HCN1 compartmentalization in CA1 pyramidal cells, essential for hippocampal information processing, is believed to be controlled by the extracellular matrix protein Reelin. Expression of Reelin, in turn, is stimulated by 17β-estradiol (E2). In this study, we therefore tested whether E2 regulates the compartmentalization of HCN1 in CA1 via Reelin. In organotypic entorhino-hippocampal cultures, we found that E2 promotes HCN1 distal dendritic enrichment via the G protein–coupled estrogen receptor GPER1, but apparently independent of Reelin, because GST-RAP, known to reduce Reelin signaling, did not prevent E2-induced HCN1 enrichment in distal CA1. We therefore re-examined the role of Reelin for the regulation of HCN1 compartmentalization and could not detect effects of reduced Reelin signaling on HCN1 distribution in CA1, either in the (developmental) slice culture model or in tamoxifen-inducible conditional reelin knockout mice during adulthood. We conclude that for HCN1 channel compartmentalization in CA1 pyramidal cells, Reelin is not as essential as previously proposed, and E2 effects on HCN1 distribution in CA1 are mediated by mechanisms that do not involve Reelin. Because HCN1 localization was not altered at different phases of the estrous cycle, gonadally derived estradiol is unlikely to regulate HCN1 channel compartmentalization, while the pattern of immunoreactivity of aromatase, the final enzyme of estradiol synthesis, argues for a role of local hippocampal E2 synthesis.
1Successful navigation in complex acoustic scenes requires focusing on relevant 2 sounds while ignoring irrelevant distractors. It has been argued that the ability to 3 track stimulus statistics and generate predictions supports the choice what to 4 attend and what to ignore. However, the role of these predictions about future 5 auditory events in drafting decisions remains elusive. While most psychophysical 6 studies in humans indicate that expected stimuli serve as implicit cues attracting 7 attention, most work studying physiological auditory processing in animals 8 highlights the detection of unexpected, surprising stimuli. Here, we tested whether 9 in the mouse, target probability is used as an implicit cue attracting attention or 10 whether detection is biased towards low-probability deviants using an auditory 11 detection task. We implemented a probabilistic choice model to investigate 12 whether a possible dependence on stimulus statistics arises from short term serial 13 correlations or from integration over longer periods. Our results demonstrate that 14 adapt their behavior according to the stimulus statistics (Bargones and Werner, 39 1994a; Gordon Z. Greenberg and Larkin, 1968). This form of selective auditory 40 attention does not require awareness of the subject and is driven by unconscious 41 expectations (Wolmetz and Elhilali, 2016). Within this framework, the 42 improvement of detectability is based on the expectation as an implicit cue and 43 serves as an internal reward-maximizing strategy that drives the attention towards 44 the expectation (Girshick et al., 2011). 45 While most psychophysical studies indicate that expected stimuli serve as implicit 46 cues attracting attention, most work studying the physiology of auditory 47 processing highlights the detection of unexpected, surprising stimuli. Stimuli are 48 more salient when presented rarely to the auditory system and thus might be 49 easier to detect due to pre-attentive mechanisms (Malmierca et al., 2015; Pérez-50 González et al., 2005; Tiitinen et al., 1994). Within this framework, the evaluation 51 of stimulus statistics serves to detect novelty, emphasizing changes in the auditory 52 scene rather than enabling tracking of task relevant information. 53 Thus, tracking of stimulus probability influences auditory processing in two 54 contrary ways: on the physiological level, low-probability sounds elicit maximal 55 responses, but during listening tasks, relevant high-probability sounds appear to 56 attract attention, improving their detectability. While physiological evidence for 57 deviant detection spans all the way from animal models to humans (Heilbron and 58 Chait, 2017; Khouri and Nelken, 2015), behavioral assessment of the effects of 59 target probability is largely restricted to humans. In order to understand the neural 60 mechanisms underlying predictive coding, animal models such as rodents in 61 which both physiology and behavior can be studied are needed. 62 Although rodents serve as widely used animal models to study audi...
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