Does Artificial Intelligence Elegantly Worm its Way into the Study of Cognition ? II (Pun Intended)

How can robotics and AI help us to understand neural and cognitive processes? On the simplest scale some researchers have succeeded in modeling an organism or part of its neural functional system in great detail.
Cangelosi & Parisi (1997) for example created a computational model that simulated the neural circuit for touch sensitivity in Caenorhabditis elegans, a roundworm of about 1mm length.
The complete neural circuit of adult C. elegans consists of only 302 Neurons (compared to 10¹⁰ neurons in the adult human brain) (Chalfie et al. 1985). Interestingly, even in C. elegans there is a neuronal Left/Right functional asymmetry, suggesting the

“possibility that neural asymmetries observed across the animal kingdom are similarly established by very early embryonic interactions” (Poole & Hobert 2006: 2279).

Cangelosi and Parisi tried to “reproduce the nematode’s withdrawal response to touch in the head or tail regions” (Cangelosi & Parisi 1997), whose underlying neural circuit consists of 85 neurons (Chalfie et al. 1985). Not only did they succeed in creating a working computational model, but when they ‘knocked out’ single neurons of the model, the whole system behaved in a way similar to a nematode in which equivalent cells had been killed by laser beams (I hope Zombie-Aliens never do similar experiments when investigating human culture, like

Zombie-Scientist A: “What’s this 'Train Station'-thing good for, anyway?”
Zombie-Scientist B “I dunno, let’s do some ‘laser ablation’ and find out what happens”)

The crucial point is, of course, the word ‘similar’, and Cangelosi and Parisi conclude that:

“The presence of some non-matching data between the real neural circuit and artificial neural networks indicate that the model needs adjustment.”

They throw in another caveat, namely that

“the fact a computational model replicates the behavior of a real organism is only a first proof of its validity. There must be agreement between the computational model and the real organism both in what the model/organism does and in how the model/organism does it. That is, a good computational model must reflect the same mechanisms and processes present in the real organism” (Cangelosi & Parisi 1997: 95)

By now C.elegans is one of the best-studied multicellular organisms. Due to its relatively simple structure it seems to be a very fitting candidate for computational studies, and several computer models of it have been proposed so far (Suzuki et al. 2005) (At Keiko University in Japan, for example, there even is a Cybernetic Caenorhabditis elegans Project). At present, these computer models can be divided in three groups:

1. Those simulating mechanisms of stimulus-reception and -processing
2. Those generating motor-coordination patterns and movement
3. Those integrating these approaches to build a simplified model of “the flow series from reception of the stimulation to motion generation in” (Suzuki et al. 2005)

Examples of an advanced stage of the third group is the work if Michiyo Suzuki and his colleagues’, who succeeded in building a virtual C. elegans, consistsing of a “a neuronal circuit model for motor control that responds to touch stimuli and a kinematic model of the body for movement” integrated into a whole body model. (Suzuki et al. 2005).
Although even studies on the simple scales like that of C. elegans are still developing, it seems possible to achieve highly precise approximations between the behaviors of artificial and real organisms. (Suzuki et al. 2005) Thus, in the future it may even be able to simulate more complex neural or even cognitive mechanisms, which, for example, is the final aim of the “Blue Brain Project”, “the first comprehensive attempt to reverse-engineer the mammalian brain, in order to understand brain function and dysfunction through detailed simulations.”

In 2006, the project succeded in building a model of the somatosensory neocortex of 2-week-old rat at the cellular level (i.e. disregarding genetic and molecular levels),with about 10,000 neurons forming a neocortical column, that is, a recurring network unit of the brain. However, “Computational power needs to increase about 1-million-fold before we will be able to simulate the human brain, with 100 billion neurons.” (Markram 2006).

References:

Cangelosi, Angelo and Domenico Parisi. 1999. “A Neural Network Model ofn s: The Circuit of Touch Sensitivity.” Neural Processing Letters 6: 91–98, 1997

Chalfie, M., J.E. Sulston, J.C. White, E. Southgate, J.N. Thomson and S. Brenner. 1985. “The neural circuit for touch sensitivity in Caenorhabditis elegans”, Journal of Neuroscience, 5:959– 964.

Poole, Richard J. and Oliver Hobert. 2006. “Early Embryonic Programming of Neuronal Left/Right Asymmetry in C. elegans.” Current Biology 16, 2279–2292

Suzuki, Michiyo, Toshio Tsuji and Hisao Ohtake. 2005. “A model of motor control of the nematode C. elegans with neuronal circuits” Artificial Intelligence in Medicine 35: 75—86

Markram, Henry. 2006. ""The Blue Brain Project." Nature Reviews Neuroscience 7: 153-160.