Background: Cholinergic interneurons (ChIs) are important for learning and memory. They exhibit a multiphasic excitation-pause-rebound response to reward or sensory cues indicating a reward, believed to gate dopamine-dependent learning. Although ChIs receive extensive top-down inputs from the cortex and bottom-up inputs from the thalamus and midbrain, it is unclear which inputs are involved in the development of ChI multiphasic activity.
Methods: We used a single-unit recording of putative ChIs (pChIs) in response to cortical and visual stimulation to investigate how top-down and bottom-up inputs regulate the firing pattern of ChIs.
Results: We demonstrated that cortical stimulation strongly regulates pChIs, with the maximum firing rate occurring at the peak of the inverted local field potential (iLFP), reflecting maximum cortical stimulation. Pauses in pChIs occurred during the descending phase of iLFP, indicating withdrawal of excitatory cortical input. Visual stimulation induced long pauses in pChIs, but it is unlikely that bottom- up inputs alone induce pauses in behaving animals. Also, the firing pattern of ChIs triggered by visual stimulation did not correlate with the iLFP as it did after cortical stimulation. Top-down and bottom-up inputs independently regulate the firing pattern of ChIs with similar efficacy but notably produce a well-defined pause in ChI firing.
Conclusion: This study provides evidence that the multiphasic ChI response may require both top-down and bottom-up inputs. The findings suggest that the firing pattern of ChIs correlated to the iLFP might be a useful tool for estimating the degree of contribution of top-down and bottom-up inputs in regulating the firing activity of ChIs.
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http://dx.doi.org/10.2174/1570159X22666231115151403 | DOI Listing |
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Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt 60528, Germany.
Under natural conditions, animals repeatedly encounter the same visual scenes, objects or patterns repeatedly. These repetitions constitute statistical regularities, which the brain captures in an internal model through learning. A signature of such learning in primate visual areas V1 and V4 is the gradual strengthening of gamma synchronization.
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Research Centre of Mathematics, University of Minho, Guimarães, Portugal.
Continuous bump attractor networks (CANs) have been widely used in the past to explain the phenomenology of working memory (WM) tasks in which continuous-valued information has to be maintained to guide future behavior. Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning. We address both challenges in a two-dimensional (2D) network model formalized by two coupled neural field equations of Amari type.
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Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA; Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA.
Sensory systems use context to infer meaning. Accordingly, context profoundly influences neural responses to sensory stimuli. However, a cohesive understanding of the circuit mechanisms governing contextual effects across different stimulus conditions is still lacking.
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