Biology subjects

Deneux, T.

Publications and source records attributed to Deneux, T..

5 recordsLinked to original sources

bioRxiv2025

Adolescent social profiles predict adult social behaviour, monoamine brain content and regional gene expression

The study of inter-individual variability in animal behaviors during cognitive processes is crucial to better grasp the emergence of personality traits in healthy animals. Social experiences occurring at adolescence can affect the expression of the behavioural variability as it is a critical period of brain and cognitive plasticity. Here, we asked how individual variability of social profiles is shaped at adolescence and whether the profiles are associated with regional brain metabolites and gene expressions in the adult brain. At adolescence we determined a dominant profile expressing more dominance, a submissive profile expressing more submission and an explorer profile showing more novelty exploration than social interaction. Adolescent social behaviors are conserved at adulthood. Our data suggest that dominance tendencies already present at adolescence are likely to stabilize throughout life, thus shaping future social behaviors. Our results show that social profiles are not related to sex and life environment. However, being more dominant, interactive and socially motivated shape dopaminergic olfactory activity at adolescence. Also, showing the explorer or submissive profiles is associated with an opposite serotonin pattern in the cerebellum, but a similar kynurenine pattern in the prefrontal cortex and cerebellum, thus pointing toward major neurotransmitter systems in key regions for brain health. This work opens new perspectives on the importance of studying social profiles early in life and the individual neurobiological associated features that could potentially predict individual vulnerability. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/684125v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@bc26b7org.highwire.dtl.DTLVardef@91dba2org.highwire.dtl.DTLVardef@1ea7755org.highwire.dtl.DTLVardef@4083f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

Population coding and network dynamics during OFF responses in auditory cortex

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

neuroscience↗

bioRxiv2018

Cortical recruitment determines learning dynamics and strategy

Salience is a broad and widely used concept in neuroscience whose neuronal correlates, however, remain elusive. In behavioral conditioning, salience is used to explain various effects, such as stimulus overshadowing, and refers to how fast and strongly a stimulus can be associated with a conditioned event. Here, we show that sounds of diverse quality, but equal intensity and perceptual detectability, can recruit different levels of population activity in mouse auditory cortex. When using these sounds as cues in a Go/NoGo discrimination task, the degree of cortical recruitment matches the salience parameter of a reinforcement learning model used to analyze learning speed. We test an essential prediction of this model by training mice to discriminate light-sculpted optogenetic activity patterns in auditory cortex, and verify that cortical recruitment causally determines association or overshadowing of the stimulus components. This demonstrates that cortical recruitment underlies major aspects of stimulus salience during reinforcement learning.

neuroscience↗

bioRxiv2018

Context-dependent signaling of coincident auditory and visual events in primary visual cortex

Detecting rapid coincident changes across sensory modalities is essential to recognize sudden threats and events. Using two-photon calcium imaging in identified cell types in awake mice, we show that auditory cortex (AC) neurons projecting to primary visual cortex (V1) preferentially encode the abrupt onsets of sounds. In V1, a sub-population of layer 1 interneurons gates this selective cross-modal information by a suppression specific to the absence of visual inputs. However, when large auditory onsets coincide with visual stimuli, visual responses are strongly boosted in V1. Thus, a dynamic asymmetric circuit across AC and V1 specifically identifies visual events starting simultaneously to sudden sounds, potentially catalyzing localization of new sound sources in the visual field.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗