Task representations in neural networks trained to perform many cognitive tasks

• task representation = RNN weights when performing specific task (?)

• task explanation and codes are in https://github.com/gyyang/multitask/blob/master/task.py

• tasks at a glance - there are two modalities of stimulus
  
  

• learn multiple tasks “sequentially” with continual-learning technique

  • if there’s some ‘state’ in each task, would there be replay memory?
  • is there any biased results on the “sequence” of the task?
    • if tested it will take all permutations of that all tasks…

• RNN model: just one hidden layer & non-negative (with 256 units)

  • and calculated task variance. also clustering across units.
    • didn’t read much about how they did clustering and clean out the noise

• 32 ring (direction-specific recurrent unit)

  • how is is possible to force them response to specific directions??

• (just cameout) one paper using fMRI

  • complexity defined as number of choices (but the same task family unlike us)
  • more complex task → people tends to do model-based thinking
  • https://www.nature.com/articles/s41467-019-13632-1

• in line with above paper, I think we should think about each task’s complexity

  • what amount information (or dimension) should be sufficient for correct answer?
  • ‘amount’ and ‘dimension’ → complexity (how does it combine?)

• task vector

  • why do they use only “steady-state” response across stimulus?
  • they can’t capture the task variance (sensitive unit activity…)
  • how about trying DSA like https://github.com/mitchellostrow/DSA
  • I don’t know
  • how to combine the ‘task variance’ to this?

• task vector 2

  • tho it’s cool that there could exist algebraic form of compositional representation

• rule compositionality (combination of rule inputs!!!)

  • most important part of this paper
  • Dly Anti task = Anti + (Dly Go - Go)
  • but failed in DMS != DMC + DNMS - DNMC
  • can we use multiplication or convolution??https://neuroai.neuromatch.io/tutorials/W2D2_NeuroSymbolicMethods/student/W2D2_Tutorial2.html

• a bit of continual learning

  • Dly GO → Ctx Dly Dm1, Ctx Dly DM2 ----- forget Dly Go
  • added “penalty for deviations of important synaptic weights” ~ regularizer

• I didn’t look through much about

  • how they clustered
  • how they analyzed with different activation functions (tanh, ,,,)
  • how they did continual learning (method, regularizer part)