In recent years, there has been increasing interest in developing models and
tools to address the complex patterns of connectivity found in brain tissue.
Specifically, this is due to a need to understand how emergent properties
emerge from these network structures at multiple spatiotemporal scales. We
argue that computational models are key tools for elucidating the possible
functionalities that can emerge from interactions of heterogeneous neurons
connected by complex networks on multi-scale temporal and spatial domains. Here
we review several classes of models including spiking neurons, integrate and
fire neurons with short term plasticity (STP), conductance based
integrate-and-fire models with STP, and population density neural field (PDNF)
models using simple examples with emphasis on neuroscience applications while
also providing some potential future research directions for AI. These
computational approaches allow us to explore the impact of changing underlying
mechanisms on resulting network function both experimentally as well as
theoretically. Thus we hope these studies will inform future developments in
artificial intelligence algorithms as well as help validate our understanding
of brain processes based on experiments in animals or humans.

本文旨在研究计算模型对处理脑组织中复杂连接模式的作用，我们找到了几类计算模型可以有效探索多尺度时空领域中异质性神经元相互作用和复杂网络连接带来的潜在功能性和后果。

神经生物学中的时空模式：未来人工智能概述

Spatiotemporal Patterns in Neurobiology: An Overview for Future Artificial Intelligence

This monograph attempts a theory of every 'thing' that can be distinguished
from other things in a statistical sense. The ensuing statistical
independencies, mediated by Markov blankets, speak to a recursive composition
of ensembles (of things) at increasingly higher spatiotemporal scales. This
decomposition provides a description of small things; e.g., quantum mechanics -
via the Schrodinger equation, ensembles of small things - via statistical
mechanics and related fluctuation theorems, through to big things - via
classical mechanics. These descriptions are complemented with a Bayesian
mechanics for autonomous or active things. Although this work provides a
formulation of every thing, its main contribution is to examine the
implications of Markov blankets for self-organisation to nonequilibrium
steady-state. In brief, we recover an information geometry and accompanying
free energy principle that allows one to interpret the internal states of
something as representing or making inferences about its external states. The
ensuing Bayesian mechanics is compatible with quantum, statistical and
classical mechanics and may offer a formal description of lifelike particles.

提出了一种从统计意义上区分事物的理论，着重考察了马尔可夫毛毯对于自组织至非平衡定态的影响，并提供了一种贝叶斯力学用于自主或活动的事物，该理论涵盖量子、统计和经典力学，并可能提供生命粒子的形式化描述。

一个特定物理领域的自由能原理

A free energy principle for a particular physics

Next place prediction algorithms are invaluable tools, capable of increasing
the efficiency of a wide variety of tasks, ranging from reducing the spreading
of diseases to better resource management in areas such as urban planning. In
this work we estimate upper and lower limits on the predictability of human
mobility to help assess the performance of competing algorithms. We do this
using GPS traces from 604 individuals participating in a multi year long
experiment, The Copenhagen Networks study. Earlier works, focusing on the
prediction of a participant's whereabouts in the next time bin, have found very
high upper limits (>90%). We show that these upper limits are highly dependent
on the choice of a spatiotemporal scales and mostly reflect stationarity, i.e.
the fact that people tend to not move during small changes in time. This leads
us to propose an alternative approach, which aims to predict the next location,
rather than the location in the next bin. Our approach is independent of the
temporal scale and introduces a natural length scale. By removing the effects
of stationarity we show that the predictability of the next location is
significantly lower (~71%) than the predictability of the location in the next
bin.

研究了使用 GPS 追踪数据进行下一地点预测的算法，提出了一个独立于时间尺度的自然长度尺度，并表明相比下一时间段位置预测，这种方法可以显著降低对下一地点的预测可靠性。