Mind-Brain Lecture: Mikhail I. Rabinovich (San Diego)
When the complex biophysical properties of neurons and their connections are combined with realistic connectivity rules and scales, brain network dynamics are difficult to predict. Yet, experimental neuroscience is often based on the implicit premise that the neural mechanisms underlying sensation, perception, and cognition are well approximated by steady-state measurements (of neuron activity), snapshot of images, or by models that demonstrate just simple, i.e., steady state or periodic, activity. I will unfold a new paradigm in the study of brain dynamics departing from the stable transient activity neural networks, as supported by experiments. This new approach needs a new intuition in contrast to the traditional one that based on the analyses of simple experimental systems. Computing with “attractors” is a concept familiar to the neural networks and brain communities because of it objectivity and simplicity: Upon some input signal, a model neural network will gradually change its pattern of activated nodes (neuronal clusters) until it settles into one pattern — presumably, an attractor state. In this view, the stimulus — a voice, an odor, or something more abstract — routes the network towards a particular attractor state. Such patterns of neural activity may indeed be established, learned, and recalled during perception, memorization, and retrieval, respectively. However, the conditions required to achieve such attractors in artificial neural networks are often implausible for known biological circuits in the brain. A simple observation rejecting the attractor-oriented perspective is that the brain activity is usually non-stationary. Transient dynamics, on the other hand, offers a sound formulation to the observed qualities of brain activity, while providing a rigorous set of analysis tools. Transients have two main features: First, although they cannot be described by classical attractor dynamics, they are resistant to noise, and reliable even in the face of small variations in initial conditions; the sequence of states visited by the system (its trajectory), is thus structurally stable. Second, the transients are input-specific, and thus convey information about what caused them in the first place. This new dynamical view manifests a rigorous explanation of how perception, cognition, emotion, and other mental processes evolve as a sequence of activity patterns in the brain, and, most importantly, how they intervene with each other. The mathematical image of such transients is a stable heteroclinic channel that is possibly the only dynamical object that satisfies all required conditions, i.e., being both stable and sensitive to incoming information at the same time. We applied these ideas to description and prediction of the emotion-cognition interaction in connection with anxiety and decision-making.
References:
- M.Rabinovich, R.Huerta, G.Laurent , SCIENCE. 321,48 (2008).
- M. Rabinovich et al., Phys. Rev. Lett. 87, 068102 (2001).
- R. Huerta, M. Rabinovich, Phys Rev Lett. 93, 238104 (2004).
- M. Rabinovich, R. Huerta, P. Varona, V. Afraimovich, PLoS Comput. Biol. 4, e1000072 (2008).
Mikhail I. Rabinovich is Professor at the Institute for Nonlinear Science, University of California, San Diego.
http://inls.ucsd.edu/~rabin/
