基于神经能量场梯度的智力探索

PDF(1391 KB)

振动与冲击 ›› 2017, Vol. 36 ›› Issue (2) : 7-12.

论文

基于神经能量场梯度的智力探索

王毅泓王如彬朱雅婷

作者信息 +

Mental exploration based on neural energy field gradient

WANG Yihong,WANG Rubin,ZHU Yating

Author information +

文章历史 +

摘要

啮齿类动物可以在海马中形成代表环境的认知地图来解决空间问题。在经典的认知地图模型中，系统需要大量物理探索来学习空间环境，从而解决寻路问题，这个过程会耗费大量时间和能量。虽然Hopfield的智力探索模型弥补了这个缺陷，但是该模型并没有关注路径的高效性。并且这个模型主要来自于人工神经网络，缺乏明确的生理学意义。本文的计算模型在智力探索概念的基础上，运用神经能量编码的理论来解决路径搜索问题：该模型通过位置细胞集群的发放功率构建一种能量场，并计算能量场的梯度，进而用梯度向量来研究智力探索问题。研究结果表明本文提出的这种新的智力探索模型不仅可以更高效地找到优化路径，而且呈现了具有生物物理学意义的学习过程。这种新思路验证了位置细胞和突触对空间记忆的重要性以及能量编码的有效性，为了解空间记忆的神经动力学机制提供了理论基础。

Abstract

Rodent animals can solve the space problem by means of forming a cognitive map in the hippocampus representing the environment. In the classic model of cognitive map,the system needs large amount of physical exploration to study the spatial environment so as to solve path-finding problems,which costs a lot of time and energy. Although the Hopfield’s mental exploration model makes up the deficiency mentioned above,the efficiency of the path has not been focused in the model. Moreover,this model mainly comes from the artificial neural network,lacking of clear physiological significance. In the paper,based on the concept of mental exploration,the neural energy coding theory was applied to the calculation model so as to solve the path search problem: an energy field was constructed in the model on the basis of the firing power of position cell clusters,and the energy field gradient was calculated which can be used to study the mental exploration problem. The study shows that the new mental exploration model proposed can efficiently find the optimal path,and present the learning process with biophysical meaning as well. The new idea verifies the importance of position cell and synapse on the spatial memory and the efficiency of energy coding,which provides the theoretical basis for the neural dynamics mechanism of spatial memory.

导出引用

王毅泓王如彬朱雅婷 . 基于神经能量场梯度的智力探索[J]. 振动与冲击, 2017, 36(2): 7-12

WANG Yihong,WANG Rubin,ZHU Yating. Mental exploration based on neural energy field gradient[J]. Journal of Vibration and Shock, 2017, 36(2): 7-12

参考文献

[1] Tolman E C. Cognitive maps in rats and men. Psychological Review, 1948, 55:189-208
[2] O'Keefe J, Nadel L. The hippocampus as a cognitive map. Oxford: Oxford University Press, 1978
[3] A.D. Redish, D.S. Touretzky. Beyond the Cognitive Map. [PHD thesis]. Pittsburg, PA: Dep. of Computer Science, Carnegie Mellon University, 1997
[4] 朱青, 王如彬. 基于智力探索的认知神经动力学. 振动与冲击, 2013, 32(2):189-200 (Zhu Qing, Wang Rubin. The cognitive Neurodynamics Based on Mental Exploration. Journal of Vibration and Shock, 2013, 32(2):189-200 (in Chinese))
[5] Wilson MA, McNaughton BL. Dynamics of the hippocampal ensemble code for space. Science, 1993, 261:1055–1058
[6] Redish AD. Beyond the cognitive map. Cambridge: The MIT Press, 1999
[7] Muller RU, Kubie JL. The effects of changes in the environment on the spatial firing of hippocampal complexspike cells. J. Neurosci, 1987, 7(7):1951–1968
[8] Bostock E, Muller RU, Kubie JL. Experience-dependent modifications of hippocampal place cell firing. Hippocampus, 1991, 1(2):193–205
[9] Gothard KM, Skaggs WE, Moore KM, McNaughton BL. Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task. J. Neurosci, 1996, 16(2):823–835
[10] Muller RU, Kubie JL, Saypoff R. The hippocampus as a cognitive graph (abridged version). Hippocampus, 1991, 1(3):243–246
[1]1 Muller RU, Stead M, Pach J. The hippocampus as a cognitive graph. J. Gen Physiol, 1996, 107(6):663–694
[12] Burgess N, Recce M, O’Keefe J. A model of hippocampal function. Neural Networks, 1994, 7(6–7):1065–1081
[13] Blum KI, Abbott LF. A model of spatial map formation in the hippocampus of the rat. Neural Comput, 1996,8(1):85–93
[14] Gerstner W, Abbott LF. Learning navigational maps through potentiation and modulation of hippocampal place cells. J. Comput Neurosci, 1997, 4(1):79–94
[15] Redish AD, Touretzky DS. The role of the hippocampus in solving the Morris water maze. Neural Comput, 1998, 10(1):73–111
[16] Trullier O, Meyer JA. Animat navigation using a cognitive graph. Biol Cybernet, 2000, 83(3):271–285
[17] John J. Hopfield. Neurodynamics of mental exploration. PNAS, 2010, 107(4): 1648–1653
[18] Rubin Wang, Zhikang Zhang, Mechanism on brain information processing: energy coding. Applied Physical Letters (APL). 2006, 89:123903
[19] Rubin Wang, Zhikang Zhang, Energy coding in biological neural network. Cognitive Neurodynamics. 2007, 1(3):203-212
[20] Rubin Wang, Zhikang Zhang, Chen Guanrong. Energy function and energy evolution on neural population. IEEE Transactions on Neural Networks, 2008, 19(3): 535-538
[21] Rubin Wang, Zhikang Zhang, Guanrong Chen, Energy coding and energy functions for local activities of brain. Neurocomputing. 2009, 73(1-3): 139-150
[22] Rubin Wang, Zhikang Zhang, Computation of neuronal energy based on information coding. Chinese Journal of Theoretical and Applied Mechanics. 2012, 44(4):779-786
[23] Rubin Wang, Ziyin Wang, Ichiro Tsuda. Can neural information be represented as neural energy? Front. Comput. Neurosci. doi: 10.3389/fncom.2015.00007
[24] Rubin Wang, Ziyin Wang. Energy distribution property and energy coding of a structural neural network. Front. Comput. Neurosci. doi: 10.3389/fncom.2014.00014
[25] Rubin Wang, Ichiro Tsuda, Zhikang Zhang. A New Work Mechanism on Neuronal Activity. International Journal of Neural Systems, 2015, 25(1): 1450037
[26] 王如彬,张志康. 基于信息编码的神经能量计算. 力学学报, 2012, 44(4):779-786 (Wang Rubin, Zhang Zhikang. Computation of neural energy based on information coding. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(4):779-786 (in Chinese))
[27] 郑锦超,王如彬,张志康等. 神经能量与神经信息之间内在动力学关系初探. 力学学报, 2012, 44(5):919-927 (Zheng Jinchao, Wang Rubin, Zhang Zhikang, et al. The first exploration of the dynamic relation between nervous energy and neural information. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(5):919-927 (in Chinese))
[28] 王如彬,张志康. 耦合条件下大脑皮层神经振子群的能量函数. 力学学报, 2008, 40(2):238-249 (Wang Rubin, Zhang Zhikang. Energy function of population of neural oscillators in cerebral cortex under coupling condition. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(2):238-249 (in Chinese）