It is possible, but the neurons are actually complex computing units that take a plethora of signals into account: subtle temporal behavior (relative timings of post and pre-synaptic activations), complex chemistry in the cell as well as the in the synaptic cleft, and many more less understood things.
Secondly, as you can imagine, the connectivity is far from random. Like in larger nervous systems (or even more than in large pools or neurons), these computing units take precise roles, part due to the developmental process, part due to later stage learning. But in the end, the machinery is precise, and simulating it requires understanding all of it.
I believe that this understanding should not come though imagery alone, but also through studies of the development of these organisms' brains. Like we are now forming artificial neural maps through "machine" learning techniques that are accelerated versions of their biological counterparts, I believe that we should develop developmental algorithms into large-scale simulations. The goals being to first understand how it works, second to see if the models are able to replicate the end result, a good indication that they are useful models.
Jefe: "It is possible, but the neurons are actually complex computing units that take a plethora of signals into account: subtle temporal behavior, complex chemistry in the cell as well as in the synaptic cleft, and many more less understood things. Secondly, as you can imagine, the connectivity is far from random. Like in larger nervous systems, these computing units take precise roles, part due to the developmental process, part due to later stage learning. "
El Guapo: "Would you say, senor, that I have a plethora of signals in each computing unit? And I just would like to know if you know what a plethora is. I would not like to think that a person would tell someone he has a plethora, and then find out that that person has no idea what it means to have a plethora."
Jefe: "Forgive me, El Guapo. I know that I, Jefe, do not have your superior intellect and education. But could it be that once again, you are angry at something else, and are looking to take it out on me?"
"But in the end, the machinery is precise, and simulating it requires understanding all of it."
Sorry to pick at you for this, but how can you be certain of the many claims you've made here? How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
(Did I misuse "plethora"? English is not my first language, sorry if I shouldn't use plethora)
> How do you know that intelligence cannot be implemented in a mechanism simpler than the biological hardware implementation or simulations thereof?
I don't know if intelligence cannot be implemented in a simpler way (I certainly hope it can), I am concerned here about understanding the biological implementation of intelligence. I say that simulations are useful because the models that we may make of the emergence of the mechanisms of intelligence in networks of neurons would be very hard to understand without being implemented and simulated.
However, if you are interested in the artificial implementation of the intelligent behavior of some organisms, you could maybe still benefit from understanding how this intelligent behavior arises in these organisms, and take inspiration from these mechanisms.
> Sorry to pick at you for this, but how can you be certain of the many claims you've made here?
I didn't make revolutionary claims regarding the functioning of biological neural networks. The fact that neurons are sensitive to relative spike timing is evident in mechanisms like spike timing dependent plasticity (http://www.scholarpedia.org/article/Spike-timing_dependent_p...). The "complex chemistry" that I mention is behind cognition is evident if you open any neurology book, for instance Principles of Neural Science by Kandel.
Then, I say that simulation (and in particular simulation of development as well as learning) is the right road to understanding biological neural systems, but I took care to mention that it is what I believe, not what I know.
Your use of plethora was completely right. For some reason giardini thinks it is a very obscure and "intellectual" word but I disagree. In Google's English corpus it is used about 1/3 as often as the word "neuron". http://books.google.com/ngrams/graph?content=plethora%2Cneur...
how can you be certain of the many claims you've made here?
It says most of this in the article. e.g. "It turns out that social behavior in the worm is controlled by a pair of neurons called RMG. The two RMG neurons receive input from various sensory neurons that detect the several environmental cues that make worms aggregate. RMG integrates this information and sends signals to the worm’s muscles." You have to understand genetics, smells, sensors, neurons, and muscles before you can explain why worms in nature tend to be together, but worms in the lab stay by themselves.
I believe that this understanding should not come though imagery alone, but also through studies of the development of these organisms' brains. Like we are now forming artificial neural maps through "machine" learning techniques that are accelerated versions of their biological counterparts, I believe that we should develop developmental algorithms into large-scale simulations. The goals being to first understand how it works, second to see if the models are able to replicate the end result, a good indication that they are useful models.
(I do research in computational neuroscience.)