Biology subjects

Vogels, T. P.

Publications and source records attributed to Vogels, T. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Dale's law, stability and nonlinearity are sufficient constraints to ignite transient and self-sustained neural dynamics

The motor cortex orchestrates a rich and flexible repertoire of network dynamics for rhythmic and goal-directed movements. Computational studies have begun to illuminate the mechanistic origins of this repertoire, but a comprehensive model that can explain the emergence of both transient and self-sustained dynamics is still missing. Here, we show that three simple ingredients: Dalean connectivity, stability, and nonlinear neural responses, suffice to reverse-engineer networks that produce transient, steady-state, and self-sustained periodic activity. A single dynamical principle underlies this repertoire: the interaction of non-normal amplification, inherent to Dalean networks, with neuronal nonlinearity, so to ignite and sustain multi-stable, controllable dynamics. Our approach yields entire families of connectivity matrices that require no hand-tuning or learning of weights. Without fitting them to data, these networks reproduce the population-level signatures of motor cortex, implying that the richness of cortical dynamics need not be sculpted by learning, but may emerge from simple biological ingredients.

neuroscience↗

bioRxiv2026

Learning to play with spikes: characterizing, predicting, and engineering unsupervised plasticity rules for spiking reservoir computing

Spiking reservoir computing, and reservoir computing more generally, is a powerful and efficient framework for neuromorphic and biological applications, in which a fixed random reservoir drives a trained readout. Its performance depends critically on the reservoir initialization, so that enriching the reservoir with adaptive, unsupervised plasticity rules offers a natural solution to this limitation. However, it is unclear which rules work, or why, and how to find the best-suited rules for specific tasks without exhaustive and expensive search. Here, we learn how to play the Atari game Pong in a plastic spiking reservoir network. We systematically characterize a large family of local plasticity rules that were meta-learned in prior work. We then show that their performance is predictable and structured: high-scoring rules are characterized by strong differentiation between neurons encoding the ball trajectory and the background, with stable weight dynamics, and consistent readout alignment across time. These mechanistic signatures are not task-specific and transfer to a delayed-match recognition task. Rule performance can be estimated from rule parameters alone. Finally, conditioning simulation-based inference (SBI) on high scores allows us to sample directly from promising regions of the rule space, to discover rules that exceed the performance of those found in the prior distribution and reveal stable, high-performing configurations. Together, these results offer competitive performance against classical reservoir computing while providing a transparent, interpretable account of what makes a plasticity rule useful for neuromorphic hardware and biological computing.

neuroscience↗

bioRxiv2026

Ingrams for engrams: co-active inhibitory-inhibitory plasticity shapes inhibitory assemblies that stabilize and recall embedded engrams through disinhibition

Inhibitory neurons have been widely understood to play a supporting role in neural function and memory formation, but recent advances highlight that memories are encoded by both excitatory and inhibitory neurons (EI assemblies), carving out a bigger role for inhibition beyond mere stabilization. However, the computations enabled by such EI assemblies are still unclear, and the synaptic plasticity rules that could sustain and retrieve memories are unknown. Here, we construct a computational model for EI assembly recall and investigate the computational benefits of such mixed engrams over classical, excitatory-only engrams. Towards this end, we consider large recurrent spiking networks with symmetrical Hebbian synaptic plasticity at both inhibitory-to-inhibitory (I-to-I) and inhibitory-to-excitatory (I-to-E) synapses. The conjunction of these rules can robustly stabilize embedded EI assemblies in the asynchronous irregular regime. Assemblies can then be reactivated by two distinct mechanisms: direct stimulation of the engram or disinhibition through the inhgram. Both mechanisms of recall lead to reliable pattern completion and separation. Crucially, we show that networks that lack I-to-I plasticity show weak recall and cannot discriminate between overlapping engrams via disinhibitory activation. This suggests that inhibitory plasticity can facilitate recall of overlapping engrams, with inhibitory neurons controlling multiple excitatory engrams. Furthermore, we show that inhgrams can emerge from pre-existing E-I-E loops in the network's random connectivity. Our work proposes that inhibitory plasticity is a plausible mechanism for high-quality disinhibitory recall, allowing inhibitory neurons to selectively control multiple excitatory engrams through synapse-specific plasticity rules.

neuroscience↗

bioRxiv2026

Transcription-specific synaptic plasticity mediates engram computations

Memory is encoded by sparse ensembles of neurons. While activity-dependent transcription and learning-induced synaptic plasticity are causally related to memory formation and recall, the link between the transcriptional regulation of synaptic plasticity and memory computations remains elusive. In particular, even though transcriptionally defined neuronal ensembles within a memory engram exhibit specific forms of synaptic plasticity and support distinct behavioral outputs, it is still unclear whether transcription-specific synaptic plasticity drives ensemble computations, rather than merely serving as a marker of ensemble identity. Here, we demonstrate that transcription-specific synaptic plasticity enables ensemble computations essential for learning-induced adaptive behaviors. In the mouse dentate gyrus (DG), we found that neuronal ensembles genetically defined by Fos-dependent transcription engage plasticity in feedforward excitatory synapses, whereas those defined by Npas4-dependent transcription engage plasticity in recurrent inhibitory synapses. We modeled spiking neural networks with transcription-specific synaptic plasticity and observed that Fos- and Npas4-dependent ensembles emerged following learning and stabilized with memory consolidation. Our computational model predicted that blocking Fos- or Npas4-dependent synaptic plasticity disrupts memory generalization and discrimination, respectively. By acutely deleting Fos or Npas4 in the DG to selectively block Fos- or Npas4-dependent synaptic plasticity, we conducted contextual fear conditioning experiments whose results supported our computational model's prediction. Our study provides causal evidence that specific transcriptional programs induced in distinct neuronal ensembles differentially engage synaptic plasticity and thereby regulate memory computations.

neuroscience↗

bioRxiv2026

Spinal circuit regionalization diversifies motor output along the vertebrate body axis

The evolution of the vertebrate limb-torso-limb body plan drove a diversification of motor behavior. How neural circuits are organized to generate distinct outputs across body regions, however, remains unknown. Here, we construct a cross-species, spatiotemporal atlas of spinal interneurons spanning the rostrocaudal and developmental axes of frogs and mice. We uncover a common framework of interneuron regionalization that mirrors each vertebrates body plan and arises through Hox-dependent regulation of rostrocaudal neurogenesis. Combining electrophysiology with computational modeling, we show that changing regional circuit composition is sufficient to respecify motor output. Together, these findings establish a causal link between developmental patterning, circuit architecture, and motor function, identifying interneuron regionalization as a fundamental organizational principle linking body-plan evolution to motor diversity.

neuroscience↗

bioRxiv2025

An ion channel omnimodel for standardized biophysical neuron modelling

Biophysical neuron modeling is an indispensable tool in neuroscience research, with the combination of diverse ion channel kinetics and morphologies being used to explain various single-neuron properties and responses. Despite this, there is no standard way of formulating ion channel models, making it challenging to relate models to one another and experimental data. Here, we revive the idea of a standard model for ion channels based on the Hodgkin-Huxley formulation, and apply it to a recently curated database of ion channel models. We demonstrate that this standard formulation, which we refer to as an omnimodel, accurately fits the majority of voltage-gated models in the database (over 3,000 models). It produces similar, if not identical, responses to voltage-clamp protocols in simulations where the ion channel omnimodels were used. Importantly, the standard formalism enables easy comparison of models based on parameter settings. It can also be used to make new observations about the space of ion channel kinetics found in neurons. Furthermore, it facilitates the inference of ion channel parameters from the responses to standard protocols. We provide an interactive platform to compare and select channel models, and encourage the community to use this standard formulation whenever possible, to facilitate understanding and comparison among models.

neuroscience↗

bioRxiv2025

Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of strikingly diverse quadruplets of excitatory(E)-to-E, E-to-inhibitory(I), IE, and II plasticity rules, cooperating to stabilize recurrent spiking networks. Despite the fact that quadruplets were selected for homeostasis, more than 90% of them performed well in simple and more difficult memory tasks such as novelty detection, contextual novelty and sequence replay. Co-activity was crucial, i.e., most rules failed in isolation. Our purely local, unsupervised plasticity rules could also help solve computer games such as pong. Our work showcases automated discovery augmenting human intuition to find en masse solutions for high dimensional problems.

neuroscience↗

bioRxiv2025

Nightingales imitate the duration of whistle syllables

While real-time vocal adjustments are crucial for interactive communication, not much is known about the spectral and temporal vocal flexibility of animals. Here, using a combination of field song recordings and controlled playback experiments, we show that wild nightingales imitate whistle song syllable durations in real time. However, when exposed to playbacks with variations in whistle pitch and duration that are beyond their natural range of vocalizations, nightingales emulated temporal or spectral features, approximating the whistle playbacks within the constraints of their natural whistle song repertoire. Our findings reveal a previously unknown dimension of real-time vocal flexibility in songbirds and suggest complex auditory-motor integration during song interactions.

animal behavior and cognition↗

bioRxiv2024

Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

Synaptic plasticity is a key player in the brains life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta-learn local co-active plasticity rules in large recurrent spiking net-works, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta-learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain net-work dynamics to plausible solutions a priori. Moreover, in line with previous work, we can [fi]nd multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.

neuroscience↗

bioRxiv2024

Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species

While impairing neuronal metabolism limits brain performance, it remains poorly understood whether enhancing metabolism in neurons, in contrast, could boost brain function. We find that reducing the expression of the mitochondrial H+/Ca2+ exchanger Letm1 results in increased Ca2+ retention in the mitochondrial matrix of firing neurons, which overactivates neuronal metabolism in flies and rodents. We find that upscaled metabolic states in active neurons of central memory circuits of flies and mice enable storing long-term memories in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in active neurons and prove its crucial role in governing higher brain functions, such as long-term memory formation. Highlights- Letm1 controls activity-driven mitochondrial Ca2+ efflux in neurons - Increased mitochondrial Ca2+ retention during activity overactivates neuronal metabolism - Activity-driven upscaling of neuronal metabolism facilitates long-term olfactory memory in flies and mice

neuroscience↗

bioRxiv2023

Indistinguishable network dynamics can emerge from unalike plasticity rules

Synaptic plasticity is thought to be critical for building and maintaining brain circuits. Models of plasticity, or plasticity rules, are typically designed by hand, and evaluated based on their ability to elicit similar neuron or circuit properties to ground truth. While this approach has provided crucial insights into plasticity mechanisms, it is limited in its scope by human intuition and cannot identify all plasticity mechanisms that are consistent with the empirical data of interest. In other words, focusing on individual hand-crafted rules ignores the potential degeneracy of plasticity mechanisms that explain the same empirical data, and may thus lead to inaccurate experimental predictions. Here, we use an unsupervised, adversarial approach to infer plasticity rules directly from neural activity recordings. We show that even in a simple, idealised network model, many mechanistically different plasticity rules are equally compatible with empirical data. Our results suggest the need for a shift in the study of plasticity rules, considering as many degenerate plasticity mechanisms consistent with data as possible, before formulating experimental predictions.

neuroscience↗

bioRxiv2022

Metabolically driven action potentials serve neuronal energy homeostasis and protect from reactive oxygen species

So-called spontaneous neuronal activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its origin and purpose remain unclear. Here, we propose that non-input driven firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. We built a framework of models that incorporate homeostatic control of metabolic products-ATP, ADP, and reactive oxygen species, among others-by way of changes in firing. Our theory can account for key features of neuronal activity observed in many experiments in studies ranging from ion channels function all the way to resting state dynamics. We propose an integrated, crucial role for metabolic spiking that bridges the gap between metabolic homeostasis and neuronal function. Finally, we make testable predictions to validate or falsify our theory.

neuroscience↗

bioRxiv2021

Developmentally-regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome

Dravet syndrome (DS) is a neurodevelopmental disorder defined by epilepsy, intellectual disability, and sudden death, due to heterozygous variants in SCN1A with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing parvalbumin GABAergic interneurons (PV-INs) from pre-weanling Scn1a+/- mice show impaired action potential generation. A novel approach assessing PV-IN function in the same mice at two developmental time points showed that, at post-natal day (P) 16-21, spike generation was impaired all mice, deceased prior or surviving to P35. However, synaptic transmission was selectively dysfunctional in pre-weanling mice that did not survive. Spike generation in surviving mice normalized by P35, yet we again identified abnormalities in synaptic transmission. We conclude that combined dysfunction of PV-IN spike generation and synaptic transmission drives disease severity, while ongoing dysfunction of synaptic transmission contributes to chronic pathology. Modeling revealed that PV-IN axonal propagation is more sensitive to decreases in sodium conductance than spike generation.

neuroscience↗

bioRxiv2021

Interacting synapses stabilise both learning and neuronal dynamics in biological networks

The brains functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity they also affect each other. The nature of such "codependency" is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalises synaptic codependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity, while neighbouring excitatory-excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, codependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighbourly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.

neuroscience↗

bioRxiv2021

Regimes and mechanisms of transient amplification in abstract and biological networks

We use upper triangular matrices as abstract representations of neuronal networks and directly manipulate their eigenspectra and non-normality to explore different regimes of transient amplification. Counter-intuitively, manipulating the imaginary distribution can lead to highly amplifying regimes. This is noteworthy, because biological networks are constrained by Dales law and the non-existence of neuronal self-loops, limiting the range of manipulations in the real dimension. Within these constraints we can further manipulate transient amplification by controlling global inhibition.

neuroscience↗

bioRxiv2021

Developmental depression-facilitation shift controls excitation-inhibition balance

Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modelling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of synaptic working memory mediated by short-term facilitation. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences.

neuroscience↗

bioRxiv2020

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network

The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by - and fitted to - experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and "Ojas rule", a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Ojas rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions.

neuroscience↗

bioRxiv2020

The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivatives scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.

neuroscience↗