- Poster presentation
- Open Access
Self-generated neural activity: models and perspective
BMC Neuroscience volume 10, Article number: P272 (2009)
The brain is autonomously active and this self-sustained neural activity is in general modulated, but not driven, by the sensory input data stream [1, 2]. Traditionally one has regarded this eigendynamics as resulting from inter-modular recurrent neural activity . Understanding the basic modules for cognitive computation is, in this view, the primary focus of research and the overall neural dynamics would be determined by the the topology of the intermodular pathways. Here we examine an alternative point of view, asking whether certain aspects of the neural eigendynamics have a central functional role for overall cognitive computation [4, 5].
Transiently stable neural activity is regularly observed on the cognitive time-scale of 80–100 ms, with indications that neural competition  plays an important role in the selection of the transiently stable neural ensembles , also denoted winning coalitions . We report on a theory approach which implements these two principles, transient-state dynamics and neural competition, in terms of an associative neural network with clique encoding .
A cognitive system  with a non-trivial internal eigendynamics has two seemingly contrasting tasks to fulfill. The internal processes need to be regular and not chaotic on one side, but sensitive to the afferent sensory stimuli on the other side. We show, that these two contrasting demands can be reconciled within our approach based on competitive transient-state dynamics, when allowing the sensory stimuli to modulate the competition for the next winning coalition. By testing the system with the bars problem, we find an emerging cognitive capability. Only based on the two basic architectural principles, neural competition and transient-state dynamics, with no explicit algorithmic encoding, the system performs on its own a non-linear independent component analysis of input data stream. The system has rudimentary biological features. All learning is local Hebbian-style, unsupervised and online. It exhibits an ever-ongoing eigendynamics and at no time is the state or the value of synaptic strengths reset or the system restarted; there is no separation between training and performance. We believe that this kind of approach – cognitive computation with autonomously active neural networks – to be an emerging field, relevant both for system neuroscience and synthetic cognitive systems.
Ringach DL: States of mind. Nature. 2003, 425: 912-913. 10.1038/425912a.
Fiser J, Chiu C, Weliky M: Small modulation of ongoing cortical dynamics by sensory input during natural vision. Nature. 2004, 431: 573-578. 10.1038/nature02907.
Arieli A, Sterkin A, Grinvald A, Aertsen A: Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses. Science. 1996, 273: 1868-1871. 10.1126/science.273.5283.1868.
Gros C: Cognitive computation with autonomously active neural networks: an emerging field. Cognitive Computation. 2009, (a review),
Gros C: Emotions, diffusive emotional control and the motivational problem for autonomous cognitive systems. Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence. Edited by: Vallverdi, Casacuberta. 2009, (a chapter contribution).,
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences. 2005, 102: 9673-9678. 10.1073/pnas.0504136102.
Abeles M, Bergman H, Gat I, Meilijson I, Seidemann E, Tishby N, Vaadia E: Cortical activity flips among quasi-stationary states. Proceedings of the National Academy of Sciences. 1995, 92: 8616-8620. 10.1073/pnas.92.19.8616.
Crick FC, Koch C: A framework for consciousness. Nature Neuroscience. 2003, 6: 119-126. 10.1038/nn0203-119.
Gros C: Neural networks with transient state dynamics. New Journal of Physics. 2007, 9: 109-10.1088/1367-2630/9/4/109.
Gros C: Complex and Adaptive Dynamical Systems, A Primer. 2008, New York: Springer-Verlag
About this article
Cite this article
Gros, C. Self-generated neural activity: models and perspective. BMC Neurosci 10, P272 (2009) doi:10.1186/1471-2202-10-S1-P272
- Neural Activity
- Independent Component Analysis
- Cognitive System
- Algorithmic Encode
- Sensory Stimulus