Gupta Daya Shankar, Bahmer Andreas
Biology Department, Camden County College, Blackwood, NJ 08012, USA.
Comprehensive Hearing Center, ENT Clinic, University of Wuerzburg, 97080 Wuerzburg, Germany.
Entropy (Basel). 2019 Apr 4;21(4):365. doi: 10.3390/e21040365.
Perception and motor interaction with physical surroundings can be analyzed by the changes in probability laws governing two possible outcomes of neuronal activity, namely the presence or absence of spikes (binary states). Perception and motor interaction with the physical environment are partly accounted for by a reduction in entropy within the probability distributions of binary states of neurons in distributed neural circuits, given the knowledge about the characteristics of stimuli in physical surroundings. This reduction in the total entropy of multiple pairs of circuits in networks, by an amount equal to the increase of mutual information, occurs as sensory information is processed successively from lower to higher cortical areas or between different areas at the same hierarchical level, but belonging to different networks. The increase in mutual information is partly accounted for by temporal coupling as well as synaptic connections as proposed by Bahmer and Gupta (Front. Neurosci. 2018). We propose that robust increases in mutual information, measuring the association between the characteristics of sensory inputs' and neural circuits' connectivity patterns, are partly responsible for perception and successful motor interactions with physical surroundings. The increase in mutual information, given the knowledge about environmental sensory stimuli and the type of motor response produced, is responsible for the coupling between action and perception. In addition, the processing of sensory inputs within neural circuits, with no prior knowledge of the occurrence of a sensory stimulus, increases Shannon information. Consequently, the increase in surprise serves to increase the evidence of the sensory model of physical surroundings.
与物理环境的感知和运动交互可以通过支配神经元活动两种可能结果（即脉冲的存在或不存在，二元状态）的概率定律的变化来分析。考虑到有关物理环境中刺激特征的知识，分布式神经回路中神经元二元状态概率分布内熵的降低部分解释了与物理环境的感知和运动交互。随着感觉信息从较低皮质区域依次处理到较高皮质区域，或在同一层次水平上不同区域（但属于不同网络）之间处理，网络中多对回路的总熵减少量等于互信息的增加量。互信息的增加部分是由Bahmer和Gupta（《神经科学前沿》，2018年）提出的时间耦合以及突触连接所导致的。我们提出互信息的显著增加（衡量感觉输入特征与神经回路连接模式之间的关联）部分负责与物理环境的感知和成功运动交互。考虑到有关环境感觉刺激和所产生运动反应类型的知识，互信息的增加负责动作与感知之间的耦合。此外，在没有关于感觉刺激发生的先验知识的情况下，神经回路内感觉输入的处理会增加香农信息。因此，意外性的增加有助于增加物理环境感觉模型的证据。
