CiteULike: mbregman oscillations

Emergence of rhythm during motor learning.

K Sakai — 2006-06-22T21:12:00-00:00

Trends Cogn Sci, Vol. 8, No. 12. (December 2004), pp. 547-553.

Complex motor skill often consists of a fixed sequence of movements. Recent studies show that a stereotyped temporal pattern or rhythm emerges as we learn to perform a motor sequence. This is because the sequence is reorganized during learning as serial chunks of movements in both a sequence-specific and subject-specific manner. On the basis of human imaging studies we propose that the formation of chunk patterns is controlled by the cerebellum, its posterior and anterior lobes contributing, respectively, to the temporal patterns before and after chunk formation. The motor rhythm can assist the motor networks in the cerebral cortex to control automatic movements within chunks and the cognitive networks to control non-automatic movements between chunks, respectively. In this way, organized motor skill can be performed automatically and flexibly.

Large-scale model of mammalian thalamocortical systems

Eugene Izhikevich — 2008-02-28T20:15:52-00:00

Proceedings of the National Academy of Sciences (21 February 2008), 0712231105.

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales. 10.1073/pnas.0712231105

A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

Pascal Fries — 2005-09-23T14:12:08-00:00

Trends in Cognitive Sciences, Vol. 9, No. 10. (October 2005), pp. 474-480.

At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.