Neural network

…these interactions and their neural underpinnings requires experimental designs that minimally disrupt natural behaviors. In this context, fully automated experimental setups are highly advantageous. Bats, known for their complex vocal repertoire and learning capabilities, engage in rapid vocal exchanges, posing a challenge for real-time analysis and interaction. Our research aimed to develop an automated MATLAB® based tool that lev

With the number of dementia patients expected to double in Europe and triple globally by 2050, particularly affecting low- and middle-income nations, there is an urgent need for reliable, cost-effective screening methods to enable early interventions. This study investigates an EEG-based dementia screening tool, comparing it to conventional cognitive screening tests for early dementia detection. The in-house developed software automatically detects and removes EEG artifacts, and employs generalized linear models to extract spectral EEG features linked to cognitive decline. Clustering and cross

…in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle

…These methods simultaneously record neural activity at a wide range of depths in the brain, effectively allowing scientists to image a volume of brain tissue. However, this added capability comes with a trade-off: all observed signals from the volume are projected onto a 2D imaging plane. Therefore,

One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales to efficiently shape structured behaviors. I will present a model that clarifies how these complex computations could be performed in the mammalian brain, with an emphasis on the learning of an extendable library of autonomous motor motifs and the flexible stringing of these motifs in motor sequences. To build this model, we took advantage of the fact that the anatomy of the circuits involved is well known. Our results show how these architectural constraints lead to a prin

I will argue that the circuit architecture of the early olfactory system provides an adaptive, efficient mechanism for compressing the vast space of odor mixtures into the responses of a small number of sensors. In this view, the olfactory sensory repertoire employs a disordered code to compress a high dimensional olfactory space into a low dimensional receptor response space while preserving distance relations between odors. The resulting representation is dynamically adapted to efficiently encode the changing environment of volatile molecules. I will show that this adaptive combinatorial

…I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model

Homeostatic plasticity plays a key role in stabilizing neural network activity. But what is its role in neural information processing? We showed analytically how homeostasis changes collective dynamics and consequently information flow - depending on the input to the network. We then studied how input and homeostasis on a recurrent network of LIF neurons impacts information flow and task performance. We showed how we can tune the working point of the network, and found that, contrary to previous assumptions, there is not one optimal working point for a family of tasks, but each task may requir

In vivo, the human genome folds into a characteristic ensemble of 3D structures. The mechanism driving the folding process remains unknown. A theoretical model for chromatin (the minimal chromatin model) explains the folding of interphase chromosomes and generates chromosome conformations consistent with experimental data is presented. The energy landscape of the model was derived by using the maximum entropy principle and relies on two experimentally derived inputs: a classification of loci into chromatin types and a catalog of the positions of chromatin loops. This model was generalized by u

…a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experim