The Neurointeractive Paradigm: Dynamical Mechanics and the Emergence of Higher Cortical Function
Recently established biological principles of neural connectionism promote a neurointeractivist paradigm of brain and behavior which emphasizes interactivity between neurons within cortical areas, between areas of the cerebral cortex, and between the cortex and the environment. This paradigm recognizes the closed architecture of the behaving organism with respect to motor/sensory integration within a dynamic environment where the majority of sensory activity is the direct consequence of self-oriented motor actions. The top-down cortical inputs to primary sensory areas, which generate a signal that predicts discrimination behavior in monkeys (Cauller and Kulics, 1991), selectively activate the cortico-bulbar neurons that mediate directed movements. Unlike the widely distributed axons and long-lasting excitatory synaptic effects of the top-down projections, which generate the associative context for motor/ sensory interactivity, the bottom-up sensory projections are spatially precise and activate a brief excitation followed by a long-lasting inhibition (Cauller and Connors, 1994). Therefore, the sensory consequences of a motor action are the major source of negative feedback, which completes an interactive cycle of associative hypothesis testing: a winner-take-all motor/sensory pattern initiates a behavioral action within a top-down associative context; the bottom-up sensory consequences of that action interfere with top-down sensory predictions and strengthen or refine the associative hypothesis; then the testing cycle repeats as the sensory negative feedback inhibits the motor/sensory pattern and releases the next winner-take-all action.
Given this neurointeractivity, perception is a proactive behavior rather than information processing, so there is no need to impose representationalism: neurons simply respond to their inputs rather than encode sensory properties; neural activity patterns are self-organized dynamical attractors rather than sensory driven transformations; action is based upon a purely subjective model of the environment rather than a reconstruction. The associative hypothesis is the neurointeractive equivalent to awareness and hypothesis testing is the basis for attention. This neurointeractive process of action/prediction association explains early development: from self-organized cortical attractors in utero; to the emergence of self-identity in the newborn, who learns to predict the immediate effects of self-action (i.e. listening to its own speech sounds); to the discovery of ecological contingencies; to the emergence of speech by prediction of mother’s responses to infant speech. Ultimately, our scientific paradigm likewise emerges by neurointeractivity as we learn to see the world in a way that explains more of the effects of our actions.
KeywordsQuartz Depression Covariance Neurol Pyramid
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- Cauller, L.J., Connors, B.W. (1994) Synaptic physiology of horizontal afferents to layer I in slices of rat SI neocortex. J. Neuroscience 14: 751–762.Google Scholar
- Douglas, R.J., Martin, K.A. (1991) A functional microcircuit for cat visual cortex. J. Physiol. 440: 735–769.Google Scholar
- Elman, J.L., Parisi, D., Bates, E.A., Johnson, M.H., Karmiloff-Smith, A. (1997) Rethinking Innateness: A connectionist perspective on development. Boston: MIT Press.Google Scholar
- Hebb, D.O. (1949) The Organization of Behavior. New York: John Wiley.Google Scholar
- Jackson, M.E., Patterson, J., Cauller, L.J. (1996) Dynamical analysis of spike trains in a simulation of reciprocally connected “chaoscillators”: Dependence of spike pattern fractal dimension on strength of feedback connections. In: J.M. Bower (Ed.) Computational Neuroscience: Trends in Research. San Diego: Academic Press.Google Scholar
- Mumford, D. (1994) Neuronal architectures for pattern-theoretic problems. In: C. Koch, J.L. Davis (Eds.) Large-Scale Neuronal Theories of the Brain. Cambridge, MA: MIT Press, pp. 125–152.Google Scholar
- Quartz, S.R., Sejnowski, T.J. (1997) The neural basis of cognitive development: A constructivist manifesto. Behav. Brain Sci. 20: 537–596.Google Scholar
- Ullman, S. (1994) Sequence seeking and counterstreams: A model for bidirectional information flow in the cortex. In: C. Koch, J.L. Davis (Eds.) Large-Scale Neuronal Theories of the Brain. Cambridge, MA: MIT Press, pp. 257–270.Google Scholar