Biology subjects

Rudelt, L.

Publications and source records attributed to Rudelt, L..

5 recordsLinked to original sources

bioRxiv2024

Power-law adaptation in the presynaptic vesicle cycle

After synaptic transmission, fused synaptic vesicles are recycled, enabling the synapse to recover its capacity for renewed release. The recovery steps, which range from endocytosis to vesicle docking and priming, have been studied individually, but it is not clear what their impact on the overall dynamics of synaptic recycling is, and how they influence signal transmission. Here we model the dynamics of vesicle recycling and find that the multiple timescales of the recycling steps are reflected in synaptic recovery. This leads to multi-timescale synapse dynamics, which can be described by a simplified synaptic model with power-law adaptation. Using cultured hippocampal neurons, we test this model experimentally, and show that the duration of synaptic exhaustion changes the effective synaptic recovery timescale, as predicted by the model. Finally, we show that this adaptation could implement a specific function in the hippocampus, namely enabling efficient communication between neurons through the temporal whitening of hippocampal spike trains.

neuroscience↗

bioRxiv2023

Prediction mismatch responses arise as corrections of a predictive spiking code

Prediction mismatch responses in cortex seem to signal the difference between an internal model of the animal and sensory observations. Often these responses are interpreted as evidence for the existence of error neurons, which guide inference in models of hierarchical predictive coding. Here we show that prediction mismatch responses also arise naturally in a spiking encoding of sensory signals, where spikes predict the future signal. In this model, the predictive representation has to be corrected when a mispredicted stimulus appears, which requires additional neural activity. This adaptive correction could explain why mismatch response latency can vary with mismatch detection difficulty, as the network gathers sensory evidence before committing to a correction. Prediction mismatch responses thus might not reflect the computation of errors per se, but rather the reorganization of the neural code when new information is incorporated.

neuroscience↗

bioRxiv2022

Visuomotor mismatch responses as a hallmark of explaining away in causal inference

How are visuomotor mismatch responses in visual cortex embedded into cortical processing? We here argue that mismatch responses are best understood as the result of a cooperation of motor and visual areas to jointly explain optic flow. This cooperation ensures that optic flow is not explained redundantly by both areas, which in the language of causal inference is termed explaining away. This improves the efficiency of the resulting neural code, and allows the animal to easily detect movements that are independent of its own locomotion. We demonstrate the emergence of mismatch responses from explaining away in simulations, where spiking neurons learn to encode optic flow stimuli and locomotion. We furthermore lay out arguments against the prevailing idea that mismatch signals are the result of a dedicated error computation in a hierarchical model. These results provide a new perspective on several recent experiments of cross-modal neural interactions in cortex.

neuroscience↗

bioRxiv2020

Embedding optimization reveals long-lasting history dependence in neural spiking activity

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long--potentially redundant--past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox. Author summaryEven with exciting advances in recording techniques of neural spiking activity, experiments only provide a comparably short glimpse into the activity of only a tiny subset of all neurons. How can we learn from these experiments about the organization of information processing in the brain? To that end, we exploit that different properties of information processing leave distinct footprints on the firing statistics of individual spiking neurons. In our work, we focus on a particular statistical footprint: How much does a single neurons spiking depend on its own preceding activity, which we call history dependence. By quantifying history dependence in neural spike recordings, one can, in turn, infer some of the properties of information processing. Because recording lengths are limited in practice, a direct estimation of history dependence from experiments is challenging. The embedding optimization approach that we present in this paper aims at extracting a maximum of history dependence within the limits set by a reliable estimation. The approach is highly adaptive and thereby enables a meaningful comparison of history dependence between neurons with vastly different spiking statistics, which we exemplify on a diversity of spike recordings. In conjunction with recent, highly parallel spike recording techniques, the approach could yield valuable insights on how hierarchical processing is organized in the brain.

neuroscience↗

bioRxiv2020

Sequence memory in recurrent neuronal network can develop without structured input

How does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks "pre-shaped" by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa, but instead harness building blocks already present in the brain.

neuroscience↗