Hi Raf,
Welcome! Please do not respond to this, I'm just putting in a "time
filler" until someone more neurobiologically knowledgable answers this
later on today, but "A" is in fact being modeled by NuPIC, as often is
stated in these circles that the synapses on distal dendrites act as
"proximity detectors" - and we model this via a summation of synapse
"permanence" values. In addition there is some portion of organic
necessity not being abstracted in the software as some of these
concepts were deemed to not have a significant enough impact on the
overall process (as you also stated). Spikes (some not all), to a
certain extent (from explanations I have overheard on this mailing
list) are one of those concepts that organic implementation requires
yet do not have a significance with regard to maintaining the metaphor
in software. Some Spikes are in fact being modeled as a summation
instead of a continuous rate (I believe).
Much care (by leading edge neuro-scientific researchers at the Redwood
Institute as well as Numenta researchers), has been taken to ascertain
that portion of explicit biological translation which is necessary to
implement the over-all algorithms, but as always (as I have observed),
the community is open to theoretical examination of their assumptions
in this regard.
Cheers,
David
On Sat, Dec 5, 2015 at 6:04 AM, Raf <[email protected]
<mailto:[email protected]>> wrote:
Hi everybody.
I've got a couple of questions for you.
I'm a med student and I'm new to Nupic.
I'm very impressed by what Numenta is achieving and I do believe
that your work in the long run will be compared to the discovery
of penicillin :)
My project -for now- is to produce a model able to detect
neurological/psychiatric issues through a simple eeg waves
recognition; I'm an intern at the Neurosurgery dept. and my final
goal would be to use patterns recognition as an intraoperative
tool to help surgeons distinguish between healthy tissue and
cancerous cells just with a continuous eeg/emg data feed.
In order to get familiar with the machine learning world, and not
disposing of good enough datasets, since a couple of years I use
financial data (notoriously difficult-impossible do predict) as a
sandbox environment to experiment with NNs. I had encouraging results.
I'm still learning the python code behind nupic and I've two
questions for you.
1 - FIRST QUESTION
In the paper "Hierarchical Temporal Memory" (version 0.2.1, 2011),
I read that: "[...] The predominant view (especially in regards to
the neo-cortex) is that the rate of spikes is what matters.
Therefore the output of a cell can be viewed as a scalar value".
I'm aware that transforming a biological complex system such as
the neocortex into an computer software necessarily leads to
simplifications. As we know from a biological point of view the
transmission of the signal is subject to numerous variables and I
wonder how their implementation could improve the software
predictions.
The variables I'd like to focus on are:
-A) The propagation of the action potential along the
membranes follows an exponential loss distribution due to the
resistances met along the axons. For the HTM model (where the
synapses express binary weights) this could mean that the more
distant two connected cells are, the weakest their shared signal
becomes (of course directly depending on "where" the dendrite
segments are from their starting point - this would probably
require the introduction of the physical concept of space in HTM).
-B) The signal propagation speed is directly proportional to
the axon's diameter carrying it; this appears to be valid both in
unmyelinated and myelinated axons (though representing a more
obvious phenomenon in the latter type). This could have a huge
impact for HTM: if bigger axons (= weight+) burst temporally
before other smaller ones towards the same target dendrite, they
can also inhibit temporarily that targeted cell (causing a later
refractory period) therefore filtering the signal.
-C) Receptors, neurotransmitters, electrical and chemical
synapses, EPSP (excitatory postsynaptic potential) and IPSP
(inhibitory postsynaptic potential) . This is an enormous chapter.
Current NNs systems and, if my understanding is correct, also
Nupic treat synapses like if they were all electrical synapses. In
reality, according to the current consesus, the mammal brain uses
electrical synapses mostly to "synchronize" vast areas of the
neocortex (I'm deliberately omitting other findings because are
not relevant to my point). Although the electrical synapses
demonstrates various advantage when compared to their chemical
equivalents (speed, resistance, fatigue, etc.), it appears that
the complexity and the fine filtering/modulation of the signals
inside the PFC is due to the presence of numerous other elements
present in chemical synapses: neurotransmitters (such as
acetylcholine, dopamine, gaba, norepinephrine...); pre-synaptic,
synaptic gap and post-synaptic features; different receptors; etc.
Each of these elements can strongly influence the signal and the
overall "learning" process. For example: although an axon "weight"
is big and it is bursting copiously the above mentioned elements
can suppress its signal.
My first question is: are the first two points (A and C)
implemented in Nupic? Do you reckon that it could be useful to
increase the complexity of Nupic also implementing the chemical
synapses "class" with the elements described in point C?
2 - SECOND QUESTION
I'm trying to run a couple of models. This is an extract from a
OPF I created through swarming.
'model': 'CLA',
'modelParams': {'anomalyParams': {u'anomalyCacheRecords': None,
u'autoDetectThreshold': None,
u'autoDetectWaitRecords': None},
'clParams': {'alpha': 0.06173462582232023,
'clVerbosity': 0,
'regionName': 'CLAClassifierRegion',
'steps': '0'},
'inferenceType': 'NontemporalClassification',
'sensorParams': {'encoders': {u'DATE_dayOfWeek':
None,
u'DATE_timeOfDay': {'fieldname': 'DATE',
'name': 'DATE',
'timeOfDay': (21,
2.2537623685060675),
'type': 'DateEncoder'},
u'DATE_weekend': None,
'_classifierInput': {'classifierOnly': True,
'clipInput': True,
'fieldname': 'VO',
'maxval': 2.0,
'minval': 0.0,
'n': 449,
'name': '_classifierInput',
'type': 'ScalarEncoder',
'w': 21},
u'o10N_A': None,
u'o11N_A': None,
u'o12N_A': None,
u'o13N_A': None,
u'o14N_A': None,
u'o15N_A': None,
u'o1N_A': None,
u'o1N_B': None,
u'o2N_A': None,
u'o2N_B': None,
u'o3N_A': None,
u'o3N_B': None,
u'o4N_A': None,
u'o4N_B': None,
u'o5N_A': None,
u'o5N_B': None,
u'o6N_A': None,
u'o6N_B': None,
u'o7N_A': None,
u'o7N_B': None,
u'o8N_A': None,
u'o8N_B': None,
u'o9N_A': None,
u'o9N_B': None},
'sensorAutoReset': None,
'verbosity': 0},
'spEnable': False,
'spParams': {'columnCount': 2048,
'globalInhibition': 1,
'inputWidth': 0,
'maxBoost': 2.0,
'numActiveColumnsPerInhArea': 40,
'potentialPct': 0.8,
'seed': 1956,
'spVerbosity': 0,
'spatialImp': 'cpp',
'synPermActiveInc': 0.05,
'synPermConnected': 0.1,
'synPermInactiveDec': 0.0005},
'tpEnable': False,
'tpParams': {'activationThreshold': 16,
'cellsPerColumn': 32,
'columnCount': 2048,
'globalDecay': 0.0,
'initialPerm': 0.21,
'inputWidth': 2048,
'maxAge': 0,
'maxSegmentsPerCell': 128,
'maxSynapsesPerSegment': 32,
'minThreshold': 12,
'newSynapseCount': 20,
'outputType': 'normal',
'pamLength': 1,
'permanenceDec': 0.1,
'permanenceInc': 0.1,
'seed': 1960,
'temporalImp': 'cpp',
'verbosity': 0},
'trainSPNetOnlyIfRequested': False},
If I understood correctly, all the inputs (from o1N_A to o15N_A)
were discarded by the swarming process. I've also run a larger
swarm, but they are still discarded. Unfortunately I'm sure that
at least a good 60% of them are relevant sensors. How can I
improve the swarming? Am I doing something wrong? (The sensors are
outputs from thoracic low-res electrodes; the predicted field is
"VO" which represents the amount of spO2 present in the blood
stream at the moment - the idea is to predict the oxygen
saturation from the respiratory act).
Thanks for your replies. Sorry for my english (I'm italian).
Raf
--
Raf
www.madraf.com/algotrading <http://www.madraf.com/algotrading>
reply to:[email protected] <mailto:[email protected]>
skype: algotrading_madraf
--
/With kind regards,/
David Ray
Java Solutions Architect
*Cortical.io <http://cortical.io/>*
Sponsor of: HTM.java <https://github.com/numenta/htm.java>
[email protected] <mailto:[email protected]>
http://cortical.io <http://cortical.io/>
