Biology subjects

Rossbroich, J.

Publications and source records attributed to Rossbroich, J..

3 recordsLinked to original sources

bioRxiv2025

Breaking Balance: Encoding local error signals in perturbations of excitation-inhibition balance

Efficient learning algorithms such as backpropagation, predictive coding, and self-supervised learning require instructive learning signals to influence synaptic plasticity locally at specific neurons. This requirement is in-consistent with classic Hebbian theories and neuromodulation involving a non-specific third factor, thus raising the question of how neurobiology can achieve efficient learning. Here, we propose a simple and biologically plausible solution: local deviations from precise excitation/inhibition (E/I) balance encode error signals that instruct synaptic plasticity. Using a computational model derived from an adaptive control theory framework, we demonstrate that breaking E/I balance through targeted feedback to inhibitory interneurons, can produce neuron- or assembly-specific error signals that enable learning in multi-layer networks. Simulations reveal that such a balance-controlled plasticity mechanism is consistent with phenomenological local plasticity models while enabling online learning in hierarchically organized networks. Furthermore, we demonstrate that this framework is consistent with key features of disinhibitory microcircuit dynamics during in-vivo learning experiments. These results suggest that the brain may exploit E/I balance not only for stability, but also as a substrate for error-driven learning.

neuroscience↗

bioRxiv2023

Functional Subtypes of Synaptic Dynamics in Mouse and Human

Synapses show preferential responses to particular temporal patterns of activity. Across individual synapses, there is a large degree of response heterogeneity that is informally or tacitly separated into classes, and typically only two: facilitating and depressing short-term plasticity. Here we combined a kernel-based model and machine learning techniques to infer the number and the characteristics of functionally distinct subtypes of short-term synaptic dynamics in a large dataset of glutamatergic cortical connections. To this end, we took two independent approaches. First, we used unsupervised techniques to group similar synapses into clusters. Second, we used supervised prediction of cell subclasses to reveal features of synaptic dynamics that characterized cellular genetic profiles. In rodent data, we found five clusters with a remarkable degree of convergence with the transgenic-associated subtypes. Two of these clusters corresponded to different degrees of facilitation, two corresponded to depression with different degrees of variability and one corresponded to depression-then-facilitation. Strikingly, the application of the same clustering method in human data inferred highly similar clusters to those observed in rodents, supportive of a stable clustering procedure and suggesting a homology of functional subtypes across species. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

neuroscience↗

bioRxiv2020

Synaptic dynamics as convolutional units

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

neuroscience↗