Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-The lecture has two themes: embedding networks in a levels diagram and embedding networks into an operating system.

2.-Nothing in biology makes sense except in light of evolution. The 6-layer mammalian cortex enabled cortex expansion.

3.-The 24 ganglion cell types in the retina provide input on light intensity changes, enabling efficient representation.

4.-A neuromorphic camera encodes continuous waveforms as spike sequences, enabling velocity computation and efficient, low-power processing.

5.-Natural images have scale invariance and a 1/f^2 power spectrum, reflecting power law scaling.

6.-Percolation theory applied to natural images reveals a phase transition in information content around the middle bit planes.

7.-Renormalization group theory has a formal analogy to deep learning architectures, suggesting a critical point may be involved.

8.-Priming is powerful one-trial, unconscious learning that can last for years, reorganizing cortical representations.

9.-Repetition suppression in IT cortex after priming may sharpen representations by increasing some connections and decreasing others.

10.-Homeostatic plasticity scales synapses to maintain neuron firing rates, enabling efficient sparse coding of features.

11.-Combining Hebbian and homeostatic plasticity enables nonlinear ICA to extract features like horizontal and vertical bars.

12.-Temporal difference learning uses dopamine to update weights based on reward prediction errors for reinforcement learning.

13.-The basal ganglia receives convergent cortical inputs and outputs to dopamine neurons that innervate the entire cortex.

14.-Three cortico-basal ganglia-thalamic loops associate sensory input with effectors, outcomes, and plans on different timescales.

15.-Many distinct learning algorithms like priming, homeostatic plasticity, and temporal differences occur simultaneously in the brain.

16.-The volume of synaptic spines is proportional to synaptic strength, with ~5 bits of precision per synapse.

17.-Most synapses have low probability of release despite high strength precision, possibly implementing a form of dropout regularization.

18.-Evolution has discovered optimal solutions to many problems in learning and memory that we are just beginning to understand.

19.-Past experience, instantiated by the hippocampus, is evaluated by reward systems to plan future behavior.

20.-Processing inaccessible to consciousness, like priming, reveals extensive unconscious learning in the cortex.

21.-Unconscious, fast "gut responses" depend on experience and are optimized by evolution for survival, complementing slower explicit reasoning.

22.-Analyzing the visual system reveals it encodes more than just visual stimuli, including value information.

23.-The superior colliculus receives convergent cortical inputs and helps orient the animal based on sensory and internal inputs.

24.-The entire cortical mantle projects to the basal ganglia striatum, which has parallel pathways supporting different functions.

25.-Nature has evolved dozens of forms of synaptic plasticity that work together synergistically, beyond just backpropagation or Boltzmann learning.

26.-Predictive priming experiments show the visual system does more than just build a model of the world from sensory input.

27.-Percolation occurs in natural images with a critical exponent of 0.6, suggesting a phase transition in image statistics.

28.-A sharpening effect occurs after priming where strongly active neurons increase connectivity while moderately active neurons decrease connectivity.

29.-The basal ganglia compute state value used for updating weights and making decisions in an actor-critic reinforcement learning architecture.

30.-Brain architecture enables fast, unconscious responding for experts via the basal ganglia vs slow, explicit reasoning for complex decisions.

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