Design, Analysis and Validation of Biologically PlausibleComputational Models (Funded by the NSF)
Principle Investigator: Jose Principe and the CNEL Laboratory
Co-Investigator: Thomas DeMarse
Project Summary
One of the fundamental difficulties in studying brain
function is how to abstract the computation and model the
dynamical behavior of trillions of neurons. This proposal
puts together a synergistic effort to advance the theory of
biological plausible computation away from the local
minimum created by modeling both the neuron as a static
processing element (the McCulloch and Pitts model) and
neural assemblies as dynamical systems with very
restrictive dynamics. We seek more powerful computational
models, which will exploit the time dimension in a more
principled and productive manner. We center our studies in
the recently proposed liquid state machine (LSM), which
embodies computation principles very different from Von
Neumann machines. LSMs are built from a recurrent neural
network core (the liquid state (LS)) with nonconvergent
dynamics combined with a static mapper readout, and have
been shown to be universal computing machines in myopic
functional spaces.
In this proposal we establish a plan to further test the
liquid state machine principles by replacing the artificial
recurrent neural network part of the LSM with a living
neural network in the form of a dissociated neuron culture
(DCT), effectively creating a real time hybrid
(artificial-biological) computer. Presently, DCTs appear as
the most realistic and efficient test bed to the
theoretical predictions obtained with the LSM because of
the full control of inputs and extensive probing of
neurons. A set of well-defined questions was established to
compare the properties of the LSM with those of a
population of neurons grown in culture on a multi-electrode
array. At the same time we plan to enhance our
understanding of the liquid state properties advancing a
more tight connection with the theory of dynamical systems.
Specifically, we propose to integrate the LSM model with
our studies of computation in living neural networks into a
system where both model and empirical data from cultured
neural networks can be systematically investigated to
improve our understanding of neural computation, and
determine the computational capabilities of DCTs.
20 Node Xserve Cluster
References for other models of these or similar networks:
Chao, Z. C., Bakkum, D. J., Wagenaar, D. A., & Potter, S. M. (2005). Effects of random external background stimulation on network synaptic stability after tetanization: a modeling study. Neuroinformatics, 3(3), 263-280.
Netoff, T. I., Clewley, R., Arno, S., Keck, T., & White, J. A. (2004). Epilepsy in small-world networks. J Neurosci, 24(37), 8075-8083.
Persi, E., Horn, D., Volman, V., Segev, R., & Ben-Jacob, E. (2004). Modeling of synchronized bursting events: the importance on inhomogeneity. Neural Comput, 16(12), 2577-2595.
Segev, R., Shapira, Y., Benveniste, M., & Ben-Jacob, E. (2001). Observations and modeling of synchronized bursting in two-dimensional neural networks. Physical Review E, 6401(1), -.
Volman, V., Baruchi, I., Persi, E., & Ben-Jacob, E. (2004). Generative modelling of regulated dynamical behavior in cultured neuronal networks. In Physica a-Statistical Mechanics and Its Applications (Vol. 335, pp. 249-278).