{"id":null,"code":"TBMC5009","name":{"valueFi":"AI in Neuroscience","valueEn":"AI in Neuroscience","valueSv":"AI in Neuroscience"},"credits":1.0,"minCredits":1,"maxCredits":1,"tags":[],"createdAt":1790534785540,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"The students will learn the basics of AI applications which are used in neuroscience. They will learn basic techniques of how to apply AI in their example neuroscience data.","valueEn":"The students will learn the basics of AI applications which are used in neuroscience. They will learn basic techniques of how to apply AI in their example neuroscience data.","valueSv":"The students will learn the basics of AI applications which are used in neuroscience. They will learn basic techniques of how to apply AI in their example neuroscience data."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course introduces students to concepts of AI in neuroscience in:\r\n- Decode brain activity: AI models analyze complex data from EEG, fMRI, and other neuroimaging techniques to understand how the brain functions.\r\n- Diagnose neurological disorders: Machine learning helps detect patterns linked to conditions like Alzheimer's, epilepsy, and Parkinson’s disease\r\n- Enhance brain-computer interfaces (BCIs): AI enables real-time decoding of neural signals, allowing people to control devices with their thoughts\r\n- Model biological intelligence: Neuroscience-inspired AI tries to replicate how the brain learns, adapts, and processes information.\r\n- Cross-Pollination: Neuroscience Inspires AI (and vice versa)\r\nThese applications often require interpretable, biologically plausible models—not just high accuracy, but insights into how the brain works.\r\n\r\nCovers:\r\n- Deep learning models (CNNs)\r\n- Explainable AI, mechanistic interpretability","valueEn":"The course introduces students to concepts of AI in neuroscience in:\r\n- Decode brain activity: AI models analyze complex data from EEG, fMRI, and other neuroimaging techniques to understand how the brain functions.\r\n- Diagnose neurological disorders: Machine learning helps detect patterns linked to conditions like Alzheimer's, epilepsy, and Parkinson’s disease\r\n- Enhance brain-computer interfaces (BCIs): AI enables real-time decoding of neural signals, allowing people to control devices with their thoughts\r\n- Model biological intelligence: Neuroscience-inspired AI tries to replicate how the brain learns, adapts, and processes information.\r\n- Cross-Pollination: Neuroscience Inspires AI (and vice versa)\r\nThese applications often require interpretable, biologically plausible models—not just high accuracy, but insights into how the brain works.\r\n\r\nCovers:\r\n- Deep learning models (CNNs)\r\n- Explainable AI, mechanistic interpretability","valueSv":"The course introduces students to concepts of AI in neuroscience in:\r\n- Decode brain activity: AI models analyze complex data from EEG, fMRI, and other neuroimaging techniques to understand how the brain functions.\r\n- Diagnose neurological disorders: Machine learning helps detect patterns linked to conditions like Alzheimer's, epilepsy, and Parkinson’s disease\r\n- Enhance brain-computer interfaces (BCIs): AI enables real-time decoding of neural signals, allowing people to control devices with their thoughts\r\n- Model biological intelligence: Neuroscience-inspired AI tries to replicate how the brain learns, adapts, and processes information.\r\n- Cross-Pollination: Neuroscience Inspires AI (and vice versa)\r\nThese applications often require interpretable, biologically plausible models—not just high accuracy, but insights into how the brain works.\r\n\r\nCovers:\r\n- Deep learning models (CNNs)\r\n- Explainable AI, mechanistic interpretability"}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Lectures (in-person), independent work.","valueEn":"Lectures (in-person), independent work.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures (6 h) Independent project work (21 h)","valueEn":"Lectures (6 h) Independent project work (21 h)","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Onciul, R., Tataru, C.I., Dumitru, A.V., Crivoi, C., Serban, M., Covache-Busuioc, R.A., Radoi, M.P. and Toader, C., 2025. Artificial intelligence and neuroscience: Transformative synergies in brain research and clinical applications. Journal of Clinical Medicine, 14(2), p.550.\r\nWang, R. and Chen, Z.S., 2025. Large-scale foundation models and generative AI for BigData neuroscience. Neuroscience Research, 215, pp.3-14.\r\nGoodwin, N.L., Nilsson, S.R., Choong, J.J. and Golden, S.A., 2022. Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Current opinion in neurobiology, 73, p.102544.\r\nhttps://realpython.com/python-ai-neural-network/\r\nhttps://github.com/shap/shap","valueEn":"Onciul, R., Tataru, C.I., Dumitru, A.V., Crivoi, C., Serban, M., Covache-Busuioc, R.A., Radoi, M.P. and Toader, C., 2025. Artificial intelligence and neuroscience: Transformative synergies in brain research and clinical applications. Journal of Clinical Medicine, 14(2), p.550.\r\nWang, R. and Chen, Z.S., 2025. Large-scale foundation models and generative AI for BigData neuroscience. Neuroscience Research, 215, pp.3-14.\r\nGoodwin, N.L., Nilsson, S.R., Choong, J.J. and Golden, S.A., 2022. Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Current opinion in neurobiology, 73, p.102544.\r\nhttps://realpython.com/python-ai-neural-network/\r\nhttps://github.com/shap/shap","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"TKO_7107 Introduction to Programming, or otherwise Intermediate Python skills (NumPy,\r\nSciPy, Matplotlib, PyTorch)\r\nTKO_7093 Statistical Data Analysis","valueEn":"TKO_7107 Introduction to Programming, or otherwise Intermediate Python skills (NumPy,\r\nSciPy, Matplotlib, PyTorch)\r\nTKO_7093 Statistical Data Analysis","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"The grade is based on the scores of the independent work.","valueEn":"The grade is based on the scores of the independent work.","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Master's Degree Programme in Human Neuroscience","valueEn":"MDP in Human Neuroscience","valueSv":"MDP in Human Neuroscience"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Harri Merisaari","valueEn":"Harri Merisaari","valueSv":"Harri Merisaari"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}