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…

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…

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…

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…

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. These models often take the form of short computer programs, and constructing them typically requires a great deal of human effort and ingenuity. In this…

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…

Hidenori Tanaka Title and Abstract: TBD

Title and Abstract: TBD

Title and Abstract: TBD

From: Tom Zollo In Y2, the aim is to use this working group as a launchpad for a larger ARNI continual learning project (which we hope to spawn multiple subprojects and papers).  We hope for this group to tackle issues that are relevant to both modern practitioners and the ARNI mission of connecting artificial and natural intelligence.…

Title: Control when confidence is costly Abstract: We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient…

Ken Miller will be talking about E/I networks & balanced networks and some computational/functional implications, there’s two papers I’d suggest reading:on balanced amplification: https://www.sciencedirect.com/science/article/pii/S0896627309001287 review of loosely and tightly balanced networks: https://www.sciencedirect.com/science/article/pii/S0896627321005754.  Meeting Link: meet.google.com/nnq-csiy-yah