5 research outputs found
Modelling and Formal Verification of Neuronal Archetypes Coupling
International audienceIn the literature, neuronal networks are often represented as graphs where each node symbolizes a neuron and each arc stands for a synaptic connection. Some specific neuronal graphs have biologically relevant structures and behaviors and we call them archetypes. Six of them have already been characterized and validated using formal methods. In this work, we tackle the next logical step and proceed to the study of the properties of their couplings. For this purpose, we rely on Leaky Integrate and Fire neuron modeling and we use the synchronous programming language Lustre to implement the neuronal archetypes and to formalize their expected properties. Then, we exploit an associated model checker called kind2 to automatically validate these behaviors. We show that, when the archetypes are coupled, either these behaviors are slightly modulated or they give way to a brand new behavior. We can also observe that different archetype couplings can give rise to strictly identical behaviors. Our results show that time coding modeling is more suited than rate coding modeling for this kind of studies
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Exploring Models in the Biology Classroom
Models are simplified representations of more complex systems that help scientists structure the knowledge they acquire. As such, they are ubiquitous and invaluable in scientific research and communication. Because science education strives to make classroom activities more closely reflect science in practice, models have become integral teaching and learning tools woven throughout the Next Generation Science Standards (NGSS). Although model-based learning and curriculum are not novel in educational theory, only recently has modeling taken center stage in K–12 national standards for science, technology, engineering, and mathematics (STEM) classes. We present a variety of examples to outline the importance of various types of models and the practice of modeling in biological research, as well as the emphasis of NGSS on their use in both classroom learning and assessment. We then suggest best practices for creating and modifying models in the context of student-driven inquiry and demonstrate that even subtle incorporation of modeling into existing science curricula can help achieve student learning outcomes, particularly for English-language learners. In closing, we express the value of models and modeling in life beyond the classroom and research laboratory, and highlight the critical importance of “model literacy” for the next generation of scientists, engineers, and problem-solvers