Topic: spiking

In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification p

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.