Hi Raf,
Oops.
... and yes both "A" and "C" are modeled ("C" being inhibitory chemical and
cellular effects) - but again not as the multiple discrete values - more
like a summation of their effect (except for inhibitory cells whose effects
are explicitly modeled).
Cheers,
David
On Sat, Dec 5, 2015 at 6:53 AM, cogmission (David Ray) <
[email protected]> wrote:
> 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]> 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
>> reply to: [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]
> http://cortical.io
>
--
*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]
http://cortical.io
