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The 5th WBA Hackathon Orientation -- Cerenaut Part

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The 5th WBA Hackathon Orientation -- Cerenaut Part

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The presentation by Cerenaut at the orientation (2021-06-05) for the 5th WBA Hackathon, an online AI competition to implement working memory
https://wba-initiative.org/en/18687/
- Neuroscientific issues
- Architecture details
- Instruction on the CodaLab competition

The presentation by Cerenaut at the orientation (2021-06-05) for the 5th WBA Hackathon, an online AI competition to implement working memory
https://wba-initiative.org/en/18687/
- Neuroscientific issues
- Architecture details
- Instruction on the CodaLab competition

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The 5th WBA Hackathon Orientation -- Cerenaut Part

  1. 1. International collaborations ● WBAI (Japan) ● Luria (New York) ● Numenta (co-authored Boosted RSM) Understand animal intelligence / the brain Improve machine intelligence ○ Independent Research Group ○ Interested in interaction of brain regions for intelligent behaviour and decision making Cerenaut Supervise graduate students at Monash Founded in 2018, Publishing since 2012
  2. 2. Working Memory - What is it? Working memory is a short-term repository for task-relevant information that is critical for the successful completion of complex tasks (Baddeley, 2003). E.g. in a spatial working memory task, animals must hold in memory the location of food rewards to navigate to those locations after a delay.
  3. 3. Primates on DM2S Figure reproduced from ‘Principles of Neuroscience’ (Kandel et. al), reproduced from Rainer, Asaad, and Miller 1998.
  4. 4. Persistent activity in frontal cortex Figure reproduced from ‘Principles of Neuroscience’ (Kandel et. al)
  5. 5. Neuroanatomy What and Where: ● Converge at Hippocampus. ● A Short Term Memory. ● With strong projections to PFC.
  6. 6. PFC <> BG <> Thalamus Girard, Benoît & Tabareau, Nicolas & Berthoz, Alain & Slotine, Jean-Jacques. (2006). Selective amplification using a contracting model of the basal ganglia. Midbrain VTC, SNc Trains itself and the actor Figure reproduced from ‘Computational Cognitive Neuroscience’ (O’Reilly, Frank et. al) Via Thalamus context
  7. 7. Gating stripes ● Persistent neural activity through two major mechanisms: ○ 1. Intrinsic membrane properties ○ 2. Recurrent connectivity Figure reproduced from ‘Making Working Memory Work’, 2016 (O’Reilly and Frank)
  8. 8. DM2S/M2S/M2L Inherits: ActiveVisionEnv (Environment) Config: game_name_env.json Positional Encoding Retina (DoG +/- coding) SuperiorColliculus (track to position) SparseAutoencoder (Visual Cortex) What Where PrefrontalCortex MedialTemporalLobe (Short term memory) Agent (Actor - BG) Config: stub_agent_x.json Pretrained network RL Policy module Legend Reward AgentEnv (Environment) Config: stub_env_x.json Visual Path Fovea Periph Gaze Choices Data: what-where Critic (PVLV) SparseAutoencoder (Visual Cortex) Observation Action Gaze position command (absolute coordinates) Gaze position command Retina (DoG +/- coding) Gym Environment Fixed function Pass-through Visual Path Naming: Class Name (function) Delay
  9. 9. Active Vision Fovea Fovea: Can recognise shapes, but can’t see context. Periphery: Can see changes, but can’t recognise shapes. Periphery
  10. 10. Positional Encoding Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.
  11. 11. Software environment Lots of good information on the Wiki →
  12. 12. Software stack PyGame NumPy, PyTorch OpenAI Gym: Gym.Env Participant code Ray, RL Lib Ray RL Lib TorchModelV2 DM2S_Env Agent FiniteStateEnv ActiveVisionEnv PyGameEnv Participant code Environment Agent AgentEnv Task agent.stubs.medial_temporal_lobe agent.stubs.positional_encoder agent.stubs.prefrontal_cortex agent.stubs.superior_colliculus agent.stubs.visual_path Agent (brain) - pretrained RL Agent
  13. 13. Competition ● CodaLab ● Evaluation process ● Preparing submission

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