#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer
Machine Learning Street Talk (MLST) - A podcast by Machine Learning Street Talk (MLST)
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Dr. Simon Stringer. Obtained his Ph.D in mathematical state space control theory and has been a Senior Research Fellow at Oxford University for over 27 years. Simon is the director of the the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, which is based within the Oxford University Department of Experimental Psychology. His department covers vision, spatial processing, motor function, language and consciousness -- in particular -- how the primate visual system learns to make sense of complex natural scenes. Dr. Stringers laboratory houses a team of theoreticians, who are developing computer models of a range of different aspects of brain function. Simon's lab is investigating the neural and synaptic dynamics that underpin brain function. An important matter here is the The feature-binding problem which concerns how the visual system represents the hierarchical relationships between features. the visual system must represent hierarchical binding relations across the entire visual field at every spatial scale and level in the hierarchy of visual primitives. We discuss the emergence of self-organised behaviour, complex information processing, invariant sensory representations and hierarchical feature binding which emerges when you build biologically plausible neural networks with temporal spiking dynamics. 00:00:09 Tim Intro 00:09:31 Show kickoff 00:14:37 Hierarchical Feature binding and timing of action potentials 00:30:16 Hebb to Spike-timing-dependent plasticity (STDP) 00:35:27 Encoding of shape primitives 00:38:50 Is imagination working in the same place in the brain 00:41:12 Compare to supervised CNNs 00:45:59 Speech recognition, motor system, learning mazes 00:49:28 How practical are these spiking NNs 00:50:19 Why simulate the human brain 00:52:46 How much computational power do you gain from differential timings 00:55:08 Adversarial inputs 00:59:41 Generative / causal component needed? 01:01:46 Modalities of processing i.e. language 01:03:42 Understanding 01:04:37 Human hardware 01:06:19 Roadmap of NNs? 01:10:36 Intepretability methods for these new models 01:13:03 Won't GPT just scale and do this anyway? 01:15:51 What about trace learning and transformation learning 01:18:50 Categories of invariance 01:19:47 Biological plausibility https://www.youtube.com/watch?v=aisgNLypUKs
