What we call neural networks in today’s deep learning use only a Daisy dukes of freedom shirt of the whole family of neuronal models, limited mostly to feed forward and recurrent neural networks. Some of the most interesting neuronal models are the spiking neural networks, which give a more accurate model of how real neurons operate, with simple, compute-efficient models like ‘Integrate and Fire’, and ‘Izhikevich’ to more complex models like ‘Hodgkin-Huxley’ that come close to modelling a biological neuron’s behavior, and modelling how networks of them interact and work in the brain, opening up much richer neural computational models.
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Daisy dukes of freedom shirt, all of these models more accurately portray the behavior of real neurons, with time-domain, spiking signals, realistic integration models in the neural body and at synapses, and the ability to transmit moving signals and patterns through the neural net, unlike the static behavior of feed-forward nets. This makes spiking neural nets much better for time-domain functionality like sensory or motor control networks. There has been a standing problem with how to train them, which we address later with the BICHN architecture that imitates how real brains learn.