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Titlebook: Artificial Neural Networks — ICANN ’97; 7th International Co Wulfram Gerstner,Alain Germond,Jean-Daniel Nicoud Conference proceedings 1997

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How a single Purkinje cell could learn the adaptive timing of the classically conditioned eye-blink responses (CRs) for delay conditioned ISIs between 200 and 1000 msec. Modification of parts of the intracellular signalling network might represent a general mechanism for neurons to learn the timing between input and output.
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0302-9743 , Switzerland,in October 1997. The 201 revised papers presented were selected from a large number of submissions and give a unique documentation of the state of the art in the area. The papers are organized in parts on coding and learning in biology; cortical maps and receptive fields; learning: the
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https://doi.org/10.1007/978-1-4613-0079-3the prediction of reinforcing events as teaching signals. Independent of the theoretical work, neuophysiological experiments have revealed that neurons in the mammalian midbrain using the neurotransmitter dopamine process information about rewards and reward-predicting stimuli in a very similar manner as the teaching signal of TD models.
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https://doi.org/10.1007/978-3-319-90463-4t damped oscillations with a frequency which depends on synaptic time constants. Individual CCs are shown to depend weakly on synaptic connectivity. They depend more strongly on the firing rates of individual neurons.
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Reward responses of dopamine neurons: A biological reinforcement signal,the prediction of reinforcing events as teaching signals. Independent of the theoretical work, neuophysiological experiments have revealed that neurons in the mammalian midbrain using the neurotransmitter dopamine process information about rewards and reward-predicting stimuli in a very similar manner as the teaching signal of TD models.
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Cross-correlations in sparsely connected recurrent networks of spiking neurons,t damped oscillations with a frequency which depends on synaptic time constants. Individual CCs are shown to depend weakly on synaptic connectivity. They depend more strongly on the firing rates of individual neurons.
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Spatio-temporal pattern recognition with neural networks: Application to speech,ructure representation of speech for speech analysis and recognition. We give examples of neural networks architecture and applications that are designed to take into account the time structure of the process to be analysed.
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