Biology subjects

Gutkin, B.

Publications and source records attributed to Gutkin, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Control of Male Mouse Copulatory Behavior by Midbrain Dopamine Neurons

Ejaculation in male mice and humans requires intravaginal thrusting movements following penile intromission, yet the underlying neural mechanisms remain poorly understood. In this study, we used fiber photometry to record real-time activity of dopamine (DA) neurons in the ventral tegmental area (VTA) of male mice during copulation across six sexual interactions, in order to investigate how these dynamics change with experience and to examine their functional significance. Contrary to expectations, phasic VTA DA activity remained stable across sessions, showing time-locked responses to mounts, penile intromission, individual thrusts, and ejaculation, indicating experience-independent neural representations of all copulatory events. Notably, thrusting-related activity sharply declined just before ejaculation. Optogenetic inhibition of DA neurons disrupted thrusting behavior, but did not block ejaculation, suggesting DA signaling is critical for sustaining copulatory actions rather than triggering ejaculation itself. While prevailing theories frame VTA DA activity as primarily experience-dependent or centered on reward prediction, we here highlight its role in behavioral persistence during copulation. More broadly, our findings extend understanding of DA function beyond classical reward paradigms to the regulation of innate complex, goal-directed behaviors.

neuroscience↗

bioRxiv2025

Reward impulsivity is linked to addictive-like behaviors towards sweet food and sugary drinks but not fatty food

Overconsumption of hyperpalatable food is a worldwide problem. Although many previous studies have shown the link between monetary delay discounting and propensity to overconsume food, the studies did not focus on the link between delay discounting and various subcategories of palatable food. In the present study, we investigate the link between the individual propensity to choose smaller immediate rather than larger delayed reward and the degree of problematic eating behavior for five types of palatable food (sweets, sugary drinks, fatty food, salty snacks, and starchy food). The delay discounting task was used to measure propensity to choose immediate rather than delayed rewards, while the degree of addictive-like behavior was measured using the modified Yale food addiction scale (mYFAS) for each food category. Both model-free and model-based analysis showed that there is a significant correlation between the addiction score for sweet food and the tendency to choose smaller immediate rather than larger delayed rewards. A model-based analysis showed the same significant correlation for sugary drinks. However, no such correlation was observed for other food categories (fatty, salty, or starchy food), suggesting that reward impulsivity may play different roles in addictive-like behavior towards various food categories.

neuroscience↗

bioRxiv2024

Compensating functional connectivity changesdue to structural connectivity damage viamodifications of local dynamics

Neurological pathologies as e.g. Alzheimers Disease or Multiple Sclerosis are often associated to neurodegenerative processes affecting the strength and the transmission speed of long-range inter-regional fiber tracts. Such degradation of Structural Connectivity impacts on large-scale brain dynamics and the associated Functional Connectivity, eventually perturbing network computations and cognitive performance. Functional Connectivity however is not bound to merely mirror Structural Connectivity, but rather reflects the complex coordinated dynamics of many regions. Here, using analytical characterizations of toy models and computational simulations connectome-base whole-brain models, we predict that suitable modulations of regional dynamics could precisely compensate for the effects of structural degradation, as if the original Structural Connectivity strengths and speeds of conduction were effectively restored. The required dynamical changes are widespread and aspecific (i.e. they do not need to be restricted to specific regions) so that they could be potentially implemented via neuromodulation or pharmacological therapy, globally shifting regional excitability and/or excitation/inhibition balance. Computational modelling and theory thus suggest that, in the future therapeutic interventions could be designed to "repair brain dynamics" rather than structure to boost functional connectivity without having to block or revert neurodegenerative processes. AUTHOR SUMMARYNeurological disorders affect Structural Connectivity, i.e. the wiring infrastructure interlinking distributed brain regions. Here we propose that the resulting disruptions in Functional Connectivity, i.e. inter-regional coordination and information sharing, could be compensated by modifying local dynamics so to effectively emulate the restoration of Structural Connectivity (but through a suitable "software patch" rather than by repairing the "hardware"). For simple toy models involving a few regions we can achieve an analytical understanding of how structural and dynamical changes jointly control Functional Connectivity. We then show that the concept of "effective connectome change" via modulation of dynamics robustly extend also to simulation of large-scale models embedding realistic whole-brain connectivity. We thus forecast that novel therapeutic strategies could be devised, targeting dynamics rather than neurodegenerative mechanisms.

neuroscience↗

bioRxiv2024

Deciphering the Rhythmic Symphony of Speech: A Neural Framework for Robust and Time-Invariant Speech Comprehension

Unraveling the mysteries of how humans effortlessly grasp speech despite diverse environmental challenges has long intrigued researchers in systems and cognitive neuroscience. This study explores the neural intricacies underpinning robust speech comprehension, giving computational mechanistic proof for the hypothesis proposing a pivotal role for rhythmic, predictive top-down contextualization facilitated by the delta rhythm in achieving time-invariant speech processing. Our Brain-Rhythm-based Inference model, BRyBI, integrates three key rhythmic processes - theta-gamma interactions for parsing phoneme sequences, dynamic delta rhythm for inferred prosodic-phrase context, and resilient speech representations. Demonstrating mechanistic proof-of-principle, BRyBI replicates human behavioral experiments, showcasing its ability to handle pitch variations, time-warped speech, interruptions, and silences in non-comprehensible contexts. Intriguingly, the model aligns with human experiments, revealing optimal silence time scales in the theta- and delta-frequency ranges. Comparative analysis with deep neural network language models highlights distinctive performance patterns, emphasizing the unique capabilities of a rhythmic framework. In essence, our study sheds light on the neural underpinnings of speech processing, emphasizing the role of rhythmic brain mechanisms in structured temporal signal processing - an insight that challenges prevailing artificial intelligence paradigms and hints at potential advancements in compact and robust computing architectures.

neuroscience↗

bioRxiv2023

Demonstration that sublinear dendrites enable linearly non-separable computations

Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), which does not require inhibition contrary to the XOR. Notably, sublinear dendritic operations enable LNSCs when scattered synaptic activation generates increased somatic spike output. However, experimental demonstrations of scatter-sensitive neuronal computations have not yet been described. Using glutamate uncaging onto cerebellar molecular layer interneurons, we show that scattered synaptic-like activation of dendrites evoked larger compound EPSPs than clustered synaptic activation, generating a higher output spiking probability. Moreover, we also demonstrate that single interneurons can indeed implement the FBP. We use a biophysical model to predict under what conditions a neuron can implement the FBP and what leads to failures. Experimental results agree with the model-determined conditions and hence validate our protocol as a solid benchmark for a neuron to implement linearly non-separable computations. Since sublinear synaptic summation is a property of passive dendrites we expect that many different neuron types can implement LNSCs.

neuroscience↗

bioRxiv2023

Phase-dependent stimulation response is shaped by the brain's transient collective dynamics.

Exogenous stimulation is a promising tool for investigating and altering cognitive processes in the brain, with potential clinical applications. Following experimental observations, we hypothesise that the effect of stimulation crucially depends on the endogenous dynamics of the brain. Our study explores how local and global dynamical properties, like the stimulation phase of regional oscillatory activity and the transient network states, modulate the effect of single pulse stimulation in a large-scale network. Our findings demonstrate that the effect of stimulation strongly depends on the interplay between stimulated phase, transient network state, and brain region. Importantly, we show that stimulation is not only state-dependent but can also induce global state switching. Lastly, predicting the effect of stimulation by using machine learning shows that state-aware measures can increase the performance by up to 40%. Our results suggest that a fine characterisation of the complex brain dynamics in experimental setups is essential for improving the reliability of exogenous stimulation.

neuroscience↗

bioRxiv2023

Homeostatic Reinforcement Theory Accounts for Sodium Appetitive State- and Taste- Dependent Dopamine Responding.

Seeking and consuming nutrients is essential to survival and maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, where complex nutrient seeking behaviors are triggered by physiological need, recent theoretical work proposed that such strategies are a result of reinforcement learning processes. This theory also proposed that phasic dopamine (DA) signals play a key role in signaling potentially need-fulfilling outcomes. To examine potential links between homeostatic and reinforcement learning processes, we focus on sodium appetite as sodium depletion triggers state and taste dependent changes in behavior and DA signaling evoked by sodium-related stimuli. We find that both the behavior and the dynamics of DA signaling underlying sodium appetite can be accounted for by extending principles of homeostatic regulation into a reinforcement learning framework (HRRL). We first optimized HRRL-based agents to model sodium-seeking behavior measured in rats. Agents successfully reproduced the state and the taste dependence of behavioral responding for sodium as well as for lithium and potassium salts. We then show that these same agents can account for the regulation of DA signals evoked by sodium tastants in a taste and state dependent manner. Our models quantitatively describe how DA signals evoked by sodium decrease with satiety and increase with deprivation suggesting that phasic DA signals and sodium consumption are down regulated prior to animals reaching satiety. Lastly, our HRRL agents also account for the behavioral and neurophysiological observations that suggest mice cannot distinguish between sodium and lithium containing salts. Our HRRL agents exhibited an equal preference for sodium versus lithium containing solutions, and underestimated the nutritional value of sodium when lithium was concurrently available. We propose that animals use orosensory signals as predictors of the internal impact of the consumed good and our results pose clear targets for future experiments. In sum, this work suggests that appetite-dirven behavior may be driven by reinforcement learning mechanisms that are dynamically tuned by homeostatic need.

neuroscience↗

bioRxiv2022

Doing what's not wanted: Conflict in incentives and misallocation of behavioural control lead to drug-seeking despite adverse outcomes

Despite being aware of negative consequences and wanting to quit, long-term addicts find it difficult to quit seeking and consuming drugs. This inconsistency between the (often compulsive) behavioural patterns and the explicit knowledge of negative consequences represents a cognitive conflict which is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. The focus of this work is to illustrate the mechanisms that lead to a cognitive conflict and its impact on actions taken i.e. addictive choices. We propose an algorithmic model that captures how the action choices that the agent makes when reinforced with drug-rewards become incongruent with the presence of negative consequences that often follow those choices. We advance the understanding of having a decision hierarchy in representing "cognitive control" and how lack of such control at higher-level in the hierarchy could potentially lead to consolidated drug-seeking habits. We further propose a cost-benefit based arbitration scheme, which mediates the allocation of control across different levels of the decision-making hierarchy. Lastly, we discuss how our work on extending a computational model to an algorithmic one, could in turn also helps us improve the understanding of how drugs hijack the dopamine-spiralling circuit at an implementation level.

neuroscience↗

bioRxiv2022

The influence of learning on robustness of working memory to distractors

Inhibition of irrelevant information is a crucial process within the working memory (WM) system. In the present study, we explored how inhibition of distractors is learned within a Sternberg-like WM task and how this learning process affects readout performance. We introduced distractors of varying strength as well as novel stimuli to a classic Sternberg task. Our findings show that the length of the training period affected obstructiveness of distractors as well as differences between WM loads. Significant differences between conditions observed early on in accuracy became void after general ceiling performance was reached. Moreover, accuracy and reaction times, which are commonly used as readouts of WM performance, were affected differently by training. While both improved significantly, only accuracy exhibited differential dependency of experimental conditions on training. It is essential to consider these dynamics when introducing a WM task which requires a long training period.

neuroscience↗

bioRxiv2022

Simulating semantic dementia in a brain-constrained model of action and object words learning

The nature of semantic knowledge - conceptual information stored in the brain - is highly debated in the field of cognitive science. Experimental and clinical data specify various cortical regions involved in the processing of meaning. Those include semantic hubs that take part in semantic processing in general as well as sensorimotor areas that process specific conceptual categories according to their modality. Biologically inspired neurocomputational models can help adjudicate between different theories about the exact roles of those regions in the functioning of the semantic system. Here, we used an existing neuroanatomically constrained model of frontotemporal brain areas implicated in language acquisition and grounding. We adapted it to replicate and explain the effects of semantic dementia on word processing abilities. Semantic dementia is a disease characterized by semantic knowledge deterioration that correlates with neural damage in the anterior temporal lobe. The behavior of our model is in accordance with clinical data - namely, word recognition performance decreases as SD lesions progress, whereas word repetition abilities remain preserved, or are less affected. Furthermore, our model makes novel predictions about category-specific effects of SD - namely, our simulation results indicate that word processing should be more impaired for object-than for action-related words, and that white matter degradation should lead to more severe consequences than the same proportion of grey matter degradation. The present results provide a mechanistic, cortical-level explanatory account of a range of language impairments as observed in clinical populations during the onset and progress of semantic dementia.

neuroscience↗

bioRxiv2021

Neural Activity Classification with Machine Learning Models Trained on Interspike Interval Time-Series Data

Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animals behavioral state prediction and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning based models for neural decoding. We release the code allowing to reproduce the reported results. Author summaryMachine learning-based neural decoding has been shown to outperform the traditional approaches like Wiener and Kalman filters on certain key tasks [1]. To further the advancement of neural decoding models, such as improvements in deep neural network architectures and better feature engineering for classical ML models, there need to exist common evaluation benchmarks similar to the ones in the fields of computer vision or natural language processing. In this work, we propose a benchmark consisting of several individual neuron spike train classification tasks based on open-access data from a range of animals and brain regions. We demonstrate that it is possible to achieve meaningful results in such a challenging benchmark using the massive time-series feature extraction approach, which is found to perform similarly to state-of-the-art deep learning approaches.

neuroscience↗

bioRxiv2020

Frequency-Selective Oscillatory Control of Working Memory Robustness to Distractors

Working memory (WM) is the brains ability to retain information that is not directly available from the sensory systems. WM retention is accompanied by sustained firing rate modulation and changes of the large-scale oscillatory profile. Among other changes, beta-band activity elevates in task-related regions, presumably stabilizing WM retention. Alpha-band activity, in turn, is stronger in task-irrelevant regions, serving to protect WM trace from distracting information. Although a large body of experimental evidence links neural oscillations to WM functions, theoretical understanding of their interrelations is still incomplete. In this study, we used a computational approach to explore a potential role of beta and alpha oscillations in control of WM stability. First, we examined a single bistable module that served as a discrete object representation and was resonant in the beta-band in the active state. We demonstrated that beta-band input produced differentially stronger excitatory effect on the module in the active state compared to the background state, while this difference decreased with the input frequency. We then considered a system of two competing modules, selective for a stimulus and for a distractor, respectively. We simulated a task, in which a stimulus was loaded into the first module, then an identical oscillatory input to both modules was turned on, after which a distractor was presented to the second module. We showed that beta-band input prevented loading of high-amplitude distractors and erasure of the stimulus from WM. On the contrary, alpha-band input promoted loading of low-amplitude distractors and the stimulus erasure. In summary, we demonstrated that stability of WM trace could be controlled by global oscillatory input in a frequency-dependent manner via controlling the level of competition between stimulus-encoding and distractor-encoding circuits. Such control is possible due to difference in the resonant and non-linear properties between the background and the active states.

neuroscience↗

bioRxiv2020

Disinhibitory and neuromodulatory regulation of hippocampal synaptic plasticity

Hippocampal synaptic plasticity, particularly in the Schaffer collateral (SC) to CA1 pyramidal excitatory transmission, is considered as the cellular mechanism underlying learning. The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of the excitatory synaptic plasticity remains largely understudied. Our recent experiments showed that repeated cholinergic activation of 7 nACh receptors expressed in oriens-lacunosum-moleculare (OLM2) interneurons could induce LTP in SC-CA1 synapses, likely through disinhibition by inhibiting stratum radiatum (s.r.) interneurons that provide feedforward inhibition onto CA1 pyramidal neurons, revealing a potential mechanism for local interneurons to regulate SC-CA1 synaptic plasticity. Here, we pair in vitro studies with biophysically-based modeling to uncover the mechanisms through which cholinergic-activated GABAergic interneurons can disinhibit CA1 pyramidal cells, and how repeated disinhibition modulates hippocampal plasticity at the excitatory synapses. We found that 7 nAChR activation increases OLM activity. OLM neurons, in turn inhibit the fast-spiking interneurons that provide feedforward inhibition onto CA1 pyramidal neurons. This disinhibition, paired with tightly timed SC stimulation, can induce potentiation at the excitatory synapses of CA1 pyramidal neurons. Our work further describes the pairing of disinhibition with SC stimulation as a general mechanism for the induction of hippocampal synaptic plasticity. Disinhibition of the excitatory synapses, paired with SC stimulation, leads to increased NMDAR activation and intracellular calcium concentration sufficient to upregulate AMPAR permeability and potentiate the synapse. Repeated paired disinhibition of the excitatory synapse leads to larger and longer lasting increases of the AMPAR permeability. Our study thus provides a novel mechanism for inhibitory interneurons to directly modify glutamatergic synaptic plasticity. In particular, we show how cholinergic action on OLM interneurons can down-regulate the GABAergic signaling onto CA1 pyramidal cells, and how this shapes local plasticity rules. We identify paired disinhibition with SC stimulation as a general mechanism for the induction of hippocampal synaptic plasticity.

neuroscience↗

bioRxiv2020

Adaptive learning under strategic and structural uncertainty: the case of auction games

In games of incomplete information individual players make decisions facing a combination of structural uncertainty about the underlying parameters of the environment, and strategic uncertainty about the actions undertaken by their partners. How well are human actors able to cope with these uncertainties, and what models best describe their learning in such environments? We use a double auction task with different competitive and informational environments to characterize learning abilities of the single human participants (buyers) in a range of adaptive learning models covering reinforcement learning, directional learning and belief learning. Results show that real behaviour is best described using simple models of directional learning type with minimal knowledge assumptions about information efficiency of prices. This behavior is consistent with bounded rationality and risk aversion: human subjects try to maximize their chance for transaction, and do so using the simplest learning rule.

animal behavior and cognition↗

bioRxiv2019

Efficient and robust coding in heterogeneous recurrent networks

Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, type 1 and type 2 neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that type 2 neurons are more coherent with the overall network activity than type 1 neurons.

neuroscience↗

bioRxiv2018

Action in auctions: neural and computational mechanisms of bidding behavior

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behavior plays a major role in social competition. Yet how humans learn to bid efficiently remains an open question. We used model-based neuroimaging to investigate the neural mechanisms of bidding behavior under different types of competition. Twenty-seven subjects (nine male) played a prototypical bidding game: a double action, with three \"market\" types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is \"nudged\" depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behavior best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action-space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

neuroscience↗

bioRxiv2018

Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.\n\nSignificance statementEpileptic seizures are commonly thought to arise from a pathology of inhibition in the brain circuits. Theoretical models aiming to explain epileptic oscillations usually describe the neural activity solely in terms of inhibition and excitation. Single neuron adaptation properties are usually assumed to have only a limited contribution to seizure dynamics. To explore this issue, we developed a novel neural mass model with adaption in the excitatory population. By including adaptation and intrinsic excitability together with inhibition in this model, we were able to account for several experimentally observed properties of seizures, resting state dynamics, and pre-ictal oscillations, leading to improved understanding of epileptic seizures.

neuroscience↗

bioRxiv2018

Nicotinic modulation of hierarchal inhibitory control over prefrontal cortex resting state dynamics: modeling of genetic modification and schizophrenia-related pathology

Nicotinic acetylcholine receptors (nAChRs) strongly modulate the cholinergic drive to a hierarchy of inhibitory neurons in the superficial layers of the PFC, critical to cognitive processes. Genetic deletion of various types of nAChRs, located on specific interneurons, impacts the properties of ultra-slow transitions between high and low activity states (H-states and L-states, respectively), recorded in mice during quiet wakefulness. In addition, recent data indicate that a genetic mutation of the 5 nAChR subunit located on vasoactive intestinal polypeptide (VIP) inhibitory neurons, the rs16969968 single nucleotide polymorphism (5 SNP), appears to be responsible for \"hypofrontality\" observed in schizophrenia. Chronic nicotine application to 5 SNP mice restores neural activity to control levels. Using firing rate models of hierarchically organized neural populations, we showed that the change of activity patterns recorded in the genetically modified mice can be explained by a change of activity state stability, differentially modulated by cholinergic inputs to parvalbumin (PV), somatostatin (SOM) or VIP inhibitory populations. A change in amplitude, but not duration of H-states fully account for the lowered pyramidal (PYR) firing rates recorded in 5 SNP mice. We demonstrate that desensitization and upregulation of {beta}2 nAChRs located on SOM interneurons, but not activation of 5 nAChRs located on VIP interneurons, by chronic nicotine application could account for activity normalization recorded in 5 SNP mice. The model implies that subsequent nicotine withdrawal should lead to PYR activity depression more severe than the original hypofrontality caused by SNP mutation.

systems biology↗