Biology subjects

Miehl, C.

Publications and source records attributed to Miehl, C..

6 recordsLinked to original sources

bioRxiv2026

Heterogeneous plasticity rules generate diverse structures in recurrent networks

How do local changes at individual synapses give rise to structured recurrent connectivity? Experiments have revealed diverse forms of spike-timing-dependent plasticity (STDP) across the brain, making it difficult to predict which circuit structures different rules will generate. We developed an analytical framework mapping excitatory Hebbian, anti-Hebbian, and symmetric STDP rules to directed three-neuron motifs. Individual homogeneous rules generate restricted subsets of motifs, and no single rule produces all motifs. Combining different rules generates motifs inaccessible to individual rules and yields the full motif repertoire, and these predictions generalize to larger recurrent networks. The mapping is nevertheless highly non-unique, with distinct rules and parameters producing similar motif distributions, including distributions compatible with experimental connectivity. Thus, circuit structure constrains candidate plasticity rules but does not uniquely identify them. Finally, networks with similar motif distributions can nonetheless differ in their firing rates and pairwise correlations, suggesting that activity may provide an additional constraint on candidate plasticity rules.

neuroscience↗

bioRxiv2026

Homeostatic Plasticity Enables Stable, Flexible, and Tunable Assemblies

Strongly interconnected neuronal populations, called assemblies, dynamically form through synaptic plasticity mechanisms and are thought to be a substrate for memories in the brain. Many assembly formation models use Hebbian excitatory-to-excitatory plasticity, where coordinated activity strengthens recurrent structure. However, these models typically yield binary assembly outcomes: networks with either weak (no assembly) or maximally strong (assembly) connectivity. We consider networks with a combination of Hebbian excitatory-to-excitatory plasticity and inhibitory-to-excitatory synapses with plasticity that homeostatically stabilizes excitatory neuron firing at a target value. When we set excitatory-to-excitatory plasticity to be homeostatically compliant, in that potentiation and depression are balanced at the homeostatic target firing rate, we find a stable continuum of synaptic strengths, and assembly structure is no longer binary. We use a recurrent network of spiking neuron models and an associated mean-field theory to identify this continuum as a line attractor in synaptic weight space. While along the attractor, homeostasis ensures that neuronal firing rates are invariant, the dynamical response properties of the network are quite malleable, with strongly coupled networks having high gain and longer timescale responses. Using our mean-field theory we show how correlated stochastic spiking activity among the excitatory neurons can destroy the line attractor, yet this can be mitigated when correlated inputs are shared across the excitatory and inhibitory neurons. Altogether, we provide a learning framework based on homeostasis, where a tunable and flexible assembly structure is possible.

neuroscience↗

bioRxiv2025

Assembly-based computations through contextual dendritic gating of plasticity

Neuronal assemblies - groups of strongly connected neurons - are considered the basic building blocks of perception and memory in the brain by encoding representations of specific concepts. Despite recent evidence for the biological basis behind the existence and formation of such assemblies, computational models often fall short of showing how assemblies can be flexibly learned and combined to perform real-world computations. A prominent problem is catastrophic forgetting, where learning a new assembly can disrupt existing connectivity structure and lead to forgetting previously learned assemblies. We propose a biologically plausible computational model, where dendritic compartments (instead of neurons) are the loci for learning and inhibition gates learning in a dendrite-specific manner, to flexibly learn new stimuli without forgetting of old ones. By learning stable projections from one brain region into another and associations between different brain regions, we demonstrate how the proposed assembly framework implements the basic building blocks for diverse computations. In a visual-auditory association task, we demonstrate how the context-specific assembly computations can be used to correctly separate ambiguous stimuli based on their dendritic representations. Our models provide unique insights and predictions for how hierarchically connected brain areas use their biological components to implement flexible yet robust learning.

neuroscience↗

bioRxiv2025

FACED 2.0 enables large-scale voltage and calcium imaging in vivo

Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. We optimized a kilohertz-frame-rate two-photon fluorescence microscope with all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to voltage and calcium imaging in vivo, we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons.

neuroscience↗

bioRxiv2022

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Synaptic changes underlie learning and memory formation in the brain. But synaptic plasticity of excitatory synapses on its own is unstable, leading to unlimited growth of synaptic strengths without additional homeostatic mechanisms. To control excitatory synaptic strengths we propose a novel form of synaptic plasticity at inhibitory synapses. We identify two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change in the same direction as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity achieve a fixed excitatory/inhibitory set-point in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

neuroscience↗

bioRxiv2020

The generation of cortical novelty responses through inhibitory plasticity

Animals depend on fast and reliable detection of novel stimuli in their environment. Indeed, neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular and synaptic mechanisms underlie those responses. Here, we show that inhibitory synaptic plasticity readily generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. Generated novelty responses do not depend on the exact temporal structure but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically-plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make numerous experimentally testable predictions.

neuroscience↗