Biology subjects

Maes, A.

Publications and source records attributed to Maes, A..

4 recordsLinked to original sources

bioRxiv2021

Long- and short-term history effects in a spiking network model of statistical learning

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Existing spiking network models implementing sampling lack the ability to learn the statistical structure from observed stimuli and instead often hard-code a dynamics. Here, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

neuroscience↗

bioRxiv2021

Stress-induced collective behavior leads to the formation of multicellular structures and the survival of the unicellular alga Chlamydomonas

Depending on their nature, living organisms use various strategies to adapt to environmental stress conditions. Multicellular organisms implement a set of reactions involving signaling and cooperation between different types of cells. Unicellular organisms on the other hand must activate defense systems, which involve collective behaviors between individual organisms. In the unicellular model alga Chlamydomonas reinhardtii, the existence and the function of collective behavior mechanisms in response to stress remain largely unknown. Here we report the discovery of a mechanism of abiotic stress response that Chlamydomonas can trigger to form large multicellular structures that can comprise several thousand cells. We show that these aggregates constitute an effective bulwark within which the cells are efficiently protected from the toxic environment. We have generated the first family of mutants that aggregate spontaneously, the socializer mutants (saz), of which we describe here in detail saz1. We took advantage of the saz mutants to implement a large scale multiomics approach that allowed us to show that aggregation is not the result of passive agglutination, but rather genetic reprogramming and substantial modification of the secretome. The reverse genetic analysis we conducted on some of the most promising candidates allowed us to identify the first positive and negative regulators of aggregation and to make hypotheses on how this process is controlled in Chlamydomonas.

plant biology↗

bioRxiv2020

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

neuroscience↗

bioRxiv2019

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.\n\nAuthor summaryThe brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.

neuroscience↗