Biology subjects

Turcu, D.

Publications and source records attributed to Turcu, D..

8 recordsLinked to original sources

bioRxiv2026

Discrete synaptic states and context-modulated readouts support continual learning

Biological neural systems learn new behaviors while retaining earlier ones, whereas sequentially trained artificial networks often overwrite parameters and forget previously learned behaviors. We introduce the Context-modulated Synaptic-states Recurrent Neural Network (CoSyn-RNN), a new continual-learning model inspired by heterogeneous synaptic states and neuromodulatory control. CoSyn-RNN combines sparse recurrent allocation with a discrete protection rule: after each task, recurrent neurons whose synaptic weights cross a fixed threshold form a task-specific neuron group, and their associated parameters enter a protected state during later learning. A neuron-specific gain and a task-cued mask over a fixed base readout control how recurrent activity contributes to behavior. Across various task sequences consisting of many cognitive tasks, training and validation losses and accuracies were maintained throughout learning. The protection rule produced modular, task-ordered recurrent structure compatible with forward reuse of earlier computations. Pairwise recruitment measurements further defined minimum- and maximum-cost curricula that produced markedly different final recurrent structures and different remaining capacity for future learning. Thus, CoSyn-RNN provides a biologically inspired model in which protected synaptic states, sparse allocation, and context-dependent modulation support continual learning.

neuroscience↗

bioRxiv2026

Learning using switching synaptic plasticity rules

Synapses differ in molecular and ultrastructural state, but the computational role of this heterogeneity remains unclear and computational models do not account for it. Local Hebbian-like plasticity is well supported experimentally, whereas learning complex behaviors most likely requires non-local credit-assignment signals. Recent cortical electron microscopy data suggest that strong and weak synapses occupy distinct structural states, with large synapses often containing a spine apparatus that can alter calcium dynamics and plasticity. Here, we assigned different computational roles to different morphological synaptic types: we asked how networks with state-dependent plasticity operate and whether the mechanisms and structural features they develop are consistent with biological observations. We built a plasticity-switching recurrent neural network (psRNN) in which synaptic state is modeled by recurrent-weight thresholds. Synapses in the low-weight state use a local Hebbian-like rule, whereas synapses in the high-weight state receive an idealized non-local credit-assignment signal implemented with backpropagation through time. psRNNs learned cognitive tasks with temporal and working-memory demands in fewer trials than fully backpropagation-trained RNNs, despite routing BP-based updates to fewer recurrent weights. The advantage arose from three interacting mechanisms: 1) Hebbian-like updates exposed the credit-assignment component to several nearby parameter configurations, 2) the network dynamically built a task-relevant recurrent initialization, and 3) synapses switched into the credit-assignment subset before Hebbian growth destabilized learning. Structurally, trained psRNNs produced more anti-symmetric weights, lower effective rank, and stronger feedforward structure than control RNNs. Our results shed light on the computational role of heterogeneous synapses, which may regulate distinct types of learning signals, and we generate falsifiable predictions for connectomic analyses of recurrent cortical circuits.

neuroscience↗

bioRxiv2025

Connectome simulations identify a central pattern generator circuit for fly walking

Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input. However, the circuit structure of a CPG for walking is not known in any animal. To identify the cells and synapses that underlie rhythmic leg movement in walking flies, we developed dynamic simulations of the Drosophila ventral nerve cord (VNC) connectomes. A computational activation screen of descending neurons from the central brain identified DNg100--a known command neuron for walking--as the top driver of rhythmic leg motor activity. Simulated network pruning isolated a minimal rhythm-generating circuit consisting of one inhibitory and two excitatory interneurons; this three-neuron circuit was necessary and sufficient for motor rhythms across all six legs and in four connectome datasets. Simulations also predicted that a separate descending pathway (DNb08) drives rhythmic leg movements, which we confirmed experimentally using optogenetics in behaving flies. Our results reveal the cellular identity and synaptic structure of a putative CPG circuit for walking and other rhythmic leg movements in flies.

neuroscience↗

bioRxiv2024

An end-to-end model of active electrosensation

1Weakly electric fish localize and identify objects by sensing distortions in a self-generated electric field. Fish can determine the resistance and capacitance of an object, for example, even though the field distortions being sensed are small and highly-dependent on object distance and size. Here we construct a model of the responses of the fishs electroreceptors on the basis of experimental data, and we develop a model of the electric fields generated by the fish and the distortions due to objects of different resistances and capacitances. This provides us with an accurate and efficient method for generating large artificial data sets simulating fish interacting with a wide variety of objects. Using these sets, we train an artificial neural network (ANN), representing brain areas downstream of electroreceptors, to extract the 3D location, size, and electrical properties of objects. The model performs best if the ANN operates in two stages: first estimating object distance and size and then using this information to extract electrical properties. This suggests a specific form of modularity in the electrosensory system that can be tested experimentally and highlights the potential of end-to-end modeling for studies of sensory processing.

neuroscience↗

bioRxiv2022

Sparse RNNs can support high-capacity classification

Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers. Although convergent output-unit like neurons may exist in some biological neural circuits, notably the cerebellar cortex, neocortical circuits do not exhibit any obvious candidates for this role; instead they are highly recurrent. We investigate whether a sparsely connected recurrent neural network (RNN) can perform classification in a distributed manner without ever bringing all of the relevant information to a single convergence site. Our model is based on a sparse RNN that performs classification dynamically. Specifically, the interconnections of the RNN are trained to resonantly amplify the magnitude of responses to some external inputs but not others. The amplified and non-amplified responses then form the basis for binary classification. Furthermore, the network acts as an evidence accumulator and maintains its decision even after the input is turned off. Despite highly sparse connectivity, learned recurrent connections allow input information to flow to every neuron of the RNN, providing the basis for distributed computation. In this arrangement, the minimum number of synapses per neuron required to reach maximum memory capacity scales only logarithmically with network size. The model is robust to various types of noise, works with different activation and loss functions and with both backpropagation- and Hebbian-based learning rules. The RNN can also be constructed with a split excitation-inhibition architecture with little reduction in performance.

neuroscience↗

bioRxiv2020

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

Spike sorting is a critical first step in extracting neural signals from large-scale multi-electrode array (MEA) data. This manuscript presents several new techniques that make MEA spike sorting more robust and accurate. Our pipeline is based on an efficient multi-stage "triage-then-cluster-then-pursuit" approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or "collided" events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection and denoising method followed by efficient outlier triaging. The denoised spike waveforms are then used to infer the set of spike templates through nonparametric Bayesian clustering. We use a divide-and-conquer strategy to parallelize this clustering step. Finally, we recover collided waveforms with matching-pursuit deconvolution techniques, and perform further split-and-merge steps to estimate additional templates from the pool of recovered waveforms. We apply the new pipeline to data recorded in the primate retina, where high firing rates and highly-overlapping axonal units provide a challenging testbed for the deconvolution approach; in addition, the well-defined mosaic structure of receptive fields in this preparation provides a useful quality check on any spike sorting pipeline. We show that our pipeline improves on the state-of-the-art in spike sorting (and outperforms manual sorting) on both real and semi-simulated MEA data with > 500 electrodes; open source code can be found at https://github.com/paninski-lab/yass.

neuroscience↗

bioRxiv2019

Nuclear upregulation of PI3K p110β correlates with increased rRNA transcription in endometrial cancer cells

Genes encoding for components of the phosphoinositide 3-kinase (PI3K) pathway are frequently mutated in cancer, including inactivating mutations of PTEN and activating mutations of PIK3CA, encoding the PI3K catalytic subunit p110. PIK3CB, encoding p110{beta}, is rarely mutated, but can contribute to tumourigenesis in some PTEN-deficient tumours. The underlying molecular mechanisms are however poorly understood. By analysing cell lines and annotated clinical samples, we have previously found that p110{beta} is highly expressed in endometrial cancer (EC) cell lines and that PIK3CB mRNA levels increase early in primary tumours correlating with lower survival. Selective inhibition of p110 and p110{beta} led to different effects on cell signalling and cell function, p110 activity being correlated to cell survival in PIK3CA mutant cells and p110{beta} with cell proliferation in PTEN-deficient cells. To understand the mechanisms governing the differential roles of these isoforms, we assessed their sub-cellular localisation. p110 was cytoplasmic whereas p110{beta} was both cytoplasmic and nuclear with increased levels in both compartments in cancer cells. Immunohistochemistry of p110{beta} in clinically annotated patient tumour sections revealed high nuclear/cytoplasmic staining ratio, which correlated significantly with higher grades. Consistently, the presence of high levels of p110{beta} in the nuclei of EC cells, correlated with high levels of its product phosphatidylinositol 3,4,5-trisphosphate (PtdIns(3,4,5)P3) in the nucleus. Using immunofluorescence labelling, we observed both p110{beta} and PtdIns(3,4,5)P3 in the nucleoli of EC cell lines. The production of nucleolar PtdIns(3,4,5)P3 was dependent upon p110{beta} activity. EC cells with high levels of nuclear PtdIns(3,4,5)P3 and p110{beta} showed elevated nucleolar activity as assessed by the increase in 47S pre-rRNA transcriptional levels in a p110{beta}-dependent manner. Altogether, these results present a nucleolar role for the PI3K pathway that may contribute to tumour progression in endometrial cancer.

molecular biology↗

bioRxiv2019

Cholesterol is a strong promoter of an α-Synuclein membrane binding mode that accelerates oligomerization

Dysregulation of the biosynthesis of cholesterol and other lipids has been implicated in neurological diseases, including Parkinson's disease, where the misfolding of membraneassociated -Synuclein is a key molecular event. Recent research also suggests that -Synuclein aggregation is influenced by the lipid environment. The exact molecular mechanisms responsible for cholesterols effect on -Synuclein binding to lipids and how this binding may affect -Synuclein oligomerization and fibrillation remain elusive, as does the relative importance of cholesterol versus other lipid factors. We probed the interactions and fibrillation behaviour of -Synuclein using SMA nanodiscs, containing zwitterionic and anionic lipid model systems with and without cholesterol. SPR and ThT fluorescence assays were then employed to monitor -Synuclein binding, as well as fibrillation in the absence and presence of membrane models. 1H-15N correlated NMR was used to monitor the fold of -Synuclein in response to nanodisc binding, and we determined individual residue apparent affinities for the nanodisc-contained bilayers. Cholesterol inhibited -Synuclein interaction with lipid bilayers. We also find that cholesterol significantly promotes -Synuclein fibrillation, with a more than 20-fold reduction of lag-times before fibrillation onset. When -Synuclein-bilayer interactions were analysed for individual residues by solution-state NMR, we observed two different effects of cholesterol. In nanodiscs made of DOPC, cholesterol modulated the NAC part of -Synuclein, leading to stronger interaction of this region with the lipid bilayer. In contrast, in the nanodiscs comprising DOPC, DOPE and DOPG, the NAC part was mostly unaffected by cholesterol, while the binding of the N-terminal and the C-terminal were both inhibited.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC=\"FIGDIR/small/725762v3_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@1c9c126org.highwire.dtl.DTLVardef@a7e026org.highwire.dtl.DTLVardef@16d1a9forg.highwire.dtl.DTLVardef@1eeda2d_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗