Abstract
Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hutter, M.: Universal algorithmic intelligence: A mathematical top-down approach. In: Goertzel, B., Pennachin, C. (eds.) Artificial General Intelligence, pp. 227–290. Springer (2007)
Koene, R.: Fundamentals of whole brain emulation: State, transition and update representations. International Journal of Machine Consciousness 4(1) (2012)
Koene, R.: A window of opportunity. H+ Magazine, Based on the TEDxTallinn 2012 talk (2012), http://www.carboncopies.org/a-window-of-opportunity
Ljung, L.: Perspectives on system identification. Plenary talk at the Proceedings of the 17th IFAC World Congress, Seoul, South Korea (2008)
Rieke, F., Warland, D., de Ruyter van Steveninck, R., Bialek, W.: Spikes: Exploring the Neural Code. MIT Press, Cambridge (1997)
Hampson, R., Gerhardt, G., Marmarelis, V., Song, D., Opris, I., Santos, L., Berger, T., Deadwyler, S.: Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. Journal of Neural Engineering 9 (2012), doi:10.1088/1741–2560/9/5/056012
Bi, G., Poo, M.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18(24), 10464–10472 (1998)
Berger, T., Ahuja, A., Courellis, S., Deadwyler, S., Erinjippurath, G., Gerhardt, G., Gholmieh, G., Granacki, J., Hampson, R., Hsaio, M., Lacoss, J., Marmarelis, V., Nasiatka, P., Srinivasan, V., Song, D., Tanguay, A., Wills, J.: Restoring lost cognitive function. IEEE Engineering in Medicine and Biology 24(5), 30–44 (2005)
Lazar, A., Slutskiy, Y.: Channel identification machines. Computational Intelligence and Neuroscience (in press, 2012)
Seung, S.: CONNECTOME: How the Brain’s Wiring Makes Us Who We Are. Houghton Mifflin Harcourt (2012)
Briggman, K., Helmstaedter, M., Denk, W.: Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011)
Bock, D., Lee, W.C.A., Kerlin, A., Andermann, M., Hood, G., Wetzel, A., Yurgenson, S., Soucy, E., Kim, H., Reid, R.: Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011)
Zador, A.: Sequencing the connectome: A fundamentally new way of determining the brain’s wiring diagram. Technical report, Project Proposal, Paul G. Allen Foundation Awards Grants (2011)
Kording, K.: Of toasters and molecular ticker tapes. PLoS Computational Biology 7(12), e1002291 (2011), doi:10.1371/journal.cpbi.1002291
Anastassiou, C.A., Perin, R., Markram, H., Koch, C.: Ephaptic coupling of cortical neurons. Nature Neuroscience 14(2), 217 (2012)
Koene, R., Hasselmo, M.: An integrate and fire model of prefrontal cortex neuronal activity during performance of goal-directed decision making. Cerebral Cortex 15(12), 1964–1981 (2005) (Advanced Access published on April 27, 2005)
Koene, R.: Functional requirements determine relevant ingredients to model for on-line acquisition of context dependent memory. PhD thesis, Department of Psychology, McGill University, Montreal, Canada (2001)
Koene, R., Tijms, B., van Hees, P., Postma, F., de Ridder, A., Ramakers, G., van Pelt, J., van Ooyen, A.: NETMORPH: A framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics 7(3), 195–210 (2009), doi:10.1007/s12021–009–9052–3 (Published online: August 12, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koene, R.A. (2012). Toward Tractable AGI: Challenges for System Identification in Neural Circuitry. In: Bach, J., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2012. Lecture Notes in Computer Science(), vol 7716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35506-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-35506-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35505-9
Online ISBN: 978-3-642-35506-6
eBook Packages: Computer ScienceComputer Science (R0)