(p)reprints and WWW-site on olfactory modelling

[Rainer Malaka]

Dear connectionists,

several re- and preprints on olfactory modelling, classical conditioning and spiking 
neural networks are available on our WWW-server:

	http://i11www.ira.uka.de:80/~malaka/publications.html

including the following papers:

 R. Malaka Dynamical odor coding in a model of the antennal lobe.  
    In Proceedings of the International Conference on Artificial Neural Networks (ICANN`95), 
    Paris , volume 2
    Abstract:
   	A model for the  insect antennal  lobe is presented. The model is  embedded into 
	a framework covering  chemosensory input and associative learning  of odors. The 
	resulting  dynamic representation of odors  in spatio-temporal activity patterns 
	corresponds to response patterns observed in the generalistic olfactory systems. 
	We discuss the meaning of symmetrical  and asymmetrical connections and temporal 
	coding for classical conditioning, and demonstrate, that non-converging activity 
	patterns can be learned and discriminated.

 R. Malaka, M. Hammer (1996), Real-time models of classical conditioning. 
    Submitted to the ICNN`96 conference, Washington
    Abstract:	
	Real-time  models of classical  conditioning  simulate features  of associative 
	learning  including  its dependence  on the timing  of stimuli.  We present the 
	Sutton/Barto model, the TD model, the CP model,  the drive-reinforcement model,
	and the  SOP model in a framework of reinforcement learning rules.  The role of 
	eligibility  and  reinforcement  is analyzed  and the ability  of the models to 
 	simulate  time-dependent  learning (e.g. inhibitory  backward conditioning) and 
	other  conditioning phenomena  is compared. A new model is  introduced, that is 
	mathematically simple, and overcomes weaknesses of the other models. This model 
	combines the two antagonistic US traces of the SOP model with the reinforcement 
	term of the TD model.

 R. Malaka, T. Ragg, M. Hammer (1995) A Model for Chemosensory Reception, 
    In G. Tesauro, D.S. Touretzky, T.K. Leen (eds), Advances in Neural Information 
    Processing Systems, Vol. 7
    Abstract:
	A new model for chemosensory reception is presented. It models reactions between 
	odor molecules and receptor proteins  and the activation of  second messenger by 
	receptor   proteins.  The mathematical formulation  of the  reaction kinetics is 
	transformed into an artificial neural network (ANN).  The resulting feed-forward 
	network  provides  a powerful means for parameter  fitting by  applying learning 
	algorithms.  The weights  of the network  corresponding  to chemical  parameters 
	can be  trained by presenting experimental data.  We demonstrate  the simulation 
	capabilities  of the model with  experimental data from  honey bee  chemosensory 
	neurons.  It can be  shown that our model is sufficient  to rebuild the observed 
	data and that simpler models are not able to do this task.

 R. Malaka, U. Koelsch (1994) Pattern Segmentation in Recurrent Networks of Biologically 
    Plausible Neural Elements. In Intelligent Engineering Systems Through Artificial 
    Neural Networks, Vol. 4
    Abstract:
	We introduce a neural network model using spiking neurons. The neuron model is a
 	biological neuron with Hodgkin-Huxley channels. We compare the network's ability 
	of auto-associative  pattern  recognition with to that of the  Hopfield network. 
	The  model  recognizes  patterns by  converging  into dynamic  stable  states of 
	synchonous firing activity.  This activity can last for arbitrary time or return 
	to a  resting  activation  after  stimulus  offset.  If one  presents  overlayed 
	patterns to the  network,  the network is  able to  separate the components. The 
	single components are encoded by synchronous firing patterns.

and some others.

Yours,

Rainer Malaka

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Rainer Malaka                         /|    phone: (+49) (721) 608-4212
Universitaet Karlsruhe               | | /| fax  : (+49) (721) 608-4211
Institut fuer Logik, Komplexitaet  /||/ | | csnet: malaka at ira.uka.de
und Deduktionssysteme             | | /||/  
P.O.-Box 6980                     |/ | |    WWW  :
D-76128 Karlsruhe, Germany           |/     http://i11www.ira.uka.de/~malaka/
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