Biology subjects

Naud, R.

Publications and source records attributed to Naud, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Functional Continuum of GABAergic Synaptic Dynamics Reflects Genetic Identities

The language of the brain is articulated by temporal patterns of neuronal activity, which individual synapses interpret through distinct forms of synaptic dynamics. While glutamatergic synapses have been proposed to use a handful of functionally distinct types of short-term plasticity (STP) that loosely align with genetic identities, the functional organization of GABAergic synaptic dynamics remains ambiguous. Here, we ask whether the diversity of GABAergic dynamics also clusters into discrete functional types or instead forms a continuum, and whether this functional organization also aligns with genetic identities. Using a combination of machine learning and synaptic modeling on the Allen Institute Synaptic Physiology Dataset, we present evidence that inhibitory dynamics organize into a functional continuum with overlapping modes corresponding to different genetic identities. This continuum spans Parvalbumin (Pvalb) to Vasoactive Intestinal Peptide (VIP) presynaptic neurons, with Somatostatin (Sst) neurons forming an intermediate class. Across the three classes, most inhibitory synapses showed either strong depression or a biphasic form of plasticity that is facilitating at higher stimulation frequencies and depressing at lower ones. However, a higher proportion of Sst and VIP synapses, compared to Pvalb synapses, exhibited either depression restricted to high frequencies or consistent facilitation. When paired with functional subtypes of excitatory synaptic dynamics, this inhibitory continuum completes a unified framework for understanding information flow in the cortical microcircuit, advancing long-standing efforts to explain circuit function by investigating the organization of its components.

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bioRxiv2026

Uncovering the latent structure of interwoven population and temporal codes

Population analysis methods have become standard for navigating the complexity of neural data. However, these methods often assume a rate code, neglecting information encoded in the precise timing of spikes. Critically, additional information encoded in bursts of action potentials may be missed. Here, we develop a factor analysis method that disentangles the factors associated with bursts and individual spikes. This enables burst codes to be investigated directly from the structure of the data, without requiring external covariates. We demonstrate that analyzing firing rates alone obscures the latent structure and factors underlying bursts. Applying our method to simulated and experimental data, we show that it can infer the correct latent structure and be used to test for the presence of burst coding. By merging the population and burst coding perspectives, we provide a framework for linking changes in bursting to internal variables involved in attention, perception, and learning.

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bioRxiv2025

Ca2+ Plateau Potentials Reflect Cross-Theta Cortico-Hippocampal Input Dynamics and Acetylcholine for Rapid Formation of Efficient Place-Cell Code

A central tenet of Systems Neuroscience lies in an understanding of memory and behavior through learning rules, but synaptic plasticity has rarely been shown to create functional single-neuron code in a causal and biophysically rooted manner. Behavioral Time-Scale Synaptic Plasticity (BTSP), identified in vivo, holds a great potential for explaining instantaneous hippocampal selectivity emergence by long-term potentiation (LTP), yet the cellular and endogenous mechanisms are unknown, impeding broader conceptualization of this novel rule for its algorithmic, systems-level and theoretical implications. Here, we addressed this gap by in-vivo, ex-vivo, in-silico and computational approaches to seek neurophysiologically inspired protocols for synaptically evoking Ca2+ plateau potentials and inducing potentiation in the CA1. We found induction of BTSP-LTP is best explained by a theta-oscillation-paced, gradually developed cellular state being supported with precisely timed weak ramping inputs. Remarkably, the previously presumed one-shot LTP for in-vivo place-field formation is possible under the influence of muscarinic activation. Through modeling, the notion of acetylcholine-gated BTSP gave rise to a computational advantage for low-interference continual learning. We further demonstrated that biophysics of Transient Receptor Potential (TRPM) and NMDA receptor (NMDAR) channels powerfully shapes the cross-theta dynamics underlying BTSP. These results which cover pre-, post-synaptic and neuromodulatory factors and their timing suggest fundamental principles for graded plateau potentials and hippocampal LTP induction. Overall, our work dissects cellular mechanisms potentially important for a prominent in-vivo hippocampal plasticity phenomenon, and offers a biological basis for framing BTSP as an input-dynamics-aware, neuromodulation-tuned synaptic algorithm.

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bioRxiv2025

Electrospectrometry of the Mouse Brain

A powerful computational feature of the brain is its ability to compartmentalize different functions in a way that can be flexibly recombined. Experimental evidence for such modularity arose from cytoarchitecture, connectivity, and electroencephalograms, while magnetic resonance imaging could attest that this modularity is dynamic. More spatially precise and more widely recordable, the local field potential (LFP) has an unknown brain wide organization. Here, we developed deep electrospectroscopy, a method to assess nonlinear spectral similarity between the LFP of different areas. A brain-wide application of this technique to the mouse brain revealed an organization composed of groups of spectrally similar areas. Such communities were mostly found in the fore-brain and showed an organization that did not strictly follow the cytoarchitecture. For instance, visual parts of the cortex and the visual parts of the colliculus were found in the same community. These electrospectral communities reshaped with context, showing splitting and merging operations, growing in size around brain areas required for a task. In particular, upper and lower limb parts of the somatosensory cortex were primarily in separate communities but merged during a task that required turning a wheel. Similarly, oculomotor reflexes and associative parts of the thalamus merged during the visuo-motor task. These analyzes show that LFPs are organized in a modular fashion, offering a window onto subcortical and layer-specific contributions to the compositionality of cortical brain functions.

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bioRxiv2025

Extracting Value Coding Features from Individual Serotonin Neurons

Adaptive behaviour requires animals to continually reevaluate the appetitive or aversive quality of their surroundings. Dorsal raphe serotonin neurons, the main source of serotonergic input to the forebrain, have been implicated in both signaling the quality of an animals environment and regulating reward-seeking and punishmentavoiding behaviour, but the precise quantity signaled by these neurons has remained unclear, as well as how these neurons relate with behaviour. Using open-access recordings of serotonergic neurons of the dorsal raphe nucleus while animals perform a dynamic Pavlovian task, we compare firing rate and behavioral data with a model that considers reward history accumulated over a tunable timescale. Our Bayesian parameter estimation supports that serotonergic neurons are consistent with reward history being estimated over about a hundred trials on average, with a heterogeneity across individual neurons spanning 30 to 300 trials. Anticipatory licking also correlated with reward history at multiple timescale, but could not be dissociated from that of a time/thirst nuisance variable and otherwise mostly on a timescale faster than seen in serotonergic cells. These results provide a more precise picture of the dynamics of serotonergic cells under a dynamic Pavlovian task.

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bioRxiv2025

Synaptic and intrinsic membrane defects disrupt early neural network dynamics in Down syndrome

Down syndrome, caused by trisomy 21, affects around six million people worldwide and features learning, memory and language deficits. However, the mechanisms underlying trisomy 21 neurophenotypes involving human cortical circuitry are unknown. By characterising developing neural network dynamics and single cell excitability profiles, with synaptic and voltage-dependent ion channel behaviour, using an isogenic induced pluripotent stem cell- derived neuronal model, we show that trisomy 21 impairs the activity and development of cortical circuitry. This is caused by deficient glutamatergic synaptic connectivity and by aberrant intrinsic membrane properties involving K+ and Na+ channels culminating in spike firing defects that weaken neural network activity and disrupt the synchrony of developing neurons. We also identify transiently activated A-type K+ channels, specifically Kv4.3 channels, as a key orchestrator for Down syndrome during neurodevelopment. Overall, these excitability changes will significantly contribute towards the aberrant neurophenotypes observed later on in life.

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bioRxiv2024

Functional separation of long-range inputs by intrinsic dynamics of dorsal raphe 5-HT neurons

Monoaminergic nuclei such as the serotonergic dorsal raphe nucleus (DRN) receive synaptic inputs containing functionally distinct streams of information, yet the dimensionality of the resulting output population code and its cellular underpinning are currently unknown. By combining electrophysiological and computational approaches, here we uncover separable neural encoding of two excitatory inputs conveying disjunct information to DRN 5-HT neurons - the lateral habenula (LHb) and medial prefrontal cortex (mPFC). Dual-color opsin strategies revealed that a population of 5-HT neurons receive inputs from both mPFC and LHb. Subthreshold excitatory postsynaptic potentials triggered by both inputs were largely indistinguishable, yet suprathreshold spiking behavior exhibited input-specific latencies and dispersion statistics. A support vector machine classifier demonstrated that input identity can be accurately decoded from spike timing, but not subthreshold events, of under ten 5-HT neurons. Upon examining the intrinsic cellular mechanisms in 5-HT neurons that couple EPSPs to spiking dynamics, we uncovered two likely candidate mechanisms: a low-threshold calcium conductance that selectively boosts slow excitatory inputs, and a subthreshold, voltage-dependent membrane noise that generates variation of spike latency and jitter. Stochastic simulations suggest that these two intrinsic properties of 5-HT neurons are sufficient to transform LHb and mPFC inputs into distinct output spiking patterns. These results reveal that hub-like networks like the DRN can segregate distinct informational streams by a cell-intrinsic mechanism. The resulting emergent population spike synchrony code provides a means for the DRN to widely broadcast these streams as a multiplexed signal. Significance statementPhylogenetically old neuromodulatory systems in the brain, such as the serotonergic dorsal raphe nucleus, are compact yet richly innervated structures. Here, we use the raphe as a testbed to ask how distinct informational sources to hub-like networks are processed or integrated into a coherent neural code. Using electrophysiological and computational methods, including biophysically grounded stochastic simulations, we find that intrinsic noise mechanisms in serotonergic neurons are critical to transform approximately matched subthreshold excitation into distinct spike timing profiles. Thus, cell-intrinsic noise mechanisms can effectively synthesize a spike synchrony code that, we hypothesize, multiplexes input information to hub-like networks at the population level even in the absence of strong local circuit interactions.

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bioRxiv2024

A Burst-Dependent Algorithm for Neuromorphic On-Chip Learning of Spiking Neural Networks

The field of neuromorphic engineering addresses the high energy demands of neural networks through brain-inspired hardware for efficient neural network computing. For on-chip learning with spiking neural networks, neuromorphic hardware requires a local learning algorithm able to solve complex tasks. Approaches based on burst-dependent plasticity have been proposed to address this requirement, but their ability to learn complex tasks has remained unproven. Specifically, previous burst-dependent learning was demonstrated on a spiking version of the XOR problem using a network of thousands of neurons. Here, we extend burst-dependent learning, termed Burstprop, to address more complex tasks with hundreds of neurons. We evaluate Burstprop on a rate-encoded spiking version of the MNIST dataset, achieving low test classification errors, comparable to those obtained using backpropagation through time on the same architecture. Going further, we develop another burst-dependent algorithm based on the communication of two types of error-encoding events for the communication of positive and negative errors. We find that this new algorithm performs better on the image classification benchmark. We also tested our algorithms under various types of feedback connectivity, establishing that the capabilities of fixed random feedback connectivity is preserved in spiking neural networks. Lastly, we tested the robustness of the algorithm to weight discretization. Together, these results suggest that spiking Burstprop can scale to more complex learning tasks and can thus be considered for self-supervised algorithms while maintaining efficiency, potentially providing a viable method for learning with neuromorphic hardware.

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bioRxiv2023

Inferring plasticity rules from single-neuron spiketrains using deep learning methods

Synaptic plasticity is a core basis for learning and adaptation. Determining how synapses are altered by local signals - the learning rules - is the hinge about which brain activity pivots. A large number of in vitro characterizations have focused on restricted sets of core properties, but it remains to be established which if any of the known learning rules is most consistent with changes in activity patterns in behaving animals. To address this question, we hypothesize that the correlation between features of the activity of a single post-synaptic neuron and subsequent changes of the representations could be used to detect the underlying learning rule. Because this correlation is expected to be diluted in the notoriously large variability of brain activity, we test here learning rule inference based on passive observations of single neurons using deep artificial neural networks. Using simulated data, we found that both transformers, temporal convolutional networks, and SVM could classify learning rules far above the chance level, with transformers achieving the best overall accuracy. This performance can be achieved despite the presence of noise and representational drift. We further investigated the features used by the algorithms to perform the classification and found the deep net used inner temporal differences of distinct learning rules to separate learning trajectories. We also find, however, that the classification accuracy is sensitive to alterations in network properties. Our work illustrates that distinct learning rules generate distinguishable trajectories of responses, but warns against using simulation-trained classifiers to infer learning rules from real data.

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bioRxiv2023

Serotonin predictively encodes value

The in vivo responses of dorsal raphe nucleus (DRN) serotonin neurons to emotionally-salient stimuli are a puzzle. Existing theories centred on reward, surprise, or uncertainty individually account for some aspects of serotonergic activity but not others. Here we find a unifying perspective in a biologically-constrained predictive code for cumulative future reward, a quantity called state value in reinforcement learning. Through simulations of trace conditioning experiments common in the serotonin literature, we show that our theory, called value prediction, intuitively explains phasic activation by both rewards and punishments, preference for surprising rewards but absence of a corresponding preference for punishments, and contextual modulation of tonic firing--observations that currently form the basis of many and varied serotonergic theories. Next, we re-analyzed data from a recent experiment and found serotonin neurons with activity patterns that are a surprisingly close match: our theory predicts the marginal effect of reward history on population activity with a precision <<0.1 Hz neuron-1. Finally, we directly compared against quantitative formulations of existing ideas and found that our theory best explains both within-trial activity dynamics and trial-to-trial modulations, offering performance usually several times better than the closest alternative. Overall, our results show that previous models are not wrong, but incomplete, and that reward, surprise, salience, and uncertainty are simply different faces of a predictively-encoded value signal. By unifying previous theories, our work represents an important step towards understanding the potentially heterogeneous computational roles of serotonin in learning, behaviour, and beyond.

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bioRxiv2023

Burstprop for Learning in Spiking Neuromorphic Hardware

The need for energy-efficient solutions in Deep Neural Network (DNN) applications has led to a growing interest in Spiking Neural Networks (SNNs) implemented in neuromorphic hardware. The Burstprop algorithm enables online and local learning in hier-archical networks, and therefore can potentially be implemented in neuromorphic hardware. This work presents an adaptation of the algorithm for training hierarchical SNNs on MNIST. Our implementation requires an order of magnitude fewer neurons than the previous ones. While Burstprop outper-forms Spike-timing dependent plasticity (STDP), it falls short compared to training with backpropagation through time (BPTT). This work establishes a foundation for further improvements in the Burst-prop algorithm, developing such algorithms is essential for achieving energy-efficient machine learning in neuromorphic hardware.

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bioRxiv2023

Cellular Substrate of Eligibility Traces

The ability of synapses to undergo associative, activity-dependent weight changes constitutes a linchpin of current cellular models of learning and memory. It is, however, unclear whether canonical forms of Hebbian plasticity, which inherently detect correlations of cellular events occurring over short time scales, can solve the temporal credit assignment problem proper to learning driven by delayed behavioral outcomes. Recent evidence supports the existence of synaptic eligibility traces, a time decaying process that renders synapses momentarily eligible for a weight update by a delayed instructive signal. While eligibility traces offer a means of retrospective credit assignment, their material nature is unknown. Here, we combined whole-cell recordings with two-photon uncaging, calcium imaging and biophysical modeling to address this question. We observed and parameterized a form of behavioral timescale synaptic plasticity (BTSP) in layer 5 pyramidal neurons of mice prefrontal areas wherein the pairing of temporally separated pre- and postsynaptic events (0.5 s - 1 s), irrespective of order, induced synaptic potentiation. By imaging calcium in apical oblique dendrites, we reveal a short-term and associative plasticity of calcium dynamics (STAPCD) whose time-dependence mirrored the induction rules of BTSP. We identified a core set of molecular players that were essential for both STAPCD and BTSP and that, together with computational simulations, support a model wherein the dynamics of intracellular handling of calcium by the endoplasmic reticulum (ER) provides a latent memory trace of neural activity that instantiates synaptic weight updates upon a delayed instructive signal. By satisfying the requirements expected of eligibility traces, this mechanism accounts for how individual neurons can conjunctively bind cellular events that are separated by behaviorally relevant temporal delays, and thus offers a cellular model of reinforced learning.

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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.

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bioRxiv2022

Dendritic excitability primarily controls overdispersion

The brain is an intricate assembly of intercommunicating neurons whose input-output function is only partially understood. The role of active dendrites in shaping spiking responses, in particular, is unclear. Although existing models account for active dendrites and spiking responses, they are too complex to analyze analytically and demand long stochastic simulations. Here we combined cable and renewal theory to describe how input fluctuations shape the response of neuronal ensembles with active dendrites. We found that dendritic input readily and potently controls interspike interval dispersion. This phenomenon can be understood by considering that neurons display three fundamental operating regimes: one mean-driven regime and two fluctuation-driven regimes. We show that these results are expected to appear for a wide range of dendritic properties and verify the predictions of the model in experimental data. These findings have implications for the role of interspike interval dispersion in learning and for theories of attractor states.

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bioRxiv2022

A ternary neural code resolves error and sharpening signals

Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental reports of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code in vivo while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Bursting modulation was potent and its time course evolved, even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.

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bioRxiv2021

Cell-Type Specific Responses to Associative Learning in the Primary Motor Cortex

The primary motor cortex (M1) is known to be a critical site for movement initiation and motor learning. Surprisingly, it has also been shown to possess reward-related activity, presumably to facilitate reward-based learning of new movements. However, whether reward-related signals are represented among different cell types in M1, and whether their response properties change after cue-reward conditioning remains unclear. Here, we performed longitudinal in vivo two-photon Ca2+ imaging to monitor the activity of different neuronal cell types in M1 while mice engaged in a classical conditioning task. Our results demonstrate that most of the major neuronal cell types in M1 showed robust but differential responses to both cue and reward stimuli, and their response properties undergo cell-type specific modifications after associative learning. PV-INs responses became more reliable to the cue stimulus, while VIP-INs responses became more reliable to the reward stimulus. PNs only showed robust response to the novel reward stimulus, and they habituated to it after associative learning. Lastly, SOM-IN responses emerged and became more reliable to both conditioned cue and reward stimuli after conditioning. These observations suggest that cue- and reward-related signals are represented among different neuronal cell types in M1, and the distinct modifications they undergo during associative learning could be essential in triggering different aspects of local circuit reorganization in M1 during reward-based motor skill learning.

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bioRxiv2021

Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically-identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire (GIF) models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.

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bioRxiv2021

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts. Author summaryThe language of the brain consists of sequences of action potentials. These sequences often contain bursts, short "words" consisting of a few action potentials in rapid succession. Bursts appear to play a special role in the brain. They indicate whether a stimulus was perceived or missed, and they are very effective drivers of synaptic plasticity, the neural substrate of learning. Bursts occur rarely, but consistently, a condition that is thought to maximize the amount of information they can communicate. In our article, we argue that this condition is far from self-evident, but requires very special circumstances. We show that these circumstances can be reached by homeostatic inhibitory plasticity in certain inhibitory neuron types. This may sound complicated, but basically works just like a thermostat. When bursts occur too often, inhibition goes up and suppresses them. When they are too rare, inhibition goes down and thereby increases their number. In computer simulations, we show that this simple mechanism can create circumstances akin to those in the brain, and indeed allows bursts to convey information effectively. Whether this mechanism is indeed used by the brain now remains to be tested by our experimental colleagues.

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