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Synchronous neural oscillations are found throughout the brain and are thought to contribute to neural coding and the propagation of activity. Several proposed mechanisms of synchronization have gained support through combined theoretical and experimental investigation, including mechanisms based on coupling and correlated input. Here, we ask how correlation-induced synchrony is affected by physiological heterogeneity across neurons. To address this question, we examined cell-to-cell differences in phase-response curves (PRCs), which characterize the response of periodically firing neurons to weak perturbations. Using acute slice electrophysiology, we measured PRCs across a single class of principal neurons capable of sensory-evoked oscillations in vivo: the olfactory bulb mitral cells (MCs). Periodically firing MCs displayed a broad range of PRCs, each of which was well fit by a simple three-parameter model. MCs also displayed differences in firing rate-current relationships and in preferred firing rate ranges. Both the observed PRC heterogeneity and moderate firing rate differences (∼10 Hz) separately reduced the maximum correlation-induced synchrony between MCs by up to 25-30%. Simulations further demonstrated that these components of heterogeneity alone were sufficient to account for the difference in synchronization among heterogeneous vs. homogeneous populations in vitro. Within this simulation framework, independent modulation of specific PRC features additionally revealed which aspects of PRC heterogeneity most strongly impact correlation-induced synchronization. Finally, we demonstrated good agreement of novel mathematical theory with our experimental and simulation results, providing a theoretical basis for the influence of heterogeneity on correlation-induced neural synchronization.
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http://dx.doi.org/10.1152/jn.00362.2012 | DOI Listing |
Neural Netw
July 2025
School of Computer Science and Technology, Tongji University, Shanghai, 201804, China; Engineering Research Center of Key Software Technologies for Smart City Perception and Planning, Ministry of Education, Shanghai, 201804, China.
Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions during task transitions. This paper identifies parameter variation imbalance as a critical factor contributing to prediction bias in ER-based OCL.
View Article and Find Full Text PDFPhys Rev Lett
October 2021
Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA.
We present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions. We uncover an intriguing correlation-induced freezing behavior as doped holes are segregated from half filled insulating background during equilibration. While the aggregation of holes is stabilized by the formation of ferromagnetic clusters through Hund's coupling between charge carriers and local magnetic moments, this stabilization also creates confining potentials for holes when antiferromagnetic spin-spin correlation is well developed in the background.
View Article and Find Full Text PDFEur J Neurosci
March 2015
Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada; Department of Psychology, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada.
Tinnitus is the sound heard in the absence of physical sound sources external or internal to the body. Tinnitus never occurs in isolation; it typically develops after hearing loss, and not infrequently for losses at the higher frequencies not tested in clinical audiology. Furthermore, tinnitus is often accompanied by hyperacusis, i.
View Article and Find Full Text PDFJ Neurophysiol
October 2012
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Synchronous neural oscillations are found throughout the brain and are thought to contribute to neural coding and the propagation of activity. Several proposed mechanisms of synchronization have gained support through combined theoretical and experimental investigation, including mechanisms based on coupling and correlated input. Here, we ask how correlation-induced synchrony is affected by physiological heterogeneity across neurons.
View Article and Find Full Text PDFJ Comput Neurosci
November 2011
NeuroMathComp Laboratory, INRIA/ENS Paris, 23 Avenue d'Italie, 75013 Paris, France.
The brain's activity is characterized by the interaction of a very large number of neurons that are strongly affected by noise. However, signals often arise at macroscopic scales integrating the effect of many neurons into a reliable pattern of activity. In order to study such large neuronal assemblies, one is often led to derive mean-field limits summarizing the effect of the interaction of a large number of neurons into an effective signal.
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