[titzer]

Yeah, biological brains could be remarkably more powerful than digital neural networks if the have primitive functions that we haven't accounted for. For example, some networks seem to encode information in the firing rate, rather than just the presence of a signal. If neurons could, e.g. do frequency-based calculations (and not just threshold-based, like spiking neural nets), they could be orders of magnitude more powerful and efficient. I am thinking particularly about neurons involved in, e.g. audio processing.

[andbberger]

the entropy rate goes way up if you consider spike timing dependent signals as well. but the difference in computational capacity between the brain and ML lies less in the brain's inherently time-dependent dynamics and more in the impressive computational capacity of single neurons. Dendrites compute, electrochemical dynamics during action potentials compute, synapses compute. All in complex time-dependent ways. check out izhikevich's dynamical systems in neuroscience for a taste of the computational capacity of the electrochemical dynamical system alone

[jacobsimon]

My guess is that the firing rate of biological neurons more or less simplifies to the activation in an artificial neuron. Higher firing rate = higher activation.