Understanding how cortical network dynamics support learning is a challenge. This study investigates the role of local neural mechanisms in the prefrontal cortex during contingency judgment learning (CJL). To better understand brain network mechanisms underlying CJL, we introduce ambiguity into associative learning after fear acquisition, inducing a generalized fear response to an ambiguous stimulus sharing nontrivial similarities with the conditioned stimulus. Real-time recordings at single-neuron resolution from the prelimbic (PL) cortex show distinct PL network dynamics across CJL phases. Fear acquisition triggers PL network reorganization, led by a disambiguation circuit managing spurious and predictive relationships during cue-danger, cue-safety, and cue-neutrality contingencies. Mice with PL-targeted memory deficiency show malfunctioning disambiguation circuit function, while naive mice lacking unconditioned stimulus exposure lack the disambiguation circuit. This study shows that fear conditioning induces prefrontal cortex cognitive map reorganization and that subsequent CJL relies on the disambiguation circuit's ability to learn predictive relationships.
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http://dx.doi.org/10.1016/j.celrep.2024.114926 | DOI Listing |
Cell Rep
November 2024
Department of Psychology, University of California, Riverside, Riverside, CA 92521, USA; Neuroscience Program, University of California, Riverside, Riverside, CA 92521, USA. Electronic address:
Understanding how cortical network dynamics support learning is a challenge. This study investigates the role of local neural mechanisms in the prefrontal cortex during contingency judgment learning (CJL). To better understand brain network mechanisms underlying CJL, we introduce ambiguity into associative learning after fear acquisition, inducing a generalized fear response to an ambiguous stimulus sharing nontrivial similarities with the conditioned stimulus.
View Article and Find Full Text PDFCell Rep
September 2024
Neuroscience Institute, NYU Grossman School of Medicine, New York University, New York, NY, USA; Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA. Electronic address:
Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session.
View Article and Find Full Text PDFEntropy (Basel)
June 2024
School of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China.
To construct a chaotic system with complex characteristics and to improve the security of image data, a five-dimensional tri-valued memristor chaotic system with high complexity is innovatively constructed. Firstly, a pressure-controlled tri-valued memristor on Liu's pseudo-four-wing chaotic system is introduced. Through analytical methods, such as Lyapunov exponential map, bifurcation map and attractor phase diagram, it is demonstrated that the new system has rich dynamical behaviors with periodic limit rings varying with the coupling parameter of the system, variable airfoil phenomenon as well as transient chaotic phenomenon of chaos-periodic depending on the system parameter and chaos-quasi-periodic depending on the memristor parameter.
View Article and Find Full Text PDFIEEE Trans Cybern
October 2024
Natural language processing (NLP) may face the inexplicable "black-box" problem of parameters and unreasonable modeling for lack of embedding of some characteristics of natural language, while the quantum-inspired models based on quantum theory may provide a potential solution. However, the essential prior knowledge and pretrained text features are often ignored at the early stage of the development of quantum-inspired models. To attacking the above challenges, a pretrained quantum-inspired deep neural network is proposed in this work, which is constructed based on quantum theory for carrying out strong performance and great interpretability in related NLP fields.
View Article and Find Full Text PDFbioRxiv
April 2024
Neuroscience Institute, New York University, New York, NY, USA.
Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we found that CA1 population vectors decorrelated gradually within a session.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!