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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* As we move beyond usability to create desirable interfaces, how do we handle a subjective domain like emotions?
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John Sweller's cognitive load theory focuses on the limitations of working memory during instruction. It describes three types of cognitive load - intrinsic, extraneous, and germane - that instructional design should seek to manage. The goal is to reduce extraneous load and increase germane load in order to not overwhelm working memory and optimize learning. Technology can help apply this theory by integrating multiple information sources and providing worked examples, but instructors must avoid distracting elements that increase extraneous load.
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Kain042710 mit sloan-school
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mesca
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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
Editor's Notes
- 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