Path Catalog
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Path Catalog
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This learning path guides students through the fundamental principles of neuronal function, starting with the ionic basis of resting membrane potential, progressing through action potential generation and propagation, and culminating in synaptic transmission. It emphasizes the biophysical concepts and experimental evidence that underpin our understanding of neuronal excitability and communication.
This learning path introduces the molecular mechanisms underlying neural function, starting with the central dogma of molecular biology and progressing through gene expression, receptors, ion channels, and signaling molecules. It is designed for university students in neurobiology and molecular biology who want a systematic understanding of how molecular processes enable neuronal activity.
This learning path provides a systematic introduction to the structure of the human nervous system, covering the brain, spinal cord, peripheral nerves, and protective layers. It is designed for university-level neurobiology and medical students who need a clear, foundational understanding of neuroanatomy.
This learning path introduces the cellular foundations of neurobiology, starting with core cell biology concepts and building up to the specialized structures and functions of neurons and glia. It emphasizes the molecular and structural organization that underlies neuronal signaling and support.
This learning path introduces high school students to the fundamental principles of neurobiology. It begins with foundational cell biology and progresses through the structure and function of neurons, the mechanisms of neural signaling, and the organization of the nervous system, culminating in an integrated understanding of how neural circuits produce behavior.
This learning path guides graduate students through the essential knowledge and skills required to produce a thesis in computational neuroscience. It covers foundational neuroscience and computational methods, research design, modeling, analysis, and thesis writing, culminating in the production of an original thesis.
This advanced learning path equips university students with the interdisciplinary skills needed for a career in computational neuroscience. It builds from core neuroscience, mathematics, and programming through modeling and data analysis to an independent research project, emphasizing scientific communication.
A comprehensive learning path for computational neuroscience graduate students to understand and apply open science practices, covering open data, open code, reproducibility, and sharing within the context of neuroscience research.
This path equips graduate learners with the knowledge and skills to navigate and contribute to the global computational neuroscience landscape. It covers foundational neuroscience and computational methods, advanced modeling and data analysis, and the professional skills needed for international collaboration and career development.
A beginner-friendly journey through the key figures, models, and technological advances that shaped computational neuroscience. Starting from early neuron doctrine and electrophysiology, this path traces the evolution from conceptual models to modern large-scale brain simulations.