- Poster presentation
- Open Access
Fast rhythm cycles as atomic fragments of cortical processing and learning
© Jitsev; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- Face Image
- Sparse Code
- Gamma Oscillation
- Atomic Fragment
- Elementary Fragment
Neuronal rhythms of different frequencies are ubiquitous in the brain activity. These rhythms are thought to be not just a mere epiphenomenon of neural dynamics, but to play an important role in information processing performed by the brain networks. However, the character of their functional involvement remains still largely elusive.
Fast brain rhythms in the gamma frequency range of 40-100 Hz, known to modulate both neuronal activity and synaptic plasticity, were often proposed to provide a reference frame for operations performed by cortical microcircuits [1, 2]. More precisely, it was hypothesized that a flexible winner-take-all (WTA) computation is performed in a cycle of gamma oscillation by local fine-scale subnetworks that contain tightly coupled excitatory pyramidal neurons residing in cortical layer II-III. Such operation selects and amplifies a small population of pyramidal cells based on the incoming afferent input while suppressing the rest, rapidly generating a sparse code that represents the current stimulus in a course of a single gamma cycle. This hypothesis leaves open whether learning and memory trace formation as well may rely on fast rhythm cycles as discrete atomic fragments of ongoing processing.
We use here a hierarchical recurrent network that employs gamma cycle as an atomic fragment for unsupervised learning of object identity from natural image input . Unsupervised learning runs on the top of a fast winner-take-all (WTA)-like computation performed within a single cycle of the ongoing fast rhythm. If given natural face images, the network is able to create memory traces containing reusable facial visual elements that are linked in associative, generative manner via simultaneously established bottom-up, lateral and top-down connectivity into a global person face identity. If a face image of a memorized person is presented, the network is able to rapidly recall its identity and gender in a single gamma cycle. The operation performed within a single cycle may be interpreted as a probabilistic inference of the latent causes that create the input and an estimation of the parameters of a mixture model with latent causes as its components. This computation has the character of an expectation-maximization procedure, where expectation part is carried out by WTA-like computation and maximization involves plasticity mechanisms that change synaptic strength and neural excitability over many repetitive cycles. Even if decoupled from external input, the network can self-generate activity in an off-line regime, replaying the memory content in a sequence of gamma cycles and improving its organization to generalize better over the novel face images not presented before once back in input-driven regime .
Thus, the presented network model provides interpretation of the gamma cycle as an elementary fragment of ongoing processing and learning, where each cycle embeds a winner-take-all-like computation that supports memory trace formation and memory trace maintenance in hierarchical recurrent network pathways of the cortex.
This work is supported by Helmholtz Alliance on Systems Biology, the Helmholtz Association in the Portfolio theme "Supercomputing and Modeling for the Human Brain" and the Juelich Aachen Research Alliance (JARA).
- Fries Pascal, Nikolic Danko, Singer Wolf: The gamma cycle. Trends Neurosci. 2007, 30 (7): 309-316. 10.1016/j.tins.2007.05.005.View ArticlePubMedGoogle Scholar
- Almeida Licurgode, Idiart Marco, Lisman John E: A second function of gamma frequency oscillations: an E%-max winner-take-all mechanism selects which cells fire. J. Neurosci. 2009, 29 (23): 7497-7503. 10.1523/JNEUROSCI.6044-08.2009.PubMed CentralView ArticlePubMedGoogle Scholar
- Jitsev Jenia, von der Malsburg Christoph: Experience-driven formation of parts-based representations in a model of layered visual memory. Front. Comput. Neurosci. 2009, 3: 15-PubMed CentralView ArticlePubMedGoogle Scholar
- Jitsev Jenia, von der Malsburg Christoph: Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory. International Joint Conference on Neural Networks (IJCNN). 2010, Barcelona, Spain, 3123-3130.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.