Biology subjects

Hennig, M. H.

Publications and source records attributed to Hennig, M. H..

10 recordsLinked to original sources

bioRxiv2026

Predictive learning with local plasticity in excitatory-inhibitory networks

Predictive coding is a powerful normative framework for understanding cortical computation, but it is still an open question how biologically plausible networks with local plasticity support predictive inference and representation learning. In this work we show that a recurrent excitatory-inhibitory circuit with purely local plasticity can perform predictive inference without explicit error representations. We establish a direct analytic link that shows that learning in these circuits requires the weights to remain on a consistency manifold where recurrent inhibition matches the inhibition required by the predictive coding objective. Using a closed-form derivation of the consistency condition, we derive a plasticity rule that maintains it exactly under a Gaussian prior. Under a non-Gaussian prior, we find the rule supports learning sparse, factorized features, such as edge detectors from natural images. We show empirically that a BCM-like rule with an activity-dependent threshold approximates this well, while other Hebbian-like rules tend to learn less accurate solutions because they keep the weights too far from this manifold. With recurrent excitation the networks acquire spatiotemporal features like direction selectivity, and the ability to complete partially observed sequences. Time-continuous learning then leads to the development of low-dimensional attractor-like structures and noise-driven replay. Overall, these results link predictive coding to local circuit plasticity, show it does not require an explicit prediction error representation, and suggest a normative role for BCM-like plasticity in excitatory synapses.

neuroscience↗

bioRxiv2025

UnitRefine: A Community Toolbox for Automated Spike Sorting Curation

High-density electrophysiology simultaneously captures the activity from hundreds of neurons, but isolating single-unit activity still relies on slow and subjective manual curation. As datasets keep increasing, this poses a major bottleneck in the field. We therefore developed UnitRefine, a classification toolbox that automates curation by training various machine-learning models directly on human expert annotations. Fully integrated in the SpikeInterface ecosystem, UnitRefine combines established and novel quality metrics, cascading classification and comprehensive hyperparameter search to provide optimized models for different applications. UnitRefine achieves human-level performance across diverse datasets, spanning species, probe types, and laboratories, including recordings from mice, rats, mole rats, primates, and human patients. Applied to a large brain-wide dataset, UnitRefine doubled single unit yield and improved behavioral decoding performance. A streamlined graphical interface allows models to be fine-tuned to new datasets and shared via the Hugging Face Hub, enabling broad adoption and community-driven improvement of automated curation workflows.

neuroscience↗

bioRxiv2025

BAND: Behavior-Aligned Neural Dynamics is all you need to capture motor corrections

Movement is the result of complex, dynamic interaction between cortical and subcortical circuits. These dynamic interactions implement both feedforward motor control, arising from preparatory states, and feedback control, triggered by unexpected sensory events during movement. We show that the neural responses for feedback-driven control can be transient and small in variance, posing difficulties for unsupervised inference methods. We thus propose the Behavior-Aligned Neural Dynamics (BAND) model, which exploits semi-supervised learning to extract latent dynamics that predict both feedforward planned movement and unplanned feedback corrections. Our analysis suggests that motor corrections during movement 1) are encoded on the population level in small neural variability in primary motor (M1), but not dorsal premotor (PMd) cortex; 2) are transient; and 3) are driven by sensory feedback. Our work highlights the importance of targeted closed-loop aware methods to extract and study neural dynamics underlying complex behavioral phenomena.

neuroscience↗

bioRxiv2024

The trade-off between temporal precision and effect amplitude of inhibitory plasticity regulation determines separability of learned representations

Synaptic plasticity, the process by which synapses change in an activity-dependent manner, is assumed to be the basis of learning. Experiments demonstrated that synaptic plasticity not only depends on excitatory activity but also on the rate and timing of inhibitory events. Hypothesising that the regulatory effect of inhibition is mediated by membrane potential hyperpolarisation, we identify fast fluctuation sensitivity as a contributing factor to the inhibitory regulation of plasticity. Fast fluctuation sensitivity characterises the influence of fast changes in the membrane potential on the plasticity model predictions, including (short) hyperpolarising events. Furthermore, by introducing a novel plasticity model, the Voltage-Dependent Pathway model, we show that fast fluctuation sensitivity enables a precise temporal regulation of plasticity via inhibition tightly locked to stimulus presentation. In recurrent networks, receptive fields develop independent of the degree of fluctuation sensitivity, yet the temporal precision of inhibitory regulation is critical for the heterogeneity and quality of the resulting representation. Author SummarySynaptic plasticity plays a central role in learning and enables us to interact within an ever-changing environment flexibly. The term governs all processes which leads to a change in connection strength between two neurons. Previously, studies of synaptic plasticity strongly focused on the role of excitatory activity. However, experiments excluding GABA blockers implicate inhibitory neurons to regulate synaptic plasticity. We further investigate the role of inhibitory neurons as regulators of synaptic plasticity in a model study. Specifically, we focus on the decrease in membrane potential due to inhibitory activity as a key actor in plasticity regulation. This comparative modelling study considers two well-established models and a novel model which differ in the order in which filtering and thresholding is applied to the membrane potential. We find that while all models develop connectivity structures resembling receptive fields characteristics of the visual cortex, the quality and heterogeneity of the developed feature detectors is model-specific. Heterogeneity and representation specificity is promoted by a high degree of fast fluctuation sensitivity of the model enabling temporally precise inhibitory regulation of plasticity determined by the models filter and threshold.

neuroscience↗

bioRxiv2022

Can the Insect Path Integration Memory be a Bump Attractor?

Many animal species are able to return to their nest after a foraging excursion without using familiar visual cues to guide them. They accomplish this by using a navigation competence known as path integration, which is vital in environments that do not have prominent visual features. To perform path integration, an animal maintains a running estimate of the distance and direction to its origin as it moves. This distance and direction estimate needs to be maintained in memory until the animal uses it to return to its nest. However, the neural substrate of this memory remains uncertain. A common hypothesis is that the information is maintained in a bump attractors state. We test the bump attractor hypothesis and find that its predictions are inconsistent with the path integration behaviour of ants, thus highlighting the need for alternative models of path integration memory.

animal behavior and cognition↗

bioRxiv2020

The Information Theory of Developmental Pruning: Optimizing Global Network Architecture Using Local Synaptic Rules

During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning. Why networks should initially be over-populated, and processes that determine which synapses and neurons are ultimately pruned, remains unclear. We study the mechanisms and significance of neural pruning in model neural network. In a deep Boltzmann machine model of sensory encoding, we find that (1) synaptic pruning is necessary to learn efficient network architectures that retain computationally-relevant connections, (2) pruning by synaptic weight alone does not optimize network size and (3) pruning based on a locally-available proxy for "sloppiness" based on Fisher Information allows the network to identify structurally important vs. unimportant connections and neurons. This locally-available measure of importance has a biological interpretation in terms of the correlations between presynaptic and postsynaptic neurons, and implies an efficient activity-driven pruning rule. Overall, we show how local activity-dependent synaptic pruning can solve the global problem of optimizing a network architecture. We relate these findings to biology as follows: (I) Synaptic over-production is necessary for activity-dependent connectivity optimization. (II) In networks that have more neurons than needed, cells compete for activity, and only the most important and selective neurons are retained. (III) Cells may also be pruned due to a loss of synapses on their axons. This occurs when the information they convey is not relevant to the target population.

neuroscience↗

bioRxiv2020

Dense movement with embedded sparse action-type representations in the output layer of motor cortex

Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here, we recorded layer 5B population dynamics in the caudal forelimb area of motor cortex while mice performed a forelimb push/pull task and found that most neurons show movement-invariant responses, with a minority displaying movement specificity. Cell-type-specific imaging identified that movement-invariant responses dominated pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons displayed movement-specific signaling. The proportion of IT neurons decoding movement-type peaked prior to movement initiation, while for PT neurons this occurred during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being differentially routed through relatively small, distributed subpopulations of projection neurons.

neuroscience↗

bioRxiv2019

SpikeInterface, a unified framework for spike sorting

Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

neuroscience↗

bioRxiv2019

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over heuristic localization methods such as center of mass.

neuroscience↗

bioRxiv2019

Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

Large-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis. SignificanceDeveloping statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.

neuroscience↗