I am starting this Fall 2015 as a new faculty member in Cognitive Science at Indiana University. You can find out more here: http://mypage.iu.edu/~edizquie/
Currently preparing for this.
Paper accepted to the Journal of Neuroscience.
Abstract: Chemotaxis during sinusoidal locomotion in nematodes captures in simplified form the general problem of how dynamical interactions between the nervous system, body, and environment are exploited in the generation of adaptive behavior. We used an evolutionary algorithm to generate neural networks that exhibit klinotaxis, a common form of chemotaxis in which the direction of locomotion in a chemical gradient closely follows the line of steepest ascent. Sensory inputs and motor outputs of the model networks were constrained to match the inputs and outputs of the C. elegans klinotaxis network. We found that a minimalistic neural network, comprised of an ON- OFF pair of chemosensory neurons and a pair of neck muscle motor neurons, is sufficient to generate realistic klinotaxis behavior. Importantly, emergent properties of model networks reproduced two key experimental observations that they were not designed to fit, suggesting that the model may be operating according to principles similar to those of the biological network. A dynamical systems analysis of 77 evolved networks revealed a novel neural mechanism for spatial orientation behavior. This mechanism provides a testable hypothesis that is likely to accelerate the discovery and analysis of the biological circuitry for chemotaxis in C. elegans.
Imagine you are the editor of two journals, one with higher impact than the other. You receive two modeling papers, one with more predictions than validations and vice-versa. You can only accept one to each journal. Where would you send each?
What I’m asking is: How do you gauge the importance of a data-driven* model? By the number of predictions (including the proposal of novel experiments) that it makes or by the number of existing experiments that it agrees with? If both, then how would you weight each different types of contributions?
* I specify data-driven models to separate them from theory-driven models.
Abbott, L.F. (2008) Theoretical Neuroscience Rising. Neuron 60:489-495.
Key passages from my favorite parts of the paper:
“Neuroscience has always had models, but prior to the invasion of the theorists, these were often word models. Equations force a model to be precise, complete, and self-consistent, and they allow its full implications to be worked out.”
“A skillful theoretician can formulate, explore, and often reject models at a pace that no experimental program can match. This is a major role of theory – to generate and vet ideas prior to full experimental testing. [….] Is the theorist’s job to develop, test, frequently reject, and sometimes promote new ideas. […] (to) provide valuable new ways of thinking. ”
“Identifying the minimum set of features needed to account for a particular phenomenon and describing these accurately enough to do the job is a key component of model building. […] The truly realistic model is as impossible and useless a concept as Borges’ ‘map of the empire that was of the same scale as the empire and that coincided with it point for point’”
And curiously, with regards to the ‘future’: “Learning is widely considered a job for the synapse.” and “We (…) commonly tend to think of synapses as the focus of learning and memory, and neurons as the workhorses of dynamic computation. This may be radically wrong.”
Hmn.. Is he honestly not aware of the existence of the works below providing proofs of principle of how radically wrong it could be? Or is he choosing to ignore them? And if so, it would be interesting to know why exactly.
– Yamauchi, B. and Beer, R.D. (1994). Sequential behavior and learning in evolved dynamical neural networks.
– Tuci, E., Quinn, M. and Harvey, I. (2003): An evolutionary ecological approach to the study of learning behaviour using a robot based model.
– Izquierdo, E. and Harvey, I. (2007): Hebbian Learning using Fixed Weight Evolved Dynamical ‘Neural’ Networks.
– Phattanasri, P., Chiel, H.J. and Beer, R.D. (2007). The dynamics of associative learning in evolved model circuits.
– Izquierdo, E., Harvey, I. and Beer, R.D. (2008). Associative learning on a continuum in evolved dynamical neural networks.
But perhaps more importantly, these suggests that the project of synaptic plasticity and leaning behavior has to reach a high impact journal where it can be read more widely and take a shape that biologists won’t so easily continue to ignore – this I am working on.
The other point that completely escapes this theoretical neuroscience review is the role that environmental feedback and the biomechanics of the body play on behavior. Not a brief mention, not even for the future! I guess this is exciting.