The brain is in a state of perpetual reverberant neural activity, even the absence specific tasks or stimuli. Shedding light on origin and functional significance such dynamical essential to understanding how transmits, processes, stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics empirically observed neuronal activity can be understood by assuming networks operate regime with features, including emergence scale invariance,...

Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training method. Likewise, successful implementation neuro-inspired plasticity rules into RC artificial networks has boosted performance original models. In this manuscript, we analyze role play on changes lead to better RC. To end, implement synaptic non-synaptic paradigmatic example model: Echo State Network. Testing...

It has been recently discovered that the measles virus can wipe out adaptive immune system, destroying B lymphocytes and reducing diversity of non-specific cells infected host. In particular, this implies previously acquired immunization from vaccination or direct exposition to other pathogens could be erased in a phenomenon named "immune amnesia", whose effects become particularly worrisome given actual rise anti-vaccination movements. Here we present first attempt incorporate amnesia into...

Shedding light on how biological systems represent, process and store information in noisy environments is a key challenging goal. A stimulating, though controversial, hypothesis poses that operating dynamical regimes near the edge of phase transition, i.e., at criticality or “edge chaos”, can provide information-processing living with important operational advantages, creating, e.g., an optimal trade-off between robustness flexibility. Here, we elaborate recent theoretical result, which...

Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly support hypothesis that neural dynamics operate near edge instability. However, these related analyses often fail to capture intricate temporal structure brain activity, as they primarily rely on time-integrated measurements neurons. Here, we present a novel framework designed explore signatures criticality diverse...

Advancing our knowledge of how the brain processes information remains a key challenge in neuroscience. This thesis combines three different approaches to study dynamics neural networks and their encoding representations: computational approach, that builds upon basic biological features neurons construct effective models can simulate structure dynamics; machine-learning which draws parallel with functional capabilities networks, allowing us infer dynamical properties required solve certain...

Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near edge instability across regions in brain. However, these analyses, as well related studies, often fail to capture intricate temporal structure brain activity they primarily rely on time-integrated measurements neurons. In this study, we present a novel framework designed explore signatures...

The brain encodes external stimuli through patterns of neural activity, forming internal representations the world. Recent experiments show that for a given stimulus change over time. However, mechanistic origin observed "representational drift" (RD) remains unclear. Here, we propose biologically-realistic computational model piriform cortex to study RD in mammalian olfactory system by combining two mechanisms dynamics synaptic weights at separate timescales: spontaneous fluctuations on...

Abstract The brain is in a state of perpetual reverberant neural activity, even the absence specific tasks or stimuli. Shedding light on origin and functional significance such dynamical essential to understanding how transmits, processes, stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics empirically observed neuronal activity can be understood by assuming networks operate regime near edge phase transition. Moreover, resulting...

Shedding light onto how biological systems represent, process and store information in noisy environments is a key challenging goal. A stimulating, though controversial, hypothesis poses that operating dynamical regimes near the edge of phase transition, i.e. at criticality or "edge chaos", can provide information-processing living with important operational advantages, creating, e.g., an optimal trade-off between robustness flexibility. Here, we elaborate on recent theoretical result, which...

Deciphering the underpinnings of dynamical processes leading to information transmission, processing, and storing in brain is a crucial challenge neuroscience. An inspiring but speculative theoretical idea that such dynamics should operate at brink phase transition, i.e., edge between different collective phases, entail rich repertoire optimize functional capabilities. In recent years, research guided by advent high-throughput data new developments has contributed making quantitative...

The brain is in a state of perpetual reverberant neural activity, even the absence specific tasks or stimuli. Shedding light on origin and functional significance such dynamical essential to understanding how transmits, processes, stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics empirically observed neuronal activity can be understood by assuming networks operate regime near edge phase transition. Moreover, resulting critical...