Complexity of learning for networks of spiking neurons with nonlinear synaptic interactions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2130, Issue , 2001, Pages 247-252

  Schmitt, Michael ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS;

MODEL NETWORKS; NONLINEAR FUNCTIONS; SPIKING NEURON; SYNAPTIC INPUT; SYNAPTIC INTERACTIONS; UPPER BOUND; VC DIMENSION;

NEURAL NETWORKS;

EID: 84958955636     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_35     Document Type: Conference Paper

References (6)

1. M. Anthony and P. L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge, 1999.
2. W. Gerstner. Spiking neurons. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 1, pages 3–53. MIT Press, Cambridge, Mass., 1999.
3. C. Koch. Biophysics of Computation. Oxford University Press, New York, 1999.
4. W. Maass. Computing with spiking neurons. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 2, pages 55–85. MIT Press, Cambridge, Mass., 1999.
5. W. Maass and M. Schmitt. On the complexity of learning for spiking neurons with temporal coding. Information and Computation, 153:26–46, 1999.
6. A. F. Murray. Pulse-based computation in VLSI neural networks. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 3, pages 87–109. MIT Press, Cambridge, Mass., 1999.