Instability to a heterogeneous oscillatory state in randomly connected recurrent networks with delayed interactions

Oscillatory dynamics are ubiquitous in biological networks. Possible sources of oscillations are well understood in low-dimensional systems, but have not been fully explored in high-dimensional networks. Here we study large networks consisting of randomly coupled rate units. We identify a novel type of bifurcation in which a continuous part of the eigenvalue spectrum of the linear stability matrix crosses the instability line at non-zero-frequency. This bifurcation occurs when the interactions are delayed and partially anti-symmetric, and leads to a heterogeneous oscillatory state in which oscillations are apparent in the activity of individual units, but not on the population-average level

Behavioral origin of sound-evoked activity in mouse visual cortex

Sensory cortices can be affected by stimuli of multiple modalities and are thus increasingly thought to be multisensory. For instance, primary visual cortex (V1) is influenced not only by images but also by sounds. Here we show that the activity evoked by sounds in V1, measured with Neuropixels probes, is stereotyped across neurons and even across mice. It is independent of projections from auditory cortex and resembles activity evoked in the hippocampal formation, which receives little direct auditory input. Its low-dimensional nature starkly contrasts the high-dimensional code that V1 uses to represent images. Furthermore, this sound-evoked activity can be precisely predicted by small body movements that are elicited by each sound and are stereotyped across trials and mice. Thus, neural activity that is apparently multisensory may simply arise from low-dimensional signals associated with internal state and behavior

Accéder à l'encodage des sons dans le cortex auditif à l'aide de la technique d'imagerie UltraSonore fonctionnelle

The world teems with complex sounds that animals have to interpret in order to survive. To do so, their brain must represent the richness of the sounds' acoustic structure, from simple to high-order features. Understanding how it does it, however, remains filled with challenges. In this thesis, these questions were explored through a new technical prism, namely functional UltraSound imaging (fUSi). First, fUSi was used to investigate with a high fidelity the topographical organization of the auditory system, as well as its connectivity with other brain areas. Second, it provided fundamental clues for our understanding of how natural sounds are encoded in the auditory cortex, and hints at the human particularities for speech processing. Last, it gave us access to non-continuous topographical encoding, with the example of spatial localization. Through these three aspects, we exposed the different spatially organized modules of processing that overlap within a single brain area.Le monde extérieur regorge de sons complexes, que chaque animal doit interpréter afin de survivre. Pour ce faire, leur cerveau se doit de représenter toute la richesse de la structure acoustique de ces sons, jusque dans leurs propriétés les plus complexes. Dans cette thèse, cette question est explorée à travers un nouveau prisme, l'imagerie fonctionnelle ultrasonore (fUS). Dans un premier temps, l'imagerie fUS est utilisée pour étudier avec une haute fidélité l'organisation topographique du système auditif, ainsi que ses connexions avec d'autres aires cérébrales. Dans un deuxième temps, elle permet d'explorer des aspects fondamentaux de la façon dont le cortex auditif encode les sons naturels, ainsi que les spécificités humaines pour le traitement du langage. Enfin, elle révèle des formes topographiques mais non continues d'encodage, avec l'exemple de la localisation spatiale des sons. À travers ces trois aspects sont révélés les différents modules de traitement de l'information auditive, spatialement organisés, qui se superposent au sein d'une aire cérébrale unique

Cerebellar learning using perturbations

The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm’s convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia

Cerebellar learning using perturbations

International audienceThe cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm's convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia