”，而不完全划分的同步场之间的扰动，在内部强化下，使皮层能够自适应地自我导向。随着学习的进行，变分自由能被最小化并且熵有界。