Time-series prediction with single integrate-and-fire neuron

In this paper, a learning algorithm for a single integrate-and-fire neuron (IFN) is proposed and tested for various applications in which a multilayer perceptron neural network is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and time-series prediction have been illustrated. It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.

[1]  Jianfeng Feng,et al.  Integrate-and-fire and Hodgkin-Huxley models with current inputs , 2001 .

[2]  W. J. Freeman.  Why neural networks don't yet fly: inquiry into the neurodynamics of biological intelligence , 1988, IEEE 1988 International Conference on Neural Networks.

[3]  Prem Kumar Kalra,et al.  Chaotic Behavior in Neural Networks and FitzHugh-Nagumo Neuronal Model , 2004, ICONIP.

[4]  Otávio Augusto S. Carpinteiro,et al.  A hierarchical neural model in short-term load forecasting , 2004, Appl. Soft Comput..

[5]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[6]  Jianfeng Feng,et al.  Training integrate-and-fire neurons with the Informax principle II , 2003, IEEE Trans. Neural Networks.

[7]  Jianfeng Feng.  Neuronal Models with Current Inputs , 2001, IWANN.

[8]  Bedrich J. Hosticka,et al.  Biologically-inspired artificial neurons: modeling and applications , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[9]  Prem Kalra,et al.  Classification using single neuron , 2003, IEEE International Conference on Industrial Informatics, 2003. INDIN 2003. Proceedings..

[10]  Joel L. Davis,et al.  Single neuron computation , 1992 .

[11]  Andrew D. Back,et al.  A spiking neural network architecture for nonlinear function approximation , 2001, Neural Networks.

[12]  W. Gerstner,et al.  Time structure of the activity in neural network models. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  Shih-Chii Liu,et al.  Temporal coding in a silicon network of integrate-and-fire neurons , 2004, IEEE Transactions on Neural Networks.

[14]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[15]  S. Kaplan.  The Physiology of Thought , 1950 .

[16]  Christof Koch,et al.  Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series) , 1998 .

[17]  T. Poggio,et al.  Multiplying with synapses and neurons , 1992 .

[18]  A. Kendon,et al.  Organization of behavior in face-to-face interaction , 1975 .

[19]  Vivien A. Casagrande,et al.  Biophysics of Computation: Information Processing in Single Neurons , 1999 .

[20]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[21]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[22]  A. Hodgkin.  The local electric changes associated with repetitive action in a non‐medullated axon , 1948, The Journal of physiology.

[23]  Yogesh Singh,et al.  Feedforward sigmoidal networks - equicontinuity and fault-tolerance properties , 2004, IEEE Transactions on Neural Networks.

[24]  Jianfeng Feng,et al.  Training the integrate-and-fire model with the informax principle: I , 2002 .

[25]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[26]  Loon Ching Tang,et al.  Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..

[27]  Sanjay V. Dudul,et al.  Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network , 2005, Appl. Soft Comput..