CTN: Adam Cohen
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: 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…
CTN: Jonathan Pillow
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: 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…
CTN: Monday Lab Kim Stachenfeld
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: 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…
ARNI Biological Learning Working Group
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…
CTN: Hidenori Tanaka
Zuckerman Institute- Kavli Auditorium 9th Fl 3227 Broadway, NYHidenori Tanaka Title and Abstract: TBD
CTN: Eva Naumann
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle and Abstract: TBD
CTN Monda Lab: Liam Paninski
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle and Abstract: TBD
ARNI Continual Learning Working Group Spring Opening Meeting
CEPSR 620 Schapiro 530 W. 120th StFrom: 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.…
ARNI WG Multi-resource-cost optimization of neural network models: Paul Schrater
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: 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 control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically,…
ARNI Biological Learning Working Group
VirtualKen 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
ARNI Continual Learning Project
CEPSR 620 Schapiro 530 W. 120th StFollowup to discussion in Meeting 1 Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior)
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesDescription: Advances in neurotechnology and behavioral tracking have enabled the collection of large-scale neural and behavioral datasets, offering new opportunities to study brain function in complex settings. However, researchers face significant challenges in integrating and analyzing data across different brain regions, individuals, and behavioral contexts. Inspired by recent successes in large-scale “foundation” models in natural language…