All the techniques described in this book use rate-based coding. That is, they assume that the important dynamic property is the level of activation or, in biological terms, the neuron's firing rate. Biological evidence shows that correlations in spike timing is also very important for both the network's behaviour and for learning (called "spike-timing-dependent plasticity").
Some people have started developing computer models of this property, but unfortunately it's not a widely known research topic. One paper I've read is Gerstner et al. (1999) in which they describe a model of unsupervised learning. Gerstner also has a book called Spiking Neuron Models, which is available online, that goes into a lot of detail on the topic. Other people have done supervised learning by evolving network topologies using genetic algorithms.
The advantage of spike-based models is that they they also seem to scale better to larger networks, and have greater power for networks of similar complexity.
I'm quite optimistic about this field of research. Neural network research has seemed to become stagnant recently for some reason, but I think switching to spike-based models is the way out of that.
Some people have started developing computer models of this property, but unfortunately it's not a widely known research topic. One paper I've read is Gerstner et al. (1999) in which they describe a model of unsupervised learning. Gerstner also has a book called Spiking Neuron Models, which is available online, that goes into a lot of detail on the topic. Other people have done supervised learning by evolving network topologies using genetic algorithms.
The advantage of spike-based models is that they they also seem to scale better to larger networks, and have greater power for networks of similar complexity.
I'm quite optimistic about this field of research. Neural network research has seemed to become stagnant recently for some reason, but I think switching to spike-based models is the way out of that.