Biology subjects

Brodersen, P. J. N.

Publications and source records attributed to Brodersen, P. J. N..

5 recordsLinked to original sources

bioRxiv2026

Opposing intrinsic and synaptic plasticity mechanisms stabilize altered cortical networks during sleep deprivation

Sleep deprivation alters brain activity and impairs performance, yet many aspects of behaviour are preserved in this state. In the cortex for example, sleep deprivation increases neuronal firing rates and low-frequency oscillatory activity, but cortical circuits continue to process information. Recent work has implicated synaptic inhibition in these changes, with sleep deprivation causing cortical GABAA receptor (GABAAR) signalling to become depolarizing due to changes in chloride gradients that determine the GABAAR reversal potential (EGABAAR). The impact in the intact brain remains unclear, however, as both the degree and effects of EGABAAR changes depend on network activity and intrinsic properties of neurons. To address this, we perform in vivo gramicidin recordings from cortical pyramidal neurons in sleep-deprived mice. We reveal that synaptic EGABAAR is depolarised to values sufficient to support bona fide excitatory GABAAR signalling, through combined network-dependent and cell-autonomous effects. In the face of this depolarizing drive, cortical neurons engage spike threshold adaptation mechanisms that limit their excitability. These opposing effects stabilise activity in simulations of sleep-deprived cortical networks and reproduce the increased firing rates and low-frequency oscillatory activity. This interplay between intrinsic and synaptic mechanisms may help maintain network stability during sleep deprivation, while reducing flexibility for subsequent adaptation.

neuroscience↗

bioRxiv2025

Automated sleep scoring in hibernating and non-hibernating American black bears

Hibernating bears show remarkable metabolic suppression. Their decline in core body temperature (Tb) is moderate (from 38{degrees}C to 30-35{degrees}C), but their metabolism declines as much as 75%. To understand the role of sleep in this hypometabolic state, we recorded biotelemetrically EEG, EOG and EMG data over 3500 days from 16 captive American black bears in and out of hibernation under semi-natural conditions. This data set is too large to score manually for Wake, REM- and NREM sleep, so we tested two machine learning classifiers: (1) Somnotate trained on multiple one-day recordings, and (2) Somnivore, trained on a small subset from each recording. As automated scoring methods have not been applied to hibernating species before, a major concern is the effect changing brain temperature has on the EEG and on the machine learning based detection. Therefore, we selected reference data using consensus by 3 manual sleep scorers from each of 6 bears, two one-day recordings at the highest and lowest body temperatures during hibernation when Tb was oscillating in multiday cycles, and a non-hibernating one-day recording in summer. Somnotate results were excellent when trained separately for hibernating and non-hibernating data. Training Somnotate separately for high and low Tb within hibernation did not improve results further. Sleep times in hibernation were about 2x that in summer for both automated scores and manual scores (p<0.0001). There were no significant differences in occupancy of vigilance states between automated and manual scores in hibernation (p>0.05), but a small overestimate of sleep time in summer (p<0.05). Both applications yielded F-measures against manual scores in the 0.90-0.98 range. Outliers in the 0.67-0.88 range were correlated between the two applications, indicating that specific files are more challenging to annotate. We conclude that both applications have accuracies approaching that of manual scorers when trained on high quality data.

neuroscience↗

bioRxiv2023

Active cortical networks promote shunting fast synaptic inhibition in vivo

Fast synaptic inhibition determines neuronal response properties in the mammalian brain and is mediated by chloride-permeable ionotropic GABA-A receptors (GABAARs). Despite their fundamental role, it is still not known how GABAARs signal in the intact brain. Here we use in vivo gramicidin recordings to investigate synaptic GABAAR signaling in mouse cortical pyramidal neurons under conditions that preserve native transmembrane chloride gradients. In anaesthetized cortex, synaptic GABAARs exert classic hyperpolarizing effects. In contrast, GABAAR-mediated synaptic signaling in awake cortex is found to be predominantly shunting. This is due to more depolarized GABAAR equilibrium potentials (EGABAAR), which are shown to result from the high levels of synaptic activity that characterize awake cortical networks. The EGABAAR observed in awake cortex can facilitate the decoupling of local networks, which improves the ability of the network to discriminate stimuli. Our findings therefore suggest that GABAAR signaling adapts to optimize cortical functions.

neuroscience↗

bioRxiv2022

Biologically plausible unsupervised learning in neural networks with sparse and asymmetric connectivity

In the search for biologically plausible but mathematically precise theories of learning in the brain, recent studies have begun to investigate how key assumptions underlying algorithms for supervised learning in artificial neural networks can be relaxed in biologically plausible ways. Turning to unsupervised learning, we develop biologically more plausible variants of the restricted Boltzmann machine (RBM), and benchmark their performance on MNIST. We show that RBMs with asymmetric connectivity can still be successfully trained with contrastive divergence, even if no two units are reciprocally connected. Furthermore, RBMs are able to learn if the forward, visible-to-hidden layer weights are kept constant and only the backward, hidden-to-visible layer weights are updated. These findings indicate that neural networks with biologically plausible connectivity support contrastive learning.

neuroscience↗

bioRxiv2021

Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and computational annotation methods currently ignore intermediate states because the classification features become ambiguous. However, these intermediate states contain important information regarding vigilance state dynamics. Here, we present a new classifier, "Somnotate", which produces automated annotation accuracies that exceed human expert performance on mouse electrophysiological data, is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions. Somnotate is a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). A unique feature of Somnotate is that it quantifies and reports the certainty of its annotations, enabling the experimenter to identify ambiguous recording periods in a principled manner. We leverage this feature to identify epochs that exhibit intermediate vigilance states, revealing that many of these cluster around state transitions, whereas others correspond to failed attempts to transition. We show that the success rates of different transitions can be experimentally manipulated and explain previously observed sleep patterns. Somnotate can thus facilitate the study of sleep stage transitions and offers new insight into the mechanisms underlying sleep-wake dynamics. Author summaryTypically, the three different vigilance states - awake, REM sleep, and non-REM sleep - exhibit distinct features that are readily recognised in electrophysiological recordings. However, particularly around vigilance state transitions, epochs often exhibit features from more than one state. These intermediate vigilance states pose challenges for existing manual and automated classification methods, and are hence often ignored. Here, we present Somnotate - an open-source, highly accurate and robust sleep stage classifier, which supports research into intermediate states and sleep stage dynamics. Somnotate quantifies and reports the certainty of its annotations, enabling the experimenter to identify abnormal epochs in a principled manner. We use this feature to identify intermediate states and to detect unsuccessful attempts to switch between vigilance states. This provides new insights into the mechanisms of vigilance state transitions in mice, and creates new opportunities for future experiments.

neuroscience↗