#] 
#] *********************
#] "$d_MindCode"'11_MindCode summary notes.txt'  
www.BillHowell.ca  24Feb2020 initial - from link  d_QNial_mine  'MindCode/0_MindCode notes.txt'
17Nov2023 copied d_Qndfs: from d_MindCode (2020) to d_Mind2023


48************************************************48





24************************24
# Table of Contents :  
# $ grep  "^#]"  "$d_MindCode"'11_MindCode summary notes.txt'  |  sed 's|^#\] |   |' 
# 
   *********************
   "$d_Qndfs"'Mind2023/11_MindCode summary notes.txt'  
   ToDos and issues - long-term rather than operational
      ToDos 
      Issues 
      Biological plausibility 
      net types 
      MindCode-  assumptions :
      Control structures
   Global [variable, list]s  
      netTypes - [boolean, integer, real] types are supported so far (27Feb2020)
      net global lists (structure)	- view with : 
      other globals
      netIDList  - ALL netIDs must be <= shape netIDList!!!  
   21Feb2020  thinking of programs :  
   24Feb2020  QNial basis 
   General thoughts to consider :
   22Feb2020 neuron names - not so simple to do
   NN-specific - sigmoidal, counter, timer


24************************24
30Apr2020  How to use MindCode
1. loaddefs link  d_QMindCode  'MindCode basics.ndf'  
	this loads (example) :
		 loaddefs link  d_QNial_mine  'MindCode/globals initialize.ndf'  ;  
		 loaddefs link  d_QNial_mine  'MindCode/data types.ndf'  ;  
		 loaddefs link  d_QNial_mine  'MindCode/trigger patterns.ndf'  ;  
		 loaddefs link  d_QNial_mine  'MindCode/bits to-from [intg, real].ndf'  ;  
		#loaddefs link  d_QNial_mine  'MindCode/net constructors.ndf'  ;  
2. loaddefs example net from d_QMindCode
		arith, trig examples.ndf
		MLP - multi-layered perceptron.ndf
3. loaddefs test nets - each net file has a set of tests to run, which can be launched on load
	see 'MindCode/code test',  choose one of : 
	$ ls -1 "$d_Qndfs""MindCode/code test"
		bits to-from [intg, real] tests.ndf
		fire tests.ndf
		index tests.ndf
		MLP tests.ndf
		trigger [now, cum, seq, tim, mix] tests.ndf
4. [Compare, update] test results as collected in : 
		0_tests collected results log.txt
48************************************************48









08********08
#] ToDos and issues - long-term rather than operational


#]    ToDos 
# should change to more biologically-plausible code!!!

#]    Issues 
# preNetIDs can call multiple netIDs, but processing is allows ONLY when the target netID isn't refractory
# 24Feb2020 temporary "variables" (netIDs) to be dealt with later  ;  
# conventional NNs assume that synapses add to the membrane potential, so they don't need such an operator  ;  

#]    Biological plausibility 
# [concept, mirror] neurons apparently are high-level representations?
# Problem :  how does a cell (neuron may not differ much) do physiological functions etc?  DNA, RNA, etc - biomolecule [transport, reaction] etc.
# ANN  often sum wij*xj (continuous)  - but how would that actually occur?  Cell membrane potential cannot do that alone?!?
# this is NOT biologically realistic!!? - computations at cell level!  
# but could [build, evolve] complex net!!!   like McCulloch&Pitts
# what functions can the following structures easily provide? : 
	- 6-layer cortex column 
	-?4?layer neocortex column
	- hippocampus
	- thalamus
	- other?

#]    net types 
# 25Feb2020 nets Must connect with preNetIDs of the proper type!  (eg Z80) - checked during data initiation

#]    MindCode-  assumptions :
	"data-latches" many available, but each captures ONLY one neuron output per time window
	"time window" to complete data input (or is reset)
	"refractory period" to do computation & spike
	"spike result" onward
	"open for business" period
# My job isn't to [decide, propose] mechanisms, but to list conjectures and provide a tool with "multiple conflicting hypothesis", as speculative as they might be

#]    Control structures
# possibly [mRNA, epi]-based? - it may REQUIRE this, and in any case is a goal of this project!
# chains of signal proteins on microtubulo nucleus, only molecules don't travel the whole distance
# 		immediate throughput like fire-bucket brigade



08********08
#] Global [variable, list]s  
#  LOCAL variables convention  -  simply drop the "List" part for the local variable name
# neurons are nets of "shape = 1" (list), so their [firing, netID, etc] Lists must be solitaries!!!

#]    netTypes - [boolean, integer, real] types are supported so far (27Feb2020)

#]    net global lists (structure)	- view with : 
transpose mix  netIDs	inNetList	typeList  optrList  firingList  stateList  wghtList  

#]    other globals
write  transpose mix 
	("int_loww  "int_high  "real_loww  "real_high  "realBase_max  "bitCnt_intBase  
				"bits_sign  "bits_base  "bits_expt  "bitCnt_sign  "bitCnt_base  "bitCnt_expt  "realCode )  
	( int_loww   int_high   real_loww   real_high   realBase_max   bitCnt_intBase  
				 bits_sign   bits_base   bits_expt   bitCnt_sign   bitCnt_base   bitCnt_expt   realCode )   

write  nowFireList  


#]    netIDList  - ALL netIDs must be <= shape netIDList!!!  
		eg  OR ((link netIDList)  <  (gage shape netIDList))

statList is a series of boolean lists :
bits are :  


***********
#] 21Feb2020  thinking of programs :  
- make programs easily-adaptable to vastly different [number, concept, architecture, etc] bases 
- initial work basically an 8-bicroprocessor! (Z-80 like in my case)
-	QNial basis - see "QNial the Language Definition" Michael Jenkins, 01 August 2015
		also "The Nial Dictionary"  Version 6.3, August 2005, Q'Nial Systems Limited
		"MindCode neuron - QNial basis.ndf"  : 
	arithmetic operations - [+, -, *, /, ^, trigonometric, NN-specific]  
	comparison operations 
	control structures 
	logic operations 
	measurement operation 
	construction operation 
	apply-by-partition transformer 
	NN-specific basics - sigmoidal, counter, timer, concept neuron 
- MatLab basis  
- Izhikevich-like neuron - with [DNA-mRNA-epi, addressable synapses]
- Architectures :
	- null
	- [hyperbolic tanjent, sigmoidal] neurons - direct translation of MLPs
	- memory
	- [concept, grandmother] cells
	- random [LSM, Echo State, Extreme Learning Machine]
	- variables [boolean, integer, real, complex, etc]
	- [data, spiking] structures
	- scalars [1-2-3-dimensional (eg distance, area, volume), ...]
	- time [scalar, spectral, wave, ...]
	- fractal, fractional order calculus
	- operators [+-*/, derivative, integral, etc]
	- [search, patterns, classification, ]
	- conciousness [John Taylor, ...]
- processes [learn, evolve, ...]
	- noise 
- [existence, uniqueness, stability, convergence, ...]


08********08
#] 24Feb2020  QNial basis 
- see "QNial the Language Definition" Michael Jenkins, 01 August 2015
	also "The Nial Dictionary"  Version 6.3, August 2005, Q'Nial Systems Limited
	"MindCode neuron - QNial basis.ndf"  : 
#	arithmetic operations 
		abs, floor, mod, ceiling
		+, plus, sum
			The operation plus is the same as sum except that plus enforces the rule that it must be applied to a pair. Sum is multi pervasive and can add up any number of items.  The symbol + is a synonym for sum.
		-, minus, opposite
		*, product, times
			The symbol * and prod are synonyms for product.  The operation times is product restricted to use on pairs. 
		/, divide, quotient, reciprocal
			The operation quotient is used in place of divide in situations where the result must be an integer. 
		^, power  149
		exp, ln, log 
		sin , cos , tan 
		sinh, cosh, tanh
		arcsin , arccos , arctan 
		arcsinh, arccosh, arctanh 
		
#	comparison operations 
		>, gt  92
		>=, gte  93
		<, lt  118
		<=, lte  119
		match  120
		mate  120
		max  121
		min  122
		up  231
#	control structures 
		case-expr  29
		for-loop  85
		if-expr  95
		repeat-loop  180
		while-loop  237
#	logic operations 
		and  17
		diverse  55
		equal  67
		not  129
		or  135
		unequal  230
#	measurement operation 
		allbools 
		allchars 
		allints 
		allnumeric 
		allreals
		axes  17
		depth
		isboolean  102
		ischar  102
		isfault  102
		isinteger  103
		isphrase  103
		isreal  104
		isstring  204
#	construction operation 
		append  10
		cart  28
		catenate  30
		hitch  94
		laminate  107
		link  111
		pair  137
		single  202
		solitary  204
#	apply-by-partition transformer 
		bycols  24
		byrows  25
		partition  139
		rank 162 
		reducecols  172
		reducerows 172 


08********08
#] General thoughts to consider :

# neuron-specific read from file : 
synapses_inn	- list of input  neurons, + normal, - inhibitory  
synapses_out	- list of output neurons  
neuromods		- neuro-modulator states Kenji Doja [dopamine, seratonin, nor-adrenaline, acetocholine]  
rules_fire		- list of rules  
rules_clear		- list of rules  
spike_inn		- recent firing timestamps (stack?)
spike_out		- recent firing timestamps (stack?)

# multiple synapses for each [input, output] neuron?
# [maintenance, noise]-firing
# all spikes same magnitude (pulse) at synapses
# neurons should be event-driven for efficiency?
# redundancy and wandering computations
# mathematical theory of functions
# refractory periods - [neuron, net, system]
# memory - keep state somehow, bursting neuron repeats

# as determined by DNA-mRNA-epi : 
# 		fires with "coherent input" when active, 
# 		extinction of inputs 
#			with time 
#			when fires
#			random & noise  
#			otherwise
#		sequence of inputs is important

# I would like to use Nvidia CUDA for GPU  


08********08
#] 22Feb2020 neuron names - not so simple to do
# With massive redundancy, and multi-level specialised [functional, modular, regional, whole-brain] networks, there needs to be a way of naming neurons simply for local calculations, but that maps them for the global system.
# For now, just go with a global list of neuronIDs.


08********08
#] NN-specific - sigmoidal, counter, timer


# enddoc