102 lines
3.0 KiB
Julia
102 lines
3.0 KiB
Julia
module Ironpen
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export kfn_1, synapticConnStrength!
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""" Order by dependencies of each file. The 1st included file must not depend on any other
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files and each file can only depend on the file included before it.
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"""
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include("types.jl")
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using .types # bring model into this module namespace (this module is a parent module)
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include("snn_utils.jl")
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using .snn_utils
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# include("Save_and_load.jl")
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# using .Save_and_load
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# include("DB_services.jl")
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# using .DB_services
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include("forward.jl")
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using .forward
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include("learn.jl")
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using .learn
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include("readout.jl")
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using .readout
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include("interface.jl")
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using .interface
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#------------------------------------------------------------------------------------------------100
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"""
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Todo:
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[] using RL to control learning signal
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[] consider using Dates.now() instead of timestamp because time_stamp may overflow
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[*1] training should include adjusting α, neuron membrane potential decay factor
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which defined by neuron.tau_m formula in type.jl
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[DONE] each knowledgeFn should have its own noise generater
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[DONE] where to put pseudo derivative (n.phi)
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[DONE] add excitatory, inhabitory to neuron
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[DONE] implement "start learning", reset learning and "learning", "end_learning and
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"inference"
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[DONE] output neuron connect to random multiple compute neurons and overall have
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the same structure as lif
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[DONE] time-based learning method based on new error formula
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(use output vt compared to vth instead of late time)
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if output neuron not activate when it should, use output neuron's
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(vth - vt)*100/vth as error
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if output neuron activates when it should NOT, use output neuron's
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(vt*100)/vth as error
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[DONE] use LinearAlgebra.normalize!(vector, 1) to adjust weight after weight merge
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[DONE] reset_epsilonRec after ΔwRecChange is calculated
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[DONE] synaptic connection strength concept. use sigmoid, turn connection offline
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[DONE] wRec should not normalized whole. it should be local 5 conn normalized.
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[DONE] neuroplasticity() i.e. change connection
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[DONE] add multi threads
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Change from version: v06_36a
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-
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All features
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- multidispatch + for loop as main compute method
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- hard connection constrain yes
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- normalize output yes
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- allow -w_rec yes
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- voltage drop when neuron fires voltage drop equals to vth
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- v_t decay during refractory
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duration exponantial decay
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- input data population encoding, each pixel data =>
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population encoding, ralative between pixel data
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- compute neuron weight init rand()
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- output neuron weight init randn()
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"""
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end # module end
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