Comparing representations and computations in single neurons versus neural networks |
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| Institution: | 1. Department of Psychology, National University of Singapore, Singapore 117570, Singapore;2. The N1 Institute for Health, National University of Singapore, Singapore, Singapore;1. Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain;2. Department of Psychology and Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, United States;3. Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;4. The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;5. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;6. Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain;7. Instituci’o Catalana de la Recerca i Estudis Avan,ats (ICREA), Passeig Llu’s Companys 23, Barcelona, 08010, Spain;8. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany;9. School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia;10. Starlab Barcelona, Av. Tibidabo 47b, 08035 Barcelona, Spain;11. Haskins Laboratories, 300 George Street, New Haven, CT, 06511, USA;1. School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK;1. Department of Psychology, Western University, London, ON N6A 5B7, Canada;2. Department of Psychology, Durham University, Durham DH1 3LE, UK;1. School of Psychological Science, University of Bristol, Bristol, UK;2. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany;1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China;2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China;3. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;4. Chinese Institute for Brain Research, Beijing, China;1. Biological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany |
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| Abstract: | Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function. |
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