Events
Calendar of Events
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Title: Neuromorphic Computing and the Significance of Medium Dependence Abstract: The increasingly prohibitive cost of energy demanded by large artificial neural networks (ANNs) is giving new impetus to research and development on neuromorphic computing. Importantly, there is an open question over how brain-like the hardware will have to be in order for an artificial intelligence… |
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Title: Building foundation models for neuroscience Abstract: Current methodologies for recording brain activity often provide narrow views of the brain's function. This fragmentation of datasets has hampered the development of robust and comprehensive computational models that generalize across diverse conditions, tasks, and individuals. Our work is motivated by the need for a large-scale foundation model… |
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Title: Mapping bioelectrical signals, from dendrites to circuits Abstract: Neuronal dendrites are excitable, but what are these excitations for? Are dendritic excitations involved in integration? Or in mediating back-propagation? What are their footprints, and what patterns of spiking and synaptic inputs can activate them? We mapped bioelectrical signals throughout dendritic arbors of pyramidal cells in behaving… |
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Title: Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems Abstract: Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types, which limits their ability to capture the functional roles of… |
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1 event,Title: Discovering Symbolic Cognitive Models from Human and Animal Behavior with CogFunSearch Abstract: A key goal of cognitive science is to discover mathematical models that describe how the brain implements cognitive processes.… |
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Title: Brain-like learning with exponentiated gradients and Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units Meeting Summary: Our focus will be on answering the following question, which may be a focus for the next few meetings: To what degree are different learning algorithms entangled with a particular neural architecture? Can… |
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