Neuroscience and machine learning
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Looking at the relationship between Machine Learning, Deep Learning and the Brain Slides from a talk given at the Machine Learning and AI meetup in Melbourne. http://www.meetup.com/Machine-Learning-AI-Meetup/events/227156709/
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Neuroscience and machine learning
- 2. Me
- 3. Neurons
- 7. Real Neuron
- 8. Santiago Ramón y Cajal, Dendrites
- 10. channels Figure 21-8, Lodish 4th Edition. Ion Channels
- 12. Gerstner et al. 2014 Spike Patterns
- 13. Artificial Neuron Real Neuron Simple Complex Deterministic Stochastic Stateless Stateful Time Invariant Time Sensitive Artificial vs Real Neurons
- 14. ?
- 15. Huebel & Wiesel
- 17. Dr. V. Aggarwal
- 18. Neural Networks brain vs machine (learning)
- 19. Neural Network
- 23. Busbice et al. 2012 Connectome: C. elegans
- 25. Lowery et al 2009 Back Propagation
- 29. Convolutional nets & Visual cortex
- 30. Le et al. 2012 High Level Concepts
- 31. Multimodal Embeddings Weston et al.
- 32. Sharing is Caring neuroscience <==> ml
- 34. Hopfield Network
- 35. Ng et al. 07 Visual Receptive Fields
- 37. The Brain vs Deep Learning Part I timdettmers.com Readings
Hinweis der Redaktion
- excited then disappointed by AI study Neuroscience (by accident) germany free excited by ML
- turn up in both ML and Neuroscience Fundamental so how similare are they really
- Walk through the steps Simple Linear weights Tractable Deterministic Stateless
- explain structure of a neuron, analogy to artificial neuron dendrites => input electrochemical Complex Nonlinear ‘weights’ Stochastic? intractable differential equations (simplifications)
- dendrites are input neurons connect to many other neurons dendrite computation super complex non-linear resistance dendritic spikes
- This is like the `sum` and step function on an artificial neuron Neurons have state are time dependant fire all or nothing have to recover
- electrochemical nasty differentials hard to model
- different types
- (shows recordings of neurons), current injection different firing patterns Fast spiking Stutter Regular/Adaptive
- We don’t really know exactly how they work - unlike aritificial neurons which we understand really well
- One problem - Simple vs Complex stimuli
- The real world is somewhat different
- Another problem Hard to know what’s going on when in a network
- look at where they are similar and where not
- when i think of a neural net… 80 billion neurons 10 trillion synapses run on sandwiches and glasses of water beautiful mess
- ANN - Generally we think of this feed forward multi-layer trained via-backprop around since the 80’s There are of course fancier versions (RNN etc)
- these days its called deep learning multi-layer used to be hard, now: more data faster computers tricks (dropout) Deep Learning used to reference: Restricted boltzmann machine Auto encoders unsupervised feature learning Claims deep learning like brain
- Problems with feed forward geometry => faster/slower processing times asynchrony
- Problems with Backprop no supervised signal no error function no derivatives how would you communicate it with spikes? no bi-directional weights forward backward pass
- fundamental differences in paradigms learning brain vs ANN approach to learning
- learning paradigm problem very different to how we learn unsupervised (passive) RBMs reinforcement (active) one-shot (active)
- Similarities in the actual architecture
- Neural learnings of High level concepts grandmother cell in neuroscience google from youtube
- Learning concepts Embeddings switching between modalities
- where has knowledge been shared
- neuroscience uses a lot of machine learning it’s also given ML neural networks
- Hopfield Network Hebbian learning model for associative memory in the brain explain images
- RBM’s Visual cortext => evidence we learn from our surroundings General learning - somatosensory necker cube perception
- some stats. the future brains in a dish