Parr Thomas, Sajid Noor, Friston Karl J
Wellcome Centre for Human Neuroimaging (UCL), London WC1N 3AR, UK.
Entropy (Basel). 2020 May 14;22(5):552. doi: 10.3390/e22050552.
The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised 'modules', each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is , as opposed to , that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.
神经处理过程划分为不同的信息流，一些人将此解释为支持大脑功能模块化观点的证据。这意味着存在一组专门的“模块”，每个模块在与其他大脑系统隔离的情况下执行特定类型的计算，然后再将此操作的结果与其他模块共享。鉴于对诸如大脑这样的随机非平衡系统的现代理解，一个更简单、更简洁的解释出现了。根据其密度动态来阐述非平衡稳态系统的演化表明，这类系统平均而言似乎在其稳态密度上执行梯度上升。如果这种稳态意味着足够稀疏的条件独立性结构，这就支持了一种平均场动力学公式。这将系统中所有状态的密度分解为这些状态的边际概率的乘积。这种因式分解赋予系统一种模块化的外观，从某种意义上说，我们可以独立解释每个因素的动态。然而，这里的论点是，是 而非 导致了大脑乃至任何有感知系统的功能解剖结构。在下面，我们简要概述平均场理论及其在随机动力系统中的应用。然后，我们通过简单的数值模拟来剖析这种因式分解的后果，并强调其对神经元信息传递和感知计算架构的影响。
