Biology subjects

Series, P.

Publications and source records attributed to Series, P..

13 recordsLinked to original sources

bioRxiv2025

Learning, sleep replay and consolidation of contextual fear memories: A neural network model

Contextual fear conditioning is an experimental framework widely used to investigate how aversive experiences affect the valence an animal associates with an environment. While the initial formation of associative context-fear memories is well studied - dependent on plasticity in hippocampus and amygdala - the neural mechanisms underlying their subsequent consolidation remain less understood. Recent evidence suggests that the recall of contextual fear memories shifts from hippocampal-amygdalar to amygdalo-cortical networks as they age. This transition is thought to rely on sleep. In particular, neural replay during hippocampal sharp-wave ripple events seems crucial, though open questions regarding the involved neural interactions remain. Here, we propose a biologically informed neural network model of context-fear learning. It expands the scope of previous models through the addition of a sleep phase. Hippocampal representations of context, formed during wakefulness, are replayed in conjunction with cortical and amygdalar activity patterns to establish long-term encodings of learned fear associations. Additionally, valence-coding synapses within the amygdala undergo overnight adjustments consistent with the synaptic homeostasis hypothesis of sleep. The model reproduces experimentally observed phenomena, including context-dependent fear renewal and time-dependent increases in fear generalisation. Few neural network models have addressed fear memory consolidation and to our knowledge, ours is the first to incorporate a neural mechanism enabling it. Our framework yields testable predictions about how disruptions in synaptic homeostasis may lead to pathological fear sensitization and generalisation, thus potentially bridging computational models of fear learning and mechanisms underlying anxiety symptoms in disorders such as PTSD. Author SummaryHow do we learn to fear certain environments? Why do some fear memories fade while others persist or even grow stronger over time? Scientists have long used laboratory experiments to study how animals associate danger with a particular context. These studies have helped identify brain regions involved in fear learning, including the amygdala, hippocampus, and cortex, and have inspired many computational models of how fear is acquired in the brain. However, most models focus only on what happens when fear is first learned, overlooking how these memories evolve in the days that follow and the role of sleep in this process. In this work, we present a neural network model that captures how fear memories are strengthened or reshaped during sleep. It builds on earlier models by incorporating memory replay and synaptic homeostasis, two brain processes believed to support emotional memory consolidation. Our model identifies neural processes that help make fear memories persistent, suggests that sleep is necessary to maintain adaptive behaviour after threatening experiences, and proposes that sleep disruptions mediate the harmful impact of stress on emotional regulation. By extending amygdala-based models of fear learning to include post-learning dynamics, our work offers new insight into how emotional memories are stabilised.

neuroscience↗

bioRxiv2025

Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration

Multisensory integration is a fundamental neural mechanism crucial for understanding cognition. Multiple theoretical models exist to account for the computational processes underpinning this mechanism. However, there is an absence of a consolidated framework that facilitates the examination of multisensory integration across diverse experimental and computational contexts. We introduce Scikit-NeuroMSI, an accessible Python-based open-source framework designed to streamline the implementation and evaluation of computational models of multisensory integration. The capabilities of Scikit-NeuroMSI were demonstrated in enabling the implementation of multiple models of multisensory integration at different levels of analysis. Furthermore, we illustrate the utility of the software in systematically exploring the models behavior in spatiotemporal causal inference tasks through parameter sweeps in simulations. Particularly, we conducted a comparative analysis of Bayesian and network models of multisensory integration to identify commonalities that may enable to bridge both levels of description, addressing a key research question within the field. We discuss the significance of this approach in generating computationally informed hypotheses in multisensory research. Recommendations for the improvement of this software and directions for future research using this framework are presented.

neuroscience↗

bioRxiv2024

Reduced learning rate and E/I imbalance drive Peripersonal Space boundaries expansion in Schizophrenia

Abnormal encoding of peripersonal space (PPS) is believed to affect bodily self disruptions in schizophrenia (SCZ). Empirical studies show that SCZ patients exhibit a narrower PPS than controls but maintain its plasticity. Computational research links this smaller PPS to increased excitation of sensory neurons and reduced feedforward synaptic density. However, it is unclear how such differences influence learning during the expansion of PPS boundaries. We hypothesise that Hebbian plasticity can account for PPS expansion after active tool use training. To explore the effect of such mechanisms on PPS plasticity, we developed a SCZ network model which was fit to behavioural data before and after tool manipulation. We found that PPS expansion occurs in spite of E/I imbalance or reduced synaptic density, but does not match the post-training PPS representation of patients. A better fit was obtained after altering plasticity by either reducing the learning rate, increasing the forgetting rate or increasing the plasticity threshold. We discuss our findings in terms of dysfunctional plasticity in SCZ and highlight the key challenges in identifying the neurobiological correlates of reduced plasticity within PPS networks. Because current empirical data supports multiple viable mechanisms, we propose experiments to distinguish between the proposed plasticity accounts and clarify mixed findings on PPS representation in SCZ. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/604515v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@14ee71forg.highwire.dtl.DTLVardef@127c3aborg.highwire.dtl.DTLVardef@19f1338org.highwire.dtl.DTLVardef@a6e437_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIUsing a peripersonal space network model, we found that PPS expansion after tool-use occurs in spite of E/I imbalance or reduced synaptic density. C_LIO_LIReduced feedforward synaptic plasticity is required to match the post-training PPS representation of patients. C_LIO_LISuch reduction of synaptic plasticity could be achieved by either reducing the learning rate, increasing the forgetting rate or increasing the plasticity threshold relative to a healthy control model. C_LIO_LIOur model predicts that measuring PPS at intermediate time points during a longer stimulation protocol would help distinguish between these plasticity differences. C_LI

neuroscience↗

bioRxiv2024

Influence of truthful and misleading instructions on statistical learning across the autism spectrum

Bayesian studies of perception have documented how the brain learns the statistics of a new environment and uses them to interpret sensory information. Impairments in this process have been hypothesised to be central to autism spectrum disorders. However, very few such studies have differentiated between implicit and explicit learning. We manipulated the instructions given before a cue-stimulus association task to investigate their effects on statistical learning. The task was conducted online, in 335 participants with varying autistic traits. In the implicit condition, where no information was provided, participants acquired weak prior beliefs about the task regularities. Conversely, explicit information about the presence of regularities resulted in strong priors, correctly reflecting the tasks statistics, regardless of the informations veracity. Contrary to our hypothesis, autistic traits did not correlate with the influence of priors in any condition. Author SummaryPerception is greatly influenced by the brains prior knowledge of the environment, through a process called Bayesian inference. Recent theories of psychiatric disorders and particularly autism view them as impairments in this process. A crucial aspect of this process is how individuals form their knowledge of the environment. However, previous studies have not differentiated between learning that occurs when participants are aware of what they are learning and learning that happens implicitly. In the present study, we conducted an experiment with four conditions, each varying in terms of what participants were trying to learn and whether they were aware of the general form of the regularities. Our findings revealed that participants form much stronger beliefs about the regularities when they are informed about their presence. Additionally, we discovered that participants with strong autistic traits did not differ in their beliefs about the regularities in any of the conditions.

neuroscience↗

bioRxiv2024

Excitation/Inhibition imbalance impairs multisensory causal inference by increasing the proneness to experience the sound-induced flash illusion in the schizophrenia spectrum

The spectrum of schizophrenia is characterised by an altered sense of self with known impairments in tactile sensitivity, proprioception, body-self boundaries, and self-recognition. These are thought to be produced by failures in multisensory integration mechanisms, commonly observed as enlarged temporal binding windows during audiovisual illusion tasks. To our knowledge, there is an absence of computational explanations for multisensory integration deficits in patients with schizophrenia and individuals with high schizotypy, particularly at the neurobiological level. We implemented a multisensory causal inference network to reproduce the responses of individuals who scored low in schizotypy in a simulated double flash illusion task. Next, we explored the effects of recurrent excitation, cross-modal and feedback weights, and synaptic density on the visual illusory responses of the network. Using quantitative fitting to empirical data, we found that an increase in the weights of the recurrent excitatory connectivity in the network enlarges the temporal binding window and increases the overall proneness to experience the illusion, matching the responses of individuals scoring high in schizotypy. Moreover, we found that an increase in excitation increases the probability of inferring a common cause from the stimuli. We propose an E/I imbalance account of reduced temporal discrimination in the SCZ spectrum and discuss possible links with Bayesian theories of schizophrenia. We highlight the importance of adopting a multisensory causal inference perspective to address body-related symptomatology of schizophrenia.

neuroscience↗

bioRxiv2023

Action-outcome based flexible behavior requires medial prefrontal cortex lead and its enhanced functional connectivity with dorsomedial striatum

Cognitive flexibility plays a key role in ensuring an individuals survival, and its deficit is a key symptom in many mental conditions and neurodegenerative diseases. The prefrontal cortex and striatum are both essential to cognitive flexibility. However, how the prefrontal cortex and striatum communicate with each other to enable flexible decision-making is not well understood. Competing theories are raised, debating on which structure among these two leads the role in detecting and representing the new circumstances for a change, giving largely opposing predictions on neural activities in the prefrontal cortex and striatum during flexible behavior. To address this question, we trained head-restrained mice to perform an action-outcome based dynamic foraging task and simultaneously recorded single-neuron activities in the medial prefrontal cortex (mPFC) and dorsomedial striatum (DMS). In this task, the animal chooses one of two actions to obtain reward. The animal is guided only by previous reward outcomes. We report that mPFC but not DMS activity stores information about prior reward history. A large fraction of both mPFC and DMS neurons activity represents the difference in reward probability between two alternative options, namely the perceived reward probability difference (PRPD), a key decision variable that prescribes which subsequent choice to make. We find that mPFC neural activities track the change of PRPD earlier and faster than those in the DMS, and functional connectivity between mPFC and DMS increases with reducing overall reward proportion.

neuroscience↗

bioRxiv2023

Risk and loss aversion and attitude to COVID and vaccines in anxious individuals

Anxious individuals are known to show impaired decision-making in economic gambling task and in everyday life decisions. This impairment can be due to aversion to uncertainty about outcomes (risk aversion) and/or aversion to negative outcomes (loss aversion). We investigate how non-clinical individuals with high levels of Generalised Anxiety Disorder (GAD) (N = 54) behave compared to less anxious subjects (N = 61) in a gambling decision-making task delivered online and designed to separate the distinct influences of risk and loss aversion on decision-making. By modelling subjects choices using computational models derived from Prospect Theory and fitted using Hierarchical Bayesian methods, we estimate individual levels of risk and loss aversion. Moreover we analyse the reaction times and choice data using Hierarchical Drift Diffusion Models (HDDMs). We also link estimates of these parameters to individual propensity to risk averse behaviours during the COVID pandemic, like wearing safer types of face masks, or completing a COVID vaccination course. We report increased loss aversion in individuals with increased level of GAD compared to less anxious individuals and no differences in risk aversion. The HDDM analysis suggests that high anxiety subjects have a reduced bias towards gambling in trials where there is a risk to loose points, also consistent with them being loss averse. We also report no links between risk and loss aversion and attitudes towards COVID and vaccines. These results shed new light on the interplay of anxiety and risk and loss aversion and they can provide useful directions for clinical intervention.

pathology↗

bioRxiv2021

No increased circular inference in autism or autistic traits

Autism spectrum disorders have been proposed to arise from impairments in the probabilistic integration of prior knowledge with sensory inputs. Circular inference is one such possible impairment, in which excitation-to-inhibition imbalances in the cerebral cortex cause the reverberation and amplification of prior beliefs and sensory information. Recent empirical work has associated circular inference with the clinical dimensions of schizophrenia. Inhibition impairments have also been observed in autism, suggesting that signal reverberation might be present in that condition as well. In this study, we collected data from 21 participants with diagnosed autism spectrum disorders and 155 participants with a broad range of autistic traits in an online probabilistic decision-making task (the fisher task). We used previously established Bayesian models to investigate possible associations between autism or autistic traits and circular inference. No differences in prior or likelihood reverberation were found between autistic participants and those with no diagnosis. Similarly, there was no correlation between any of the circular inference model parameters and autistic traits across the whole sample. Furthermore, participants incorporated information from both priors and likelihoods in their decisions, with no relationship between their weights and psychiatric traits, contrary to what common theories for both autism and schizophrenia would suggest. These findings suggest that there is no increased signal reverberation in autism, despite the known presence of excitation-to-inhibition imbalances. They can be used to further contrast and refine the Bayesian theories of schizophrenia and autism, revealing a divergence in the computational mechanisms underlying the two conditions. Author SummaryPerception results from the combination of our sensory inputs with our brains previous knowledge of the environment. This is usually described as a process of Bayesian inference or predictive coding and is thought to underly a multitude of cognitive modalities. Impairments in this process are thought to explain various psychiatric disorders, in particular autism and schizophrenia, for which similar Bayesian theories have been proposed despite important differences in their symptoms. Recently, a new model of Bayesian impairment in schizophrenia has been proposed and validated using behavioural experiments, called the "circular inference" model. In the current study, we used the same task and computational modelling to explore whether circular inference could also account for autism spectrum disorder. We find that participants with autistic traits or diagnoses of autism do not present increased levels of circularity. This is the first study to investigate circular inference in autism, and one of the very few to explore possible autism and schizophrenia impairments with the same task and identical analytical methods. Our findings indicate one potential way in which the explanations of the two conditions might differ.

pathology↗

bioRxiv2020

Influence of E/I balance and pruning in peri-personal space differences in schizophrenia: a computational approach

The encoding of the space close to the body, named peri-personal space (PPS), is thought to play a crucial role in the unusual experiences of the self observed in schizophrenia (SCZ). However, it is unclear why SCZ patients and high schizotypal (H-SPQ) individuals present a narrower PPS and why the boundaries of the PPS are more sharply defined in patients. We hypothesise that the unusual PPS representation observed in SCZ is caused by an imbalance of excitation and inhibition (E/I) in recurrent synapses of unisensory neurons or an impairment of bottom-up and top-down connectivity between unisensory and multisensory neurons. These hypotheses were tested computationally by manipulating the effects of E/I imbalance, feedback weights and synaptic density in the network. Using simulations we explored the effects of such impairments in the PPS representation generated by the network and fitted the model to behavioural data. We found that increased excitation of sensory neurons could account for the smaller PPS observed in SCZ and H-SPQ, whereas a decrease of synaptic density caused the sharp definition of the PPS observed in SCZ. We propose a novel conceptual model of PPS representation in the SCZ spectrum that can account for alterations in self-world demarcation, failures in tactile discrimination and symptoms observed in patients.

neuroscience↗

bioRxiv2020

Inflexible Updating of the Self-Other Divide During a Social Context in Autism; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Autism spectrum disorder (ASD) is a heterogenous disorder predominantly characterized by social and communicative differences, but increasingly recognized to also alter (multi)sensory function. To face the heterogeneity and ubiquity of ASD, researchers have proposed models of statistical inference operating at the level of computations. Here, we attempt to bridge both across domains - from social to sensory - and levels of description - from behavioral computations to neural ensemble activity to a biologically-plausible artificial neural network - in furthering our understanding of autism. We do so by mapping visuo-tactile peri-personal space (PPS), and examining its electroencephalography (EEG) correlates, in individuals with ASD and neurotypical individuals during both a social and non-social context given that (i) the sensory coding of PPS is well understood, (ii) this space is thought to distinguish between self and other, and (iii) PPS is known to remap during social interactions. In contrast to their neurotypical counterparts, psychophysical and EEG evidence suggested that PPS does not remap in ASD during a social context. To account for this observation, we then employed a neural network model of PPS and demonstrate that PPS remapping may be driven by changes in neural gain operating at the level of multisensory neurons. Critically, under the anomalous excitation-inhibition (E/I) regime of ASD, this gain modulation does not result in PPS resizing. Overall, our findings are in line with recent statistical inference accounts suggesting diminished flexibility in ASD, and further these accounts by demonstrating within an example relevant for social cognition that such inflexibility may be due to E/I imbalances.

neuroscience↗

bioRxiv2018

Acquisition of visual priors and induced hallucinations in chronic schizophrenia

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To further test these theories, we here use a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics (Chalk et al., 2010). In this task, participants are presented with a field of coherently moving dots and need to report the presented direction of the dots (estimation task) and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: 1-motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); 2-in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n=20) differ from controls (n=23) in the acquisition of the perceptual priors and/or their influence on perception. We found that, although patients were slower than controls, they showed comparable acquisition of perceptual priors, correctly approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients relative wellbeing on antipsychotic medication. Intriguingly, however, patients made significantly fewer hallucinations of the most frequently presented directions than controls and fewer prior-based lapse estimations. This suggests that prior expectations had less influence on patients perception than on controls when stimuli were absent or below perceptual threshold.

neuroscience↗

bioRxiv2018

Neuroticism Impairs the Use of Reward Values for Decision-Making in Major Depression

Depression is a debilitating condition with a high prevalence, but aetiology and pathophysiology are still unclear. Various reward-learning paradigms have been used to show impairments in depression. Both trait pessimism and neuroticism are associated with depression, but their link with the impairments in reward learning and decision-making have not been investigated. A Pavlovian conditioning task was performed by 32 subjects, 15 with depression. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Neuroticism scores across participants were significantly correlated with participants inability to follow their internal value estimations. Our results demonstrate behavioural differences in probabilistic reward learning between depressed patients and healthy controls. Neuroticism was associated with the impaired ability to follow internal reward values and consequently with worse decision-making.

neuroscience↗

bioRxiv2017

Autistic traits, but not schizotypy, predict overweighting of sensory information in Bayesian visual integration

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due not to weaker prior expectations but to more precise sensory representations.

neuroscience↗