Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data
Affiliation(s)
1 Institute of Automation, Chinese Academy of Sciences, Beijing, China. 2 University of Chinese Academy of Sciences, Beijing, China.
1 Institute of Automation, Chinese Academy of Sciences, Beijing, China. 2 University of Chinese Academy of Sciences, Beijing, China.
ABSTRACT
The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence.
The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence.
KEYWORDS
Neural Network Structure Prediction, Spike Prediction, Time-Order Strategy, Co-Occurrence Strategy, Spreading Activation
Neural Network Structure Prediction, Spike Prediction, Time-Order Strategy, Co-Occurrence Strategy, Spreading Activation
Cite this paper
Zhang, T. , Zeng, Y. , Xu, B. (2015) Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data. International Journal of Intelligence Science, 5, 102-111. doi: 10.4236/ijis.2015.52010.
Zhang, T. , Zeng, Y. , Xu, B. (2015) Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data. International Journal of Intelligence Science, 5, 102-111. doi: 10.4236/ijis.2015.52010.
References
[1] Zeng, Y., Zhang, T.L. and Xu, B. (2014) Neural Pathway Prediction Based on Multi-Neuron Spike Train Data. Proceedings of the 23rd Annual Computational Neuroscience Meeting (CNS 2014), Québec City, 26-31 July 2014, 6. [2] Zhang, T.L., Zeng, Y. and Xu, B. (2014) Neural Spike Prediction Based on Spreading Activation. Proceedings of the 23rd Annual Computational Neuroscience Meeting (CNS 2014), Québec City, 26-31 July 2014, 7. [3] Sporns, O., Tononi, G. and Kotter, R. (2005) The Human Connectome: A Structural Description of the Human Brain. PLoS Computational Biology, 1, e42. http://dx.doi.org/10.1371/journal.pcbi.0010042 [4] Arenkiel1, B.R. and Ehlers, M.D. (2009) Molecular Genetics and Imaging Technologies for Circuit-Based Neuroanatomy. Nature, 461, 900-907. http://dx.doi.org/10.1038/nature08536 [5] Mukamel, R. and Fried, I. (2011) Human Intracranial Recordings and Cognitive Neuroscience. Annual Review of Psychology, 63, 511-537. http://dx.doi.org/10.1146/annurev-psych-120709-145401 [6] Takahashi, N., Sasaki, T., Usami, A., Matsuki, N. and Ikegaya, Y. (2007) Watching Neuronal Circuit Dynamics through Functional Multineuron Calcium Imaging (fMCI). Neuroscience Research, 58, 219-225.
http://dx.doi.org/10.1016/j.neures.2007.03.001 [7] Kettunen, P. (2012) Calcium Imaging in the Zebrafish. In: Islam, S., Ed., Calcium Signaling, Springer Netherlands, Heidelberg, 1039-1071. [8] Stetter, O., Battaglia, D., Soriano, J. and Geisel, T. (2012) Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals. PLoS Computational Biology, 8, e1002653.
http://dx.doi.org/10.1371/journal.pcbi.1002653 [9] Mira, J. and Sánchez-Andrés, J.V. (1999) Foundations and Tools for Neural Modeling. Proceedings of International Work-Conference on Artificial and Natural Neural Networks, Vol. I, Alicante, 2-4 June 1999, 29. [10] Carnevale, N.T. and Hines, M.L. (2006) The Neuron Book. Cambridge University Press, Cambridge, UK.
http://dx.doi.org/10.1017/CBO9780511541612 [11] Dayan, P. and Abbott, L.F. (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge. [12] Anderson, J.R. (1983) A Spreading Activation Theory of Memory. Journal of Verbal Learning and Verbal Behavior, 22, 261-295. http://dx.doi.org/10.1016/S0022-5371(83)90201-3 [13] Yue, C.Y., Remy, S., Su, H.L., Beck, H. and Yaari, Y. (2005) Proximal Persistent Na+ Channels Drive Spike Afterdepolarization and Associated Bursting in Adult CA1 Pyramidal Cells. The Journal of Neuroscience, 25, 9704-9720.
http://dx.doi.org/10.1523/JNEUROSCI.1621-05.2005 [14] Yue, C.Y., Remy, S., Su, H.L., Beck, H. and Yaari, Y. (2011) High-Speed Multi-Neuron Calcium Imaging Using Nipkow-Type Confocal Microscopy. Current Protocols in Neuroscience, 2, Unit 2.14.
http://www.hippocampus.jp/data [15] Gómez-Di Cesare, C.M., Smith, K.L., Rice, F.L. and Swann, J.W. (1997) Axonal Remodeling during Postnatal Maturation of CA3 Hippocampal Pyramidal Neurons. Journal of Comparative Neurology, 384, 165-180. [16] Fujisawa, S., Matsuki, N. and Ikegaya, Y. (2006) Single Neurons Can Induce Phase Transitions of Cortical Recurrent Networks with Multiple Internal States. Cerebral Cortex, 16, 639-654.
http://dx.doi.org/10.1093/cercor/bhj010 [17] Sasaki, T., Matsuki, N. and Ikegaya, Y. (2007) Metastability of Active CA3 Networks. The Journal of Neuroscience, 27, 517-528. http://dx.doi.org/10.1523/JNEUROSCI.4514-06.2007 [18] Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D. and Darnell, J. (2000) Molecular Cell Biology. 4th Edition, Freeman and Company, New York.
[1] Zeng, Y., Zhang, T.L. and Xu, B. (2014) Neural Pathway Prediction Based on Multi-Neuron Spike Train Data. Proceedings of the 23rd Annual Computational Neuroscience Meeting (CNS 2014), Québec City, 26-31 July 2014, 6. [2] Zhang, T.L., Zeng, Y. and Xu, B. (2014) Neural Spike Prediction Based on Spreading Activation. Proceedings of the 23rd Annual Computational Neuroscience Meeting (CNS 2014), Québec City, 26-31 July 2014, 7. [3] Sporns, O., Tononi, G. and Kotter, R. (2005) The Human Connectome: A Structural Description of the Human Brain. PLoS Computational Biology, 1, e42. http://dx.doi.org/10.1371/journal.pcbi.0010042 [4] Arenkiel1, B.R. and Ehlers, M.D. (2009) Molecular Genetics and Imaging Technologies for Circuit-Based Neuroanatomy. Nature, 461, 900-907. http://dx.doi.org/10.1038/nature08536 [5] Mukamel, R. and Fried, I. (2011) Human Intracranial Recordings and Cognitive Neuroscience. Annual Review of Psychology, 63, 511-537. http://dx.doi.org/10.1146/annurev-psych-120709-145401 [6] Takahashi, N., Sasaki, T., Usami, A., Matsuki, N. and Ikegaya, Y. (2007) Watching Neuronal Circuit Dynamics through Functional Multineuron Calcium Imaging (fMCI). Neuroscience Research, 58, 219-225.
http://dx.doi.org/10.1016/j.neures.2007.03.001 [7] Kettunen, P. (2012) Calcium Imaging in the Zebrafish. In: Islam, S., Ed., Calcium Signaling, Springer Netherlands, Heidelberg, 1039-1071. [8] Stetter, O., Battaglia, D., Soriano, J. and Geisel, T. (2012) Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals. PLoS Computational Biology, 8, e1002653.
http://dx.doi.org/10.1371/journal.pcbi.1002653 [9] Mira, J. and Sánchez-Andrés, J.V. (1999) Foundations and Tools for Neural Modeling. Proceedings of International Work-Conference on Artificial and Natural Neural Networks, Vol. I, Alicante, 2-4 June 1999, 29. [10] Carnevale, N.T. and Hines, M.L. (2006) The Neuron Book. Cambridge University Press, Cambridge, UK.
http://dx.doi.org/10.1017/CBO9780511541612 [11] Dayan, P. and Abbott, L.F. (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge. [12] Anderson, J.R. (1983) A Spreading Activation Theory of Memory. Journal of Verbal Learning and Verbal Behavior, 22, 261-295. http://dx.doi.org/10.1016/S0022-5371(83)90201-3 [13] Yue, C.Y., Remy, S., Su, H.L., Beck, H. and Yaari, Y. (2005) Proximal Persistent Na+ Channels Drive Spike Afterdepolarization and Associated Bursting in Adult CA1 Pyramidal Cells. The Journal of Neuroscience, 25, 9704-9720.
http://dx.doi.org/10.1523/JNEUROSCI.1621-05.2005 [14] Yue, C.Y., Remy, S., Su, H.L., Beck, H. and Yaari, Y. (2011) High-Speed Multi-Neuron Calcium Imaging Using Nipkow-Type Confocal Microscopy. Current Protocols in Neuroscience, 2, Unit 2.14.
http://www.hippocampus.jp/data [15] Gómez-Di Cesare, C.M., Smith, K.L., Rice, F.L. and Swann, J.W. (1997) Axonal Remodeling during Postnatal Maturation of CA3 Hippocampal Pyramidal Neurons. Journal of Comparative Neurology, 384, 165-180. [16] Fujisawa, S., Matsuki, N. and Ikegaya, Y. (2006) Single Neurons Can Induce Phase Transitions of Cortical Recurrent Networks with Multiple Internal States. Cerebral Cortex, 16, 639-654.
http://dx.doi.org/10.1093/cercor/bhj010 [17] Sasaki, T., Matsuki, N. and Ikegaya, Y. (2007) Metastability of Active CA3 Networks. The Journal of Neuroscience, 27, 517-528. http://dx.doi.org/10.1523/JNEUROSCI.4514-06.2007 [18] Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D. and Darnell, J. (2000) Molecular Cell Biology. 4th Edition, Freeman and Company, New York.