Topic: brain computation

Dense Associative Memories (Dense AMs) are energy-based neural networks that share many desirable features of celebrated Hopfield Networks but have superior information storage capabilities. In contrast to conventional Hopfield Networks, which were popular in the 1980s, DenseAMs have a very large memory storage capacity - possibly exponential in the size of the network. This aspect makes them appealing tools for many problems in AI and neurobiology. In this talk I will describe two theories of how DenseAMs might be built in biological “hardware”. According to the first theory, DenseAMs arise a

Understanding the circuit mechanisms of brain computation and behavior is one of the central questions in neuroscience. Recent advances in task-optimized recurrent neural networks (RNNs) have opened a new avenue to directly link neural dynamics to behavior and uncover the underlying mechanisms by reverse engineering the trained RNNs. Previous studies leveraged fixed points to compare RNNs across limited tasks and found that while the geometry of neural representations varies, fixed point topology is consistent across RNNs despite differences in model architecture [1]. However, linearizing dyna