callerID-SNNs: Nomenclature and computer program symbols

• synaptic :
  • blurb - NOT a spike, but is the "signal" fir example : perhaps a temporary membrane potential increase for a neuron from one of the synaptic releases due to the firing of another neuron
  • pattern (synPatA) - is a sequence of all nSynPat*tSyn [past, future] synPatA due to the firing of all connected neurons, with respect to a specific neuron - keep in mind that the synPats from the same firing neuron differ for each connected neuron due to [dendritic synapse] connection geometries and delays!
• sequence :
  • is a sequence of all nSynPat*tSyn [past, future] synaptic [blurb, noise]s with respect to a specific neuron
  • this includes, but is distict from, synPatAs

• fireT_[01, 02, 03,...] (*)= firing (spiking) timeScales. T_01 is the lowest (first) = tSyn*nSynSeq. For now (23Aug2023), subsequent timeScales are a fractal (multiple) of the previous - factor-of-3 is used in honour of Puetz's "Universal Wave Series" (super-duper Mayan calendar-like for many known periodicities).
  • firT_01L (L) = firing for a given nur that has occurred at T01 timeScale, as recognized from synPatA for nur.
• futureSynSeqA = synSeq that will occur in future nSynPat*tSyn from firing of other neurons
• nNur (*) = number of neurons connected to nur
• nSynPat (*) = number of synaptic blurbs in synPat
• nSynSeq (*) = number of synaptic blurbs in synSeq
  • first spike on left (oldest to newest) - just an easy convention on QNial
• nurInnL = input neurons (eg from sensory, whatever), have synPatL, don't have firPatL
• patMatchL = nSynPat list that has l for each nur that matches pat
• seqMatchL = nSynSeq list that has l for each nur that matches seq
• patL_match_patL = list of lists because pat is already a list
• seqL_match_patL = synSeqL EACHLEFT EACHRIGHT synPat_in_synSeq synPatL
  • list of lists because seq is already a list
• synFracPat (*) = fraction of synPat that is l, for initial random generation of synPats
• synFracNse (*) = fraction of synSeq that is noise, for initial random generation of synSeqs
• synPatA (L) = "synaptic blurb" time sequences for nurs connected to a specific nur
  • this is how nur IDs other nurs, for now assume starts in soma
  • different neur(j) perceive different synPats for same nur(i)!
  • here it is assumed that synaptic connections are "tuned" (see below)
• synSeqA (L) = [past current, future" synaptic blurbs seen by a nur, including :
  • future blurbs coming in from a spike from a connected neuron
  • noise
  • these time sequences advance one position each tSyn
• tSyn (*) = smallest delta(time) in callerID-SNN, detects post-synaptic blurbs
  • assume something like 1 millisecond (will have to check this, no effect for now)

• (*) common basis for ALL nurs (simplicity)
• (A) denotes an array, usually 2D, but can be any integer dimension to the limit of computer memory
• (L) denotes a list
  • appending an L often indicates that the symbol applies to ALL neurons in overall system
  • [pat, seq] are already lists by definition, so L is not appended for one [pat, seq]
  • appending to symbols above mean that some symbols end with 'LL', 'AL' etc

• "tuning" of synPatA : must ensure "pragmatic novelty" for each nur in SynPatA (connected neurons). It is not important to [maximize, optimize] dissimilarities at this stage. A "pragmatic difference" is sufficient.
  
  This is VERY different from normal Neural Network "parameter adjustment" (training). It is nowhere close to network evolution.
  • pre-training to ensure sufficent dissimilarities of synPats .
  • perhaps easiest to generate & test, than to plan?
  • very easy to add a nur synPat when these are sparse
  • much harder when synPats are dense (approaching limits, for example)
  • I'm thinking of "waves" for tuning retinotopic mapping (J.P. Thivierge)
• "ID recognition" of a synPat in synSeq :
  • first syn (synaptic blurb) is on the left (oldest to newest)
  • this means that the leftmost syn must = l, for BOTH syn[Pat, Seq] for there to be a test of callerID (test is run each tSyn)
  • if synPatA is [complete, recognized] in updated synSeqA,
    • it changes nurSpkA@(nur nurInn) (@tSyn=0) to l (true)
    • it also may NOT match the actual spiking of nurInn!,
    • due to losses, confusion of source, etc

• str string
• pat pattern
• fir firing sequence (already is a list, so no ending L for a single nur)

• _init = initializing value
• _std = standard test value to compare to calculated result
• _test = operator that tests operator with same name root (without _test)

• create new [patterns, sequences] - randomness approach
• nSeq_makeRdm_spkSeq IS OP spkTimeFrac nSeq - random spike sequence of length nSeq
• spkSeq_in_spkPat IS OP spkSeq spkPat - simplest filter requires an absolute match in the sense that ALL spkPat = 1 are also in spkSeq, noise doesn't matter

• input pattern pat_in@(neur(i) neur(j) synPat(neur(i))), where :
• synPatL_synSeqL_matchTest IS
• nSynSeq_patFrac_seqFrac_matchTest IS OP nSynSeq nsynPatL nsynSeqL patFrac seqFrac
  • nSynSeq used for both synPatL and synSeqL (easy to compare!, buffer nsynPat if too short)
• firT01L_synSeqA_advance_test IS - firing of one nur only
  • advances synSeqA, including "future" synSeqA to come in from firT01L
  • current tSyn is midpoint of synSeqA
  • current tSyn is midpoint of synSeqA
• firT01L_synSeqA_advance_test IS - for testing, wouldn't normally know firT01L in future
  • for simplicity, it is assumed that firing occurs only tFire = nSynPat * tSyn
  • that way, all nSynPat old synSeqA are cleared
  • current tSyn is midpoint of synSeqA