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Understanding Amygdala :
Supporting research
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The following scientific research papers have proven helpful
in the conception and design of Amygdala.
They are listed here as a reference for further design work
and to provide background material for those interested
in learning about the details of Amygdala's theoretical underpinnings.
More papers and links will be added to this list from time to time.
If you know of a relevant paper that is not listed here,
feel free to contact the Amygdala team about it.
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Kinetic synapse models
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- An efficient method for computing synaptic conductances based on a kinetic model of receptor binding
Alain Destexhe, Zachary F. Mainen and Terrence J. Sejnowski
Neural Computation 6: 14-18, 1994
- Kinetic models of synaptic transmission
Alain Destexhe, Zachary F. Mainen and Terrence J. Sejnowski
In: Methods in Neuronal Modeling , 2nd Edition, Edited by Koch, C.
and Segev, I., MIT Press, Cambridge, MA, 1997 (in press)
- Conductance-based integrate and fire models
Alain Destexhe
Neural Computation 9: 503-514, 1997
- Fast Calculation of Short-Term Depressing Synaptic Conductances
Michele Giugliano, Marco Bove, Massimo Grattarola
Neural Computation 11, 1413-1426 (1999)
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Genetic algorithms / evolutionary computing
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- Genetic Algorithm for Artificial Neurogenesis
Manuel Clergue, Philippe Collard
Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, 1998, pages 411-416
- Development and Evolution of Neural Networks in
an Artificial Chemistry
Jens C. Astor, Christoph Adami
Proc. of 3rd German Workshop on Artificial Life,
C. Wilke, S. Altmeyer, and T. Martinetz, eds., Verlag Harri Deutsch (1998) p. 15-29
- A Simple Model of Neurogenesis and Cell
Differentiation based on Evolutionary Large-Scale Chaos
Hiroaki Kitano
Artificial Life, 2(1):79-99, 1995
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Learning in spiking neurons
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- Self-Organization of Spiking Neurons Using
Action Potential Timing
Berthold Ruf, Michael Schmitt
IEEE Transactions on Neural Networks, 9(3):575--578
- On the Complexity of Learning
for Spiking Neurons with Temporal Coding
Wolfgang Maass, Michael Schmitt
Information and Computation, 153:26--46, 1999
- Hebbian Learning in Networks of
Spiking Neurons Using Temporal Coding
Berthold Ruf, Michael Schmitt
Proc. of the Int. Work-Conference on Artificial and Natural Neural Networks IWANN'97,
Lecture Notes in Computer Science, vol. 1240, 380-389, Springer, Berlin, 1997
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Miscellaneous
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- Efficient event-driven simulation of
large networks of spiking neurons and dynamical synapses
M. Mattia and P. Del Giudice
Neural Computation, 12, 2305-2330, 2000
- The Computational Power of Spiking Neurons Depends
on the Shape of the Postsynaptic Potentials
Wolfgang Maass, Berthold Ruf
Electronic Colloquium on Computational Complexity (ECCC) 1996
- Cell Assemblies, Associative Memory
and Temporal Structure in Brain Signals
Thomas Wennekers, Günther Palm
Miller, R. (ed.) Time and the Brain. Series: CABR: Conceptual Advances in Brain Research, vol. 3, 251-273, Harwood Academic Publishers, 2000
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