Biology subjects

Kappel, D.

Publications and source records attributed to Kappel, D..

6 recordsLinked to original sources

bioRxiv2025

Learning reorganizes dendritic and stabilizes axonal initial segment inhibitory synapses in CA1 pyramidal neurons

Structural synaptic plasticity underlies the changes in brain connectivity required for learning and memory. Inhibitory synapses (INS) target all subcellular domains of excitatory pyramidal neurons (PNs), including dendrites, somata and axon initial segments (AIS). These subcellular domains have distinct molecular, structural and physiological profiles which underlie their functions. How structural plasticity of INS supports these functions as well as emerging properties such as memory is largely unknown. To tackle these questions we tracked INS on dendrites, somata and AIS of PNs in the dorsal hippocampal CA1 area of mice over two weeks. Size and temporal dynamics of INS showed a strong compartmentalization and dendritic INS were less dynamic than dendritic spines. Trace fear conditioning led to reorganization of dendritic INS and to stabilization of AIS INS but had a minimal effect on dendritic spines. Finally, mathematical modelling allowed us to probe the mechanisms underlying stabilization of INS upon learning.

neuroscience↗

bioRxiv2024

Genomic epidemiology of Candida auris introduction and outbreaks in the United Kingdom

BackgroundCandida auris is a globally emerging fungal pathogen that causes nosocomial invasive infections, particularly in intensive care units (ICU). Four prominent genetic clades originating from distinct geographic regions have been identified: South Asia (Clade I), East Asia (Clade II), Africa (Clade III) and South America (Clade IV) with each clade presenting differing antifungal resistance profiles. We aimed to elucidate the epidemiology of this infection in the United Kingdom (UK) 2014 - 2019 using genomic approaches. MethodsGenome sequences from 24 isolates from six patients in four London hospitals were combined with genome sequences from 183 additional outbreak isolates from nine UK hospitals. These data were used to assess the numbers and timings of C. auris lineage introductions into the UK and to infer subsequent patterns of inter- and intra-hospital transmission. FindingsWe found evidence for at least three introductions of C. auris into the UK, one from Clade I and one from Clade III. The evolutionary rate of C. auris Clade I and Clade III were estimated at 2.764 x 10-4 and 3.186 x 10-4 substitutions per polymorphic site per year, respectively, with entry into the UK as 2013 and 2016 for Clades I and III respectively. We observed temporal and geographical evidence for multiple transmission events between hospitals and identified local within-hospital patient-to-patient transmission events. InterpretationThese data confirm that C. auris is a newly emerged infection in the UK with at least three original introductions into this country. Our analysis shows that hospital outbreaks are linked and confirms that transmission amongst patients has occurred, explaining local hospital outbreaks. Our findings argue that enhanced surveillance of C. auris infection within the UK is necessary in order to protect healthcare and to curtail expansion of this emerging infection. FundingThis work was supported by a Wellcome Trust Institutional Strategic Support Fund Springboard Fellowship, and by the Medical Research Council (MR/R015600/1), the Natural Environment Research Council (NE/P001165/1) and the Wellcome Trust (219551/Z/19/Z). Research in contextSince its first description in 2009, Candida auris has spread across the globe. For this study, authors considered all publications describing whole genome sequences of isolates within the UK up until 2020 to assess the genomic epidemiology of this fungal pathogen. This study adds value to the current understanding of C. auris epidemiology by undertaking the first analysis to assess transmission between and within hospitals using genomic data. We also provide the first in-depth temporal analysis to estimate likely times of introduction into the UK. These results have clinical implications, encouraging hospitals to identify yeast upon admission and to assess multiple isolates from single patients, which may harbour much diversity in terms of genetics and drug resistance.

genomics↗

bioRxiv2023

Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model

The hippocampal formation exhibits complex and context-dependent activity patterns and dynamics, e.g., place cell activity during spatial navigation in rodents or remapping of place fields when the animal switches between contexts. Furthermore, rodents show context-dependent renewal of extinguished behavior. However, the link between context-dependent neural codes and context-dependent renewal is not fully understood. We use a reinforcement learning agent based on deep neural networks to study the learning dynamics that occur during spatial learning and context switching in a simulated ABA extinction and renewal paradigm in a 3D virtual environment. Despite its simplicity, the network exhibits a number of features typically found in the CA1 and CA3 regions of the hippocampus. A significant proportion of neurons in deeper layers of the network are tuned to a specific spatial position of the agent in the environment - similar to place cells in the hippocampus. These spatial representations exhibit global remapping when the agent is exposed to a new context. The spatial maps are restored when the agent returns to the previous context, accompanied by renewal of the conditioned behavior. Remapping is facilitated by memory replay of experiences during training. These complex spatial representations and dynamics occur spontaneously in the hidden layer of a deep network during learning. Our results show that integrated codes that jointly represent spatial and task-relevant contextual variables are the mechanism underlying renewal in a simulated DQN agent.

neuroscience↗

bioRxiv2022

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been fueled by advances in understanding the role of RL in both the brain and artificial intelligence. However, while in machine learning a set of tools and standardized benchmarks facilitate the development of new methods and their comparison to existing ones, in neuroscience, the software infrastructure is much more fragmented. Even if sharing theoretical principles, computational studies rarely share software frameworks, thereby impeding the integration or comparison of different results. Machine learning tools are also difficult to port to computational neuroscience since the experimental requirements are usually not well aligned. To address these challenges we introduce CoBeL-RL, a closed-loop simulator of complex behavior and learning based on RL and deep neural networks. It provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g. T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g. a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g. Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. In summary, CoBeL-RL fills an important gap in the software toolbox of computational neuroscience.

bioinformatics↗

bioRxiv2022

Synapses learn to utilize pre-synaptic noise for the prediction of postsynaptic dynamics

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use their noise to represent uncertainties about the activity of the post-synaptic neuron. To show this we utilize the free-energy principle (FEP), a well-established theoretical framework to describe the ability of organisms to self-organize and survive in uncertain environments. This principle provides insights on multiple scales, from high-level behavioral functions such as attention or foraging, to the dynamics of single microcircuits in the brain, suggesting that the FEP can be used to describe all levels of brain function. The synapse-centric account of the FEP that is pursued here, suggests that synapses form an internal model of the somatic membrane dynamics, being updated by a synaptic learning rule that resembles experimentally well-established LTP/LTD mechanisms. This approach entails that a synapse utilizes noisy processes like stochastic synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate their noise processes to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.

neuroscience↗

bioRxiv2022

Differential Hebbian learning with time-continuous signals for active noise reduction

Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain.

neuroscience↗