Topic: AI architectures

Gabriele Scheler argues that computational accounts of neurons should include intracellular processes alongside spiking. She discusses a proposed single-neuron model, neuromodulation and possible implications for simpler AI architectures, while connecting those ideas to verbal thought, language models and the experience of establishing an independent research foundation.

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