Biology subjects

Nassar, M.

Publications and source records attributed to Nassar, M..

11 recordsLinked to original sources

bioRxiv2026

A normative account of human temporal structure learning

People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers temporal structure of the environment directly from observations and makes accurate predictions across qualitatively different environments. We tested predictions of our model in an online behavioral study in which participants predicted outcomes generated according to various temporal structures, and demonstrated that people learned to exploit these structures in a qualitatively similar manner to that of our structure learning model. Furthermore, we show that normative structure learning accounts for puzzling asymmetries previously observed in sequential effects of temporal structures on human learning. Taken together our model and empirical data provide the first general account of how people use temporal structures to interpret unexpected observations in the service of learning.

neuroscience↗

bioRxiv2024

Dissociable neural signals for reward and emotion prediction errors

Reinforcement learning models focus on reward prediction errors (PEs) as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective PEs, play a crucial role in shaping behavior. Whether there is neural separability between emotion and reward signals remains unknown. We employ electroencephalography during social learning to investigate the neural signatures of reward and affective PEs. Behavioral results reveal that while affective PEs predict choices when little is known about how a partner will behave, reward PEs become more predictive overtime as uncertainty about a partners behavior diminishes. This functional dissociation is mirrored neurally by engagement of distinct event-related potentials. The FRN indexes reward PEs while the P3b tracks affective PEs. Only the P3b predicts subsequent choices, highlighting the mechanistic influence of affective PEs during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is distinguishable--behaviorally and neurally--from reward. SignificanceFor nearly a century, scientists have asked how humans learn about their worlds. Learning models borrowed from computer science--namely, reinforcement learning--provide an elegant and simple framework that showcases how reward prediction errors are used to update ones knowledge about the environment. However, a fundamental question persists: what exactly is reward? This gap in knowledge is problematic, especially when we consider the multiplicity of social contexts where external rewards must be contextualized to gain value and meaning. We leverage electroencephalography to interrogate the role of emotion prediction errors--violations of emotional expectations--during learning. We observe distinct neural signals for reward and emotion prediction errors, suggesting that emotions may act as a bridge between external rewards and subjective value.

neuroscience↗

bioRxiv2023

Expansion of novel biosynthetic gene clusters from diverse environments using SanntiS

Natural products biosynthesised by microbes are an important component of the pharmacopeia with a vast array of biomedical and industrial applications, in addition to their key role in mediating many ecological interactions. One approach for the discovery of these metabolites is the identification of biosynthetic gene clusters (BGCs), genomic units which encode the molecular machinery required for producing the natural product. Genome mining has revolutionised the discovery of BGCs, yet metagenomic assemblies represent a largely untapped source of natural products. The imbalanced distribution of BGC classes in existing databases restricts the generalisation of detection patterns and limits the ability of mining methods to recognise a broader spectrum of BGCs. This problem is further intensified in metagenomic datasets, where BGC genes may be incomplete. This work presents SanntiS, a new machine learning-based tool for identifying BGCs. SanntiS achieved high precision and recall in both genomic and metagenomic datasets, effectively capturing a broad range of BGCs. Application of SanntiS to MGnify metagenomic assemblies led to a resource containing 1.9 million BGC predictions with associated contextual data from diverse biomes and demonstrates a significant fraction of novelty compared to equivalent isolate genomes datasets. Subsequent experimental validation of a novel antimicrobial peptide detected solely by SanntiS, further demonstrates the potential of this approach for uncovering novel bioactive compounds.

bioinformatics↗

bioRxiv2023

Integration of thalamic and retrosplenial inputs in presubicular layer 3 gates cross-laminar bursting signal to upstream head direction circuit

Head-direction (HD) signals function as the brains internal compass. They are organized as an attractor, and anchor to the environment via visual landmarks. Here we examine how thalamic HD signals and visual information from the retrosplenial cortex combine in the presubiculum. We find that monosynaptic excitatory connections from anterior thalamic nucleus and from retrosplenial cortex converge on single layer 3 pyramidal neurons in the dorsal portion of mouse presubiculum. Independent dual wavelength photostimulation of these inputs in slices leads to action potential generation preferentially for near-coincident inputs, indicating that layer 3 neurons can transmit a visually matched HD signal to medial entorhinal cortex. Layer 4 neurons, which innervate the lateral mammillary nucleus, form a second step in the association of HD and landmark signals. They receive little direct input from thalamic and retrosplenial axons. We show that layer 4 cells are excited di-synaptically, transforming regular spiking activity into bursts of action potentials, and that their firing is enhanced by cholinergic agonists. Thus, a coherent sense of orientation involves projection specific translaminar processing in the presubiculum, where neuromodulation facilitates landmark updating of HD signals in the lateral mammillary nucleus.

neuroscience↗

bioRxiv2022

Close kin dyads indicate intergenerational dispersal and barriers

The movement of individuals through continuous space is typically constrained by dispersal ability and dispersal barriers. A range of approaches have been developed to investigate these. KO_SCPLOWINDISPERSEC_SCPLOW is a new approach that infers intergenerational dispersal ({sigma}) from close kin dyads, and appears particularly useful for investigating taxa that are difficult to observe individually. This study, focusing on the mosquito Aedes aegypti, shows how the same close kin data can also be used for barrier detection. We empirically demonstrate this new extension of the method using genome-wide sequence data from 266 Ae. aegypti. First, we use the spatial distribution of full-sib dyads collected within one generation to infer past movements of ovipositing female mosquitoes. These dyads indicated the relative barrier strengths of two roads, and performed favourably against alternative genetic methods for detecting barriers. The difference in variance between the sib and first cousin spatial distributions was used to infer movement over the past two generations, providing estimates of intergenerational dispersal ({sigma} = 81.5-197.1 m.gen-1/2) and density ({rho} = 833-4864 km-2). Dispersal estimates showed general agreement with those from mark-release-recapture studies. Barriers, {sigma}, {rho}, and neighbourhood size (331-526) can inform forthcoming releases of dengue-suppressing Wolbachia bacteria into this mosquito population.

genetics↗

bioRxiv2020

Oligodendrocyte secreted factors shape hippocampal GABAergic neuron transcriptome and physiology

Oligodendrocytes form myelin for central nervous system axons and release factors which signal to neurons during myelination. Here, we ask how oligodendroglial factors influence hippocampal GABAergic neuron physiology. In mixed hippocampal cultures GABAergic neurons fired action potentials of short duration and received high frequencies of excitatory synaptic events. In purified neuronal cultures without glial cells, GABAergic neuron excitability increased and the frequency of synaptic events decreased. These effects were largely reversed by adding oligodendrocyte conditioned medium. We compared the transcriptomic signature with the electrophysiological phenotype of single neurons in these three culture conditions. Genes expressed by single pyramidal or GABAergic neurons largely conformed to expected cell-type specific patterns. Multiple genes of GABAergic neurons were significantly downregulated by the transition from mixed cultures containing glial cells to purified neuronal cultures. Levels of these genes were restored by the addition of oligodendrocyte conditioned medium to purified cultures. Clustering genes with similar changes in expression between different culture conditions revealed processes affected by oligodendroglial factors. Enriched genes are linked to roles in synapse assembly, action potential generation and transmembrane ion transport, including of zinc. These results provide new insight into the molecular targets by which oligodendrocytes influence neuron excitability and synaptic function.

neuroscience↗

bioRxiv2020

Latent motives guide structure learning during adaptive social choice

Predicting the behavior of others is an essential part of human cognition that enables strategic social behavior (e.g., cooperation), and is impaired in multiple clinical populations. Despite its ubiquity, social prediction poses a generalization problem that remains poorly understood: We can neither assume that others will simply repeat their past behavior in new settings, nor that their future actions are entirely unrelated to the past. Here we demonstrate that humans solve this challenge using a structure learning mechanism that uncovers other people’s latent, unobservable motives, such as greed and risk aversion. In three studies, participants were tasked with predicting the decisions of another player in multiple unique economic games such as the Prisoner’s Dilemma. Participants achieved accurate social prediction by learning the hidden motivational structure underlying the player’s actions to cooperate or defect (e.g., that greed led to defecting in some cases but cooperation in others). This motive-based abstraction enabled participants to attend to information diagnostic of the player’s next move and disregard irrelevant contextual cues. Moreover, participants who successfully learned another’s motives were more strategic in a subsequent competitive interaction with that player, reflecting that accurate social structure learning can lead to more optimal social behaviors. These findings demonstrate that advantageous social behavior hinges on parsimonious and generalizable mental models that leverage others’ latent intentions.Significance statement A hallmark of human cognition is being able to predict the behavior of others. How do we achieve social prediction given that we routinely encounter others in a dizzying array of social situations? We find people achieve accurate social prediction by inferring another’s hidden motives—motives that do not necessarily have a one-to-one correspondence with observable behaviors. Participants were able to infer another’s motives using a structure learning mechanism that enabled generalization. Individuals used what they learned about others in one setting to predict their actions in an entirely new setting. This cognitive process can explain a wealth of social behaviors, ranging from strategic economic decisions to stereotyping and racial bias.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗

bioRxiv2019

The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases.

The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named \"Equine Virtual Farm\" (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving.

neuroscience↗

bioRxiv2019

Statistical context dictates the relationship between feedback-related EEG signals and learning

Successful decision-making requires learning expectations based on experienced outcomes. This learning should be calibrated according to the surprise associated with an outcome, but also to the statistical context dictating the most likely source of surprise. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily, demanding increased learning. In contrast, when signal corruption is expected to occur occasionally, surprising outcomes can suggest a corrupt signal that should be ignored by learning systems. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the updating P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but predicted less learning in a context where surprise was indicative of a one-off outlier (oddball). The conditional predictive relationship between the P300 and learning behavior was persistent even after adjusting for known sources of learning rate variability. Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

neuroscience↗

bioRxiv2018

Anterior thalamic excitation and feed-forward inhibition of presubicular neurons projecting to medial entorhinal cortex

The presubiculum contains head direction cells that are crucial for spatial navigation. Here, we examined the connectivity and strengths of thalamic inputs to presubicular layer 3 neurons projecting to the medial entorhinal cortex in the mouse. We recorded pairs of projection neurons and interneurons while optogenetically stimulating afferent fibers from the anterior thalamic nuclei (ATN). Thalamic input differentially affects presubicular neurons: layer 3 pyramidal neurons and fast-spiking parvalbumin expressing (PV) interneurons are directly and monosynaptically activated, with depressing dynamics, while somatostatin (SST) expressing interneurons are indirectly excited, during repetitive ATN activity. This arrangement ensures that the thalamic excitation of layer 3 cells is often followed by disynaptic inhibition. Feed-forward inhibition is largely mediated by PV interneurons which have a high probability of connection to presubicular pyramidal cells. Our data point to a specific role of presubicular microcircuits in shaping thalamic head-direction signals transmitted to medial entorhinal cortex: Short-latency PV cell activation may enforce temporally precise head direction tuning during fast turns. However, depression at ATN-PV synapses during repeated activation tends to facilitate pyramidal cell firing when head direction is maintained. Operations performed in presubicular layer 3 circuits seem well-adapted for spatial fine-tuning of head direction signals sent to the medial entorhinal cortex.\n\nSIGNIFICANCE STATEMENTHow microcircuits participate in shaping neural inputs is crucial to understanding information processing in the brain. Here, we show how the presubiculum may process thalamic head directional information before transmitting it to the medial entorhinal cortex. Synaptic inputs from the anterior thalamic nuclei (ATN) excite layer 3 pyramidal cells (PC) and parvalbumin (PV) interneurons, which mediate disynaptic feed-forward inhibition. Somatostatin (SST) interneurons are excited indirectly. Presubicular circuits may switch between two regimes according to the angular velocity of head movements. During immobility, SST-PC interactions support maintained head directional firing with attractor-like dynamics. During rapid head turns, in contrast, PV mediated feed-forward inhibition acts to tune the head direction signal transmitted to medial entorhinal cortex.

neuroscience↗

bioRxiv2017

How activity dependent feedback inhibition may maintain head direction signals in mouse presubiculum

Orientation in space is represented in specialized brain circuits. Persistent head direction signals are transmitted from anterior thalamus to the presubiculum, but the identity of the presubicular target neurons, their connectivity and function in local microcircuits are unknown. Here we examine how thalamic afferents recruit presubicular principal neurons and Martinotti interneurons and the ensuing synaptic interactions between these cells. Pyramidal neuron activation of Martinotti cells in superficial layers is strongly facilitating such that high frequency head directional stimulation efficiently unmutes synaptic excitation. Martinotti cell feedback plays a dual role: precisely timed spikes may not inhibit the firing of in-tune head direction cells, while exerting lateral inhibition. Autonomous attractor dynamics emerge from a modeled network implementing wiring motifs and timing sensitive synaptic interactions in the pyramidal - Martinotti cell feedback loop. This inhibitory microcircuit is therefore tuned to refine and maintain head direction information in the presubiculum.

neuroscience↗