Psych 465, Spring 2008: COMPUTING IN BRAINS — readings

In this course, we will integrate computational modeling with known neurophysiology as it applies to large nervous systems. In some cases, the relationship of present modeling is direct (Themes I-III); or the biology needs more computational instruction (IV); or the computation is interesting but the application obscure (V). We will spend 2-3 weeks on each subject, with half of the first sessions of each devoted to a basic overview of the relevant computational principles and neuroanatomy/physiology in each case. We encourage you to take this course if you have prior experience in either computation or neurobiology: the point of the course is making the links.

theme # heading example systems

THEME I FEEDFORWARD COMPUTATION:
Bayesian inference and decision making Multisensory integration; visuomotor decision-making

THEME II SEQUENCE REPRESENTATION AND PROCESSING Birdsong; applications to language

THEME III INFORMATION INTEGRATION AND CONTROL:
Parallel computations Complementary roles of cortex, basal ganglia, hippocampus and cerebellum

THEME IV HIGHLY DISTRIBUTED SYSTEMS Integration of innate and learned patterns in amygdala; gating in attachment systems; semantic knowledge

THEME V OPEN-ENDED COMPUTATION to be determined

theme #	heading	example systems
THEME I	FEEDFORWARD COMPUTATION: Bayesian inference and decision making	Multisensory integration; visuomotor decision-making
THEME II	SEQUENCE REPRESENTATION AND PROCESSING	Birdsong; applications to language
THEME III	INFORMATION INTEGRATION AND CONTROL: Parallel computations	Complementary roles of cortex, basal ganglia, hippocampus and cerebellum
THEME IV	HIGHLY DISTRIBUTED SYSTEMS	Integration of innate and learned patterns in amygdala; gating in attachment systems; semantic knowledge
THEME V	OPEN-ENDED COMPUTATION	to be determined

THEME I: FEEDFORWARD COMPUTATION

THEORY AND MODELING

*The Computational Brain, ch.3, P. S. Churchland and T. J. Sejnowski, MIT Press (1992)
*Technical Introduction: A primer on probabilistic inference, T. L. Griffiths and A. Yuille, supplementary material to a special issue of Trends in Cognitive Sciences on Bayesian cognition (July 2006)
*The Bayesian brain: the role of uncertainty in neural coding and computation, D. C. Knill and A. Pouget, Trends in Neurosciences 27:712-719 (2004)
[optional: *Bayesian Rationality, M. Oaksford and N. Chater, Oxford University Press, chapters 1-4; alternatively, precis for BBS which is expected shortly.]
*Bayesian Spiking Neurons I: Inference, and [optional] *II: Learning, S. Deneve, Neural Computation 20:91-117 and 118-145 (2008)

EXAMPLE SYSTEMS

MULTISENSORY INTEGRATION

*Enhancement of perceived visual intensity by auditory stimuli: A psychophysical analysis, B. E. Stein et al., Journal of Cognitive Neuroscience 8(6): 497-506 (1996)
*Using Bayes' Rule to Model Multisensory Enhancement in the Superior Colliculus, T. J. Anastasio, P. E. Patton, and K. Belkacem-Boussaid, Neural Computation 12:1165-1187 (2000). [OPTIONAL]
*Occipital transcranial magnetic stimulation has opposing effects on visual and auditory stimulus detection: Implications for multisensory interactions, V. Romei et al., Journal of Neuroscience 27(43):11465-11472 (2007)

NEUROPHYSIOLOGY OF OCULOMOTOR DECISIONS

*Exploring the neurophysiology of decisions, M. I. Leon and M. N. Shadlen, Neuron 21(4):669-672 (1998)
*The dynamics of memory as a consequence of optimal adaptation to a changing body, Kording, K. P., J. B. Tenenbaum, et al., Nat. Neurosci. 10(6):779-86 (2007)

THEME II: SEQUENCE REPRESENTATION AND PROCESSING

THEORY AND MODELING

*Temporal sequence learning, prediction, and control: a review of different models and their relation to biological mechanisms, F. Worgotter and B. Porr, Neural Computation 17:245-319 (2005)
*Polychronization: computation with spikes, E. M. Izhikevich, Neural Computation 18:245-282 (2006)
*Development of neural circuitry for precise temporal sequences through spontaneous activity, axon remodeling, and synaptic plasticity, J. K. Jun and D. Z. Jin, PLoS One 8:e723 (2007)

EXAMPLE SYSTEMS

BIRDSONG

*The evolution of vocal learning systems in birds, M. A. Farries and D. J. Perkel, in The Evolution of Nervous Systems V.II. Non-mammalian vertebrates, J. H. Kaas ed., Academic Press
*Temporal hierarchical control of singing in birds, A. C. Yu and D. Margoliash, Science 273:1871-1875 (1996)
*Model of song selectivity and sequence generation in area HVc of the songbird, P. J. Drew and L. F. Abbott, J Neurophys 89:2697-2706 (2003)

CORTICAL ORGANIZATION AND LANGUAGE

*Synfire chains and cortical songs: temporal modules of cortical activity, Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster, and R. Yuste, Science 304:559-564 (2004)
*From sensorimotor sequence to grammatical construction: evidence from simulation and neurophysiology, P. F. Dominey, Adaptive Behavior 13:347-361 (2005)

BINDING AND SYNCHRONY

*Neuronal synchrony: A versatile code for the definition of relations?, W. Singer, Neuron 24: 49-65 (1999)
*Synchrony unbound: a critical evaluation of the temporal binding hypothesis, M. N. Shadlen and J. A. Movshon, Neuron 24: 67-77 (1999)

THEME III: INFORMATION INTEGRATION AND CONTROL

*Biologically based computational models of high-level cognition, R. C. O'Reilly, Science 314:91-94 (2006)
*Complementary roles of basal ganglia and cerebellum in learning and motor control, K. Doya, Current Opinion in Neurobiology 10:732-739 (2000)
*Hippocampus, cortex, and basal ganglia: Insights from computational models of complementary learning systems, H. E. Atallah, M. J. Frank, and R. C. O'Reilly, Neurobiology of Learning and Memory 82/3:253-267 (2004)
*Cortex, countercurrent context, and dimensional integration of lifetime memory, B. Merker, Cortex 40:559-576 (2004)

THEME IV: HIGHLY DISTRIBUTED SYSTEMS, CLASSICAL CONDITIONING AND "GATES"

Example systems: integration of innate and learned patterns in amygdala; gating in attachment systems; semantic knowledge

*Evolution of the Amygdala in Vertebrates, F. Martinez-Garcia, A. Novejarque, and E. Lanuza, in Evolution of Nervous Systems, J. H. Kaas and T. H. Bullock, eds. (2006)
*Organization of extra-amygdaloid circuitry in the rat: an emerging framework for understanding functions of the amygdala, A. Pitkanen, V. Savander and J. E. LeDoux, Trends in NeuroSciences 20:517-523 (1997)
*The vertebrate social behavior network: evolutionary themes and variations, J. L. Goodson, Hormones and Behavior 48:11-22 (2004)
*The neurobiology of pair bonding, L. J. Young and Z. Wang, Nature Neuroscience 7:1048-54 (2004)
*The human amygdala and the emotional evaluation of sensory stimuli, D. H. Zald, Brain Research Reviews 41:88-123 (2003)

THEME V: OPEN-ENDED COMPUTATION

*Liquid computing, W. Maass, Proceedings of the CiE'07 Conference: COMPUTABILITY IN EUROPE 2007, Lecture Notes in Computer Science, Springer (Berlin), 2007
*Enzymatic computation and cognitive modularity, H. C. Barrett, Mind and Language 20:259-287 (2005)
*Virtual machines and consciousness [omit sections on consciousness], A. Sloman and R. L. Chrisley, Journal of Consciousness Studies 10(4-5):113-172 (2003)
*The major evolutionary transitions, E. Szathmary and J. Maynard Smith, Nature 374:227-232 (1995)
*Agency, emergence, and organization, P. Clayton and S. A. Kauffman, Biology and Philosophy 21:501-521 (2006)

Shimon Edelman <se37 at cornell.edu>

Last modified on Tue Apr 8 19:45:51 2008