Monday, October 1, 2018


Temporary Stop

I have decided to stop writing artificial intelligence and physics articles based on my interpretation of occult Biblical texts. At least for the time being. The reason is that I am not wise enough to judge the potential consequences. This moratorium will continue unless I somehow become wiser or someone wiser than me convinces me to end it. I may publish a demo AI application but not the source code.

By the way, I am on Twitter and Gab where I post my thoughts on various subjects including AGI, politics, mainstream AI, free speech, world affairs and architecture. I will make all major future announcements (such as the release of a demo app) on Gab exclusively.

Friday, August 24, 2018

Temporal Correlations and Predictions in the Cortex

Using Minicolumns for Predictions and Temporal Correlations

This is another short post. I have written previously about cortical columns. I claimed that the minicolumns that comprise cortical columns are highly predictive event detectors. That is to say, the firing of a minicolumn is predictive of the firing of a minicolumn residing in another cortical column. Minicolumns owe their predictive abilities to the fact that they detect rare temporal correlations.
Note to believers: The Book of Zechariah (Zech 3:8) uses the Hebrew word 'mo.phet' which means wonder, miracle, symbol or sign (of a future event) to describe the activation of a 7-node minicolumn. That is to say, the activation of a minicolumn (the stone with seven eyes) is a rare event that announces another event.
Temporal correlations run the gamut from highly correlated to occasionally correlated concurrent signals. The signals that arrive at the entry or bottom layer of the neocortex come from highly correlated patterns. As one climbs up the cortical hierarchy, the minicolumns detect less and less frequent correlations. It just so happens that the less frequent a correlation is, the more deterministic it also is. A fully deterministic prediction is one that has only one predictor. In other words, minicolumns at the higher levels of the cortical hierarchy are very deterministic.
A cortical column shown with five minicolumns
This is all for now.

See Also:

Fast Cortical Learning Using Spike Timing

Thursday, August 23, 2018

How Would the World React?

A Question to Readers

How would people from different countries, cultures and religions react if artificial general intelligence suddenly arrives on the world scene (say, in the form of a human-level intelligent robot) and it is clearly demonstrated that the science behind it was written down thousands of years ago in metaphorical texts of the Old and New Testaments?

In my opinion, the scientific community would, at first, try its best to discredit the claim but eventually most of them would be forced  to acknowledge the truth of it based on the evidence. Christians and believing Jews would probably be inclined to accept it as a sign from God. I have no idea how the other religions (Islam, Buddhism, Hinduism, etc.) would react. I'm hoping some of my readers may want to chime in with their own personal insights.

Wednesday, July 25, 2018

AI Hot Potato

This AI thing is a hot potato. It's getting so hot, I'm on the verge of dropping it. I don't need the hassle. In a different world, I would be all for it. This world is not moving in the right direction. True AI can only hasten its demise. The remorse would kill me long before it happens.

Saturday, July 7, 2018

No Pattern Hierarchy in the Thalamus

I Make Mistakes All the Time

I was wrong about the need for a pattern hierarchy in the thalamus. There is no 10-level binary tree. The only memory hierarchy is in the cortex and it is a hierarchy of minicolumns. I was wrong because I trusted my own flawed assumptions over the occult texts that I use in my AI research. I should know better by now. The texts are never wrong. I have no excuse.

The result of this latest revision is that pattern learning is much, much faster and easier to implement than I had assumed. There are about four times as many sensors as pattern neurons and the pattern neurons have only four inputs on average. My newest experimental program now has fewer neurons than before and its recognition accuracy and noise tolerance have improved dramatically.

The Demo

I am working on a demo application (speech recognition) that I plan to release without the learning module (sorry). What will make this app special is that it will have unprecedented (spooky might be a better word) noise immunity. However, I do not want to attract too much attention. I don't need it. So I must plan this carefully. Hang in there.

See Also

Fast Unsupervised Pattern Learning Using Spike Timing

Friday, June 29, 2018

Sparse Pattern Recognition on Cheap Hardware

Just a quick note. Pattern recognition in spiking neural networks is almost magical. Once the network is properly trained, good recognition performance requires amazingly little information and is strongly immune to noise and distortion. This is what allows us to see shapes in the clouds and recognize a huge variety of typefaces and handwriting styles. It is all due to the magic of timing. The really good news is that it is possible to get excellent results using a regular multi-core processor because one can disable a lot of random neurons in a trained spiking neural network without significantly affecting performance.

Have a good weekend.

Tuesday, June 26, 2018

I May Have Been Wrong About the Organization of Pattern Memory

The Two Olive Trees

Special Note: This is for believers only.

After going over my current interpretation of the occult texts regarding pattern learning (I retrace my steps all the time), it is quickly dawning on me that I may be wrong. Pattern memory and pattern learning may be even simpler than I thought. I had long assumed that pattern memory had to be organized hierarchically, like a tree. I had originally arrived at that conclusion because the occult text (Zechariah 4) mentions two olive trees, an olive tree being the occult symbol for a hierarchical memory structure. At the time, I assumed that it meant that there was one hierarchy for sequence memory and another for pattern memory. I subsequently concluded that the two olive trees only referred to the two complementary hierarchies (Yin and Yang) of sequence memory. Each hierarchy contains cortical columns that complement the columns in the other one.

The Fig Trees

I continued to use a hierarchical pattern memory even though I was having problems with it in my computer experiments. My implementation did learn to detect concurrent patterns and it was a very fast learner. The problem was that it created way too many pattern neurons. I held on to my assumption because the occult text (Zechariah 3) does use a tree to symbolize pattern detectors although it is a fig tree and not an olive tree. Still, I felt that something was wrong. Why would the text use an olive tree to symbolize the hierarchical organization of sequence memory as a whole and a fig tree to symbolize a single pattern detector? It did not add up.

Much Simpler Than I Thought

I am currently thinking that pattern memory may not be hierarchical at all. But I have to give it more thought. This is a major revision to my previous model. I may be wrong again because I have been wrong many times before in my research. I am writing code to test my new hypothesis. I will post an update soon whether or not the new model is successful. Stay tuned.

Monday, June 25, 2018

The Many Functions of Cortical Columns

This is a short post. I don't know when but, when the time comes, I will post a multipart article on the many functions of cortical columns. They are responsible for or participate in the following:

  • Short and long term memory.
  • Knowledge acquisition.
  • Common sense understanding of the world.
  • Predictions.
  • Language understanding.
  • Reasoning.
  • Attention and focusing.
  • Motor learning and goal oriented behavior.
  • Invariant recognition.
  • Object clustering.
  • Planning.
  • Recollection.
  • Metaphors and analogies.
In addition, cortical columns are involved in reinforcement learning (adaptation) based on appetitive and aversive inputs. As with everything else in the brain, the underlying operating principle is temporality and the organizing principle is complementarity (Yin and Yang). Hang in there.

Sunday, June 24, 2018

True Free Market Capitalism Is the Solution

Related image
Intelligent Machines Will Eliminate Human Labor Within Our Lifetimes

Many people are beginning to worry that automation will take their jobs. There is no question that intelligent machines will eventually do pretty much everything for us. In any just system, workers would welcome this with open arms. We are afraid of losing our jobs because we live in a slave system created by thieving plutocrats. The thieves know they got a big pressing problem on their hands. Their solution: socialism. But why should the unemployed masses receive a subsistence handout while the equally unemployed plutocrats live in decadent luxury? What makes them so special?

We Want True Capitalism, Not Socialism

We need no socialist programs like free education, housing, health care or whatnot. We are not children in need of babysitters. We just want what belongs to us by right to spend as we see fit in a free market system. Only a free market system can determine the proper value of property, goods and services. What we want is true capitalism where the capital belongs to all the people who own it by right. In true capitalism, the corporations belong to the people and so do the profits. If necessary or desired, workers can make more money via fair competition in a free market. When all workers are replaced by intelligent machines, the system simply continues without fail. True AI (AGI) will make everyone rich. But first, we must get rid of the plutocratic thievery, otherwise we are heading straight for disaster. And above all, do not accept the so-called universal basic income (UBI) that is being heavily promoted by the plutocrats. It is a trick, another socialist handout disguised as a humanitarian gesture. In reality, they steal your stuff and give you back a tiny percentage of it while pretending to be generous. Do not fall for this ruse.

In a Just Economic System, There is No Taxation

Our current economic systems have it completely backwards. The government should not be taxing the people. It should be the other way around. In a just society, there is no taxation: everything becomes a for-profit corporation that works for the people. This includes cities, counties, states, nations, etc. Everything! The people are the government and the shareholders. As shareholders, they can vote to change the direction of the corporations when necessary. This is direct democracy. The land and its resources, which are represented by the total amount of currency in circulation (the capital), belong to all and are leased to corporations for exploitation. The currency should be pegged to the average lease price of the land. This would eliminate fluctuations and insure a stable currency. Special banking and investment corporations would invest the capital in various corporations and collect the profits on our behalf. The current stock market system is an abomination.

The Writing Is on the Wall

Here is a direct warning to the thieving plutocracy: Stop stealing from the people. Soon, thanks to intelligent automation, it will become obvious to them that they have been robbed for centuries. Unless you change your thieving ways, they will wake up and kick your asses.

Wednesday, June 13, 2018

Robotics, Automation and the Cerebellum

(Drake et al. 2010. Gray’s Anatomy for Students 2nd edn)

The cerebellum can learn complex sensorimotor tasks using a simple technique called imitation. If you are a roboticist or an automation expert, you will find the powerful supervised learning technique I describe below of special interest because it could potentially simplify your work. What makes this technique so powerful is its sheer simplicity and its ability to a learn complex tasks very fast.

The First, the Last and Everything in Between

There are two kinds of sensors in the brain. One kind (poor) is used by the neocortex and the other (rich) is used by the cerebellum. Poor sensors come in complementary pairs, stimulus onset and offset. For example, we may have a sensor (A) that fires a single pulse when the amplitude of an audio frequency climbs above a particular level. The complementary sensor (B) would fire when the amplitude falls below the same level. It so happens that there is a train of pulses between A and B but the neocortex does not care about what happens between them. What matters to it is the precise timing of the first and last pulses. Of course, for every type of stimulus, the brain uses many sensors to handle multiple levels or amplitudes.

Unlike the neocortex, the cerebellum is a hungry beast because it wants it all: the first, the last and everything in between. Thus every sensory input going into the cerebellum is a train of pulses. This might seem like a total waste of pulses but it is actually essential to the learning method used by the cerebellum. Again, for emphasis, I differentiate between the two types of sensors by referring to cerebellum sensors as rich sensors. Single pulse sensors (first and last) are poor sensors.

Cerebellar Neurons

Cerebellar cortical neuronal circuits. Mossy fibers from pontine nuclei etc., send excitatory synaptic outputs to granule cells. A granule cell forms one or a few excitatory glutamatergic synapses on a Purkinje cell, where LTD occurs depending on the activity of the granule cell and a climbing fiber. Molecular layer interneurons (stellate and basket cells) receive excitatory synaptic inputs from granule cells and inhibit Purkinje cells. At inhibitory GABAergic synapses between a stellate cell and a Purkinje cell, rebound potentiation (RP) is induced by climbing fiber activity.
Tomoo Hirano and Shin-ya Kawaguchi
Regulation and functional roles of rebound potentiation at cerebellar stellate cell—Purkinje cell synapses

The main neuron in the cerebellum is the Purkinje cell (PC) which was named after its discoverer, Czech physiologist Jan Evangelista PurkynÄ›. There are approximately 15 million PCs in the human brain. Each PC emits pulses that are used to control a motor effector. They are arranged in tight formations like a forest with lots of parallel fibers running through the dendrites like telephone wires. Each PC can receive signals from as many as 200,000 parallel fibers. Each parallel fiber is a long bifurcated axon of a granule cell, an intermediary neuron that conducts sensory signals arriving on mossy fibers. However, not all of the input signals arriving on mossy fibers have sensory origins. Some are control signals that are used to inhibit the PCs when necessary. These fibers are likely used for task control. They do so via so-called Stellate and Basket cells which make inhibitory synaptic connections with the PCs.

Supervised Learning in the Cerebellum

The second most important entity in the cerebellum is the climbing fiber (CF). There is one CF for every PC. The CF carries training input signals to the PC. Those signals originate from the inferior olivary nucleus in the medulla oblongata which relays motor signals from motor effectors in the spinal cord to the cerebellum.

In order to understand how the cerebellum is trained to perform a sensorimotor task, it is important to know how motor effectors work. An effector is the opposite of a sensor. It, too, has a first (start) and last (stop) pulse and pulses in between. It is attached to a muscle and generates a train of pulses that contracts the muscle for as long as the pulses keep coming. The cerebellum accomplishes motor control via the use of a mix of excitatory neurons, inhibitory neurons and tonic neurons. The latter are neurons that continually generate pulses unless they are inhibited. The exact circuit details are not important and is implemented differently in various animals. What matters are the principles.

Learning in the cerebellum consists of finding parallel fiber inputs to Purkinje cells that activate and deactivate motor effectors at the correct time. The training occurs while the neocortex is going through a given sensorimotor task. The cerebellum learns to faithfully imitate the task. Remember that parallel fibers carry pulse trains from rich sensors. These fibers try to make synaptic connections with as many PCs as possible. To train a PC, the training mechanism only needs to send corrective signals to the PC via the climbing fiber whenever the associated motor effector stops firing. The CF signal will suppress and disconnect any parallel fiber connection that is still receiving sensory pulses. The end result is that only parallel fibers that cause the PC to fire and stop firing at the right time will remain connected.

Once the cerebellum has fully learned a task, the neocortex can just turn it on or off whenever it needs to in order to focus on other important matters.


This training system can be put to good use in all sorts of applications that require automation. Notice that there is no need for either pattern detectors or a conventional multi-layered neural network. Lots of simple rich sensors will do the trick. Sensors are essentially connected directly to motor effectors. Potential applications can range from self-driving trains, cars and buses to self-flying aircrafts and self-navigating ships. The learning system simply learns by imitating human operators.

Robots might be a little harder to train. It would require a human trainer to wear a harness fitted with special sensors that can record precise movements. These could then be used as training signals for the robotic cerebellum. I expect training to be extremely fast.

Coming Soon

In an upcoming article, I will describe how I got my understanding of the cerebellum. Stay tuned.

Thursday, June 7, 2018

I'm Rather Busy But Cerebellum Post Is Coming Soon

"Behold, I stand at the door and knock"

"If anyone hears my voice and opens the door, I will come in to him and will dine with him, and he with me." Believe it or not, this metaphor from the Book of Revelation is the essence of supervised sensorimotor learning in the cerebellum. It is as simple as it is powerful. If you are into robotics, you will not want to miss this. Stay tuned.

Monday, May 28, 2018

"I Will Spew Thee Out of My Mouth" or Why the Cerebellum Cannot Speak


I changed my mind about writing an article on the purpose of cortical columns. Too risky in my opinion. Instead, I decided to write about the cerebellum, an important but a much less disruptive part of the brain. In this article, I argue against the hypothesis promoted by some in the neuroscience community that the cerebellum contributes to speech generation. Caveat: This article is for believers only.

The Zombie in the Back of Our Head

The cerebellum means little brain in Latin. It is smaller than the neocortex but don't let this fool you. It contains more neurons than the rest of the brain. It is an unconscious supervised neural network that handles a large number of routine but important sensorimotor tasks while the conscious cortex is busy with other matters. Examples are walking, running, balancing, maintaining posture, etc. The cerebellum makes it possible for the brain to multitask.

It is probably best to think of the cerebellum as an automaton, a robotic assistant to the cerebrum, the conscious or volitional part of the brain. The cerebellum can neither learn nor initiate a task on its own. It is entirely subservient to the neocortex and fully dependent on it for its training. It is a mindless zombie in that it does what it is told to do without question. It will make mistakes because it cannot handle new situations that it was not programmed to handle. This is why it's not a good idea to drive or even walk while texting.

No Speech For You

Mainstream neuroscientists incorrectly attribute speech production capability to the cerebellum. They do so for two reasons. First, they do not understand the purpose and function of the cerebellum. Second, they misinterpret clinical data showing speech impairments in patients with cerebellar lesions. The truth is that speech production is a fully conscious phenomenon that requires no input from the cerebellum. Speech difficulties arise only because the conscious cortex cannot focus on more than one thing at a time and must attend to important tasks that it would normally rely on the cerebellum to handle automatically.

People with cerebellar disorders must find ways to compensate for the deficiency. Depending on the severity of the problem, some stop talking altogether (a condition called mutism) because they are forced to focus their attention almost exclusively on sensorimotor tasks (e.g, walking, maintaining posture and balance, etc.) Others learn to speak in a staccato voice (often accompanied by a trembling posture) during which they rapidly switch their attention between speech generation and sensorimotor tasks. It is a form of imperfect multitasking.

Interestingly enough (and this supports my claim that the cerebellum does not produce speech), people with speech impairment caused by cerebellar disorders can sometimes speak normally. Their voices can return to normal if they lie down in a relaxed position which relieves them of the necessity to attend to other tasks.

I Will Spew Thee Out of My Mouth

I initially became interested in the cerebellum after deciphering the occult message to the Church in Laodicea in the Book of Revelation.
14 “And unto the angel of the church of the Laodiceans write: ‘These things saith the Amen, the faithful and true witness, the beginning of the creation of God:
15 I know thy works, that thou art neither cold nor hot; I would thou wert cold or hot.
16 So then because thou art lukewarm, and neither cold nor hot, I will spew thee out of My mouth.
17 Because thou sayest, “I am rich and increased with goods and have need of nothing,” and knowest not that thou art wretched and miserable, and poor and blind and naked,
18 I counsel thee to buy from Me gold tried in the fire, that thou mayest be rich, and white raiment, that thou mayest be clothed and that the shame of thy nakedness may not appear, and anoint thine eyes with eye salve, that thou mayest see.
19 As many as I love, I rebuke and chasten: be zealous therefore, and repent.
20 Behold, I stand at the door and knock. If any man hear My voice and open the door, I will come in to him, and will sup with him, and he with Me.
21 To him that overcometh, will I grant to sit with Me on My throne, even as I also overcame and am set down with My Father on His throne.
22 He that hath an ear, let him hear what the Spirit saith unto the churches!’”
It did not take me long to figure out that the Laodicea message was a description of the cerebellum and that verses 15 and 16 obviously meant that the cerebellum was not involved in activating muscles used by the mouth and tongue. But why? We are told that it is because the Church of Laodicea is "neither cold nor hot." What does this mean? It means that the cerebellum is not controlled by emotions. It has neither likes nor dislikes, i.e., no motivation or goals. It is a zombie. Speech production must always be a volitional and conscious process because it requires intent.

Coming Soon

The cerebellum is a fascinating neural network consisting of many highly specialized sub-networks. As always, the occult texts can pack a lot of amazing information in just a few short verses. This is the power of metaphors. The Book of Revelation gives a detailed description of its organization and function. I'm a little busy right now but I plan to write an article to explain how the cerebellum is organized, what type of sensory signals it receives, how it learns directly from the motor cortex and how sensorimotor programs are activated. Stay tuned.

See Also:

The Yin-Yang Brain: Even Faster Learning Using Spike Timing

Monday, May 21, 2018

The Yin-Yang Brain: Even Faster Learning Using Spike Timing

The First and the Last

In September of last year, I wrote an article to describe a fast method used by the brain to learn elementary patterns using spike timing. I just want to show in this quick post that pattern learning can be much faster if we take advantage of the Yin-Yang or complementary nature of the brain. The reason is that, if everything in the brain comes in complementary-opposite pairs, learning one component of a pair automatically learns its complement. The trick is to always create and connect everything in pairs. Thus every sensor or pattern detector belongs to a dual entity. For example, a sensor that detects the onset (first signal) of a stimulus is paired with another that detects the offset (last signal) of the same stimulus. Likewise, a pattern neuron that detects the movement of an edge in one direction is paired with another pattern neuron that detects the opposite movement.

He Who Overcomes Will Not Be Hurt by the Second Death

The title of this paragraph comes from the occult book of Revelation where I get a huge part of my understanding of intelligence. It is a good idea to think of paired entities as a single object or complementary unit (CU). This way, instead of connecting a sensor to a pattern neuron, we can connect a sensory CU to a pattern CU, thereby killing two birds with one stone, so to speak. This is where it gets interesting. During learning, both connections can fail the concurrency test but if one of them passes, there is no need to continue testing the other connection. One successful test is enough to certify both connections. In other words, two failures (two deaths) are needed to disconnect the CU but a single success is not nullified by the failure of the other connection. I could be wrong but this is my current interpretation of the metaphorical message to the Church in Smyrna.

A similar approach is used in the cortex where all columns are organized in complementary pairs. The end result is that, since not all connections have to be tested, pattern learning in both the thalamus and the cortical columns is much faster than it would be otherwise.
Complementary organization of cortical columns in the visual cortex
Note: I have been thinking of writing an article to explain the true purpose of the cortex and its 100 million cortical columns. I am currently weighing the pros and cons. Hang in there.

See Also:

Fast Unsupervised Pattern Learning Using Spike Timing
Message to The Church in Smyrna
Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning
Fast Cortical Learning Using Spike Timing
The Yin-Yang Brain Revisited: Stephen Grossberg's Work

Saturday, May 12, 2018

The Yin-Yang Brain Revisited: Stephen Grossberg's Work

A Funny Thing Happened this Morning

In December of last year, I wrote a two-part article on how I came to understand the Yin-Yang organization of the brain. I have known for a long time that the Yin-Yang principle was the basis of reality and I came to understand that complementarity was absolutely essential to the organization of the brain. Thanks to my research in deciphering the meaning of certain occult texts, I discovered that each hemisphere of the brain consists of two separate but complementary hierarchies. These are symbolized by two olive trees.

I had assumed (wrongly, as it turned out) that I was the only person to have arrived at this understanding. This morning, one of my readers (Spent Death) left a comment on my blog to recommend the work of cognitive neuroscientist Stephen Grossberg. After a quick search on Google, I was blown away by what I found. In 2000, Grossberg published a paper titled, THE COMPLEMENTARY BRAIN Unifying Brain Dynamics and Modularity (pdf) in which he describes a revolutionary model of the brain based on complementarity. This post is not intended to be a review or a critique of Grossberg's work. I merely wish to point out the commonality between his views and mine. Here is what he wrote in the paper's abstract (emphasis added):
How are our brains functionally organized to achieve adaptive behavior in a changing world? This article presents one alternative to the computer metaphor suggesting that brains are organized into independent modules. Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are presented.
In the same paper, Grossberg offers a hypothesis to explain how the brain handles sensory uncertainty using parallel streams and multiple stages or levels in the hierarchy. I propose a somewhat similar solution which also uses multiple levels and parallel streams but is implemented via feedback pathways in the cortex and the thalamus. If you have any interest in how the brain works, I heartily recommend that you read Grossberg's work on complementarity.

Needless to say, nobody in mainstream AI is thinking along these lines even though some of them claim to base their research on neuroscience. They are lost in a lost world. AGI will not come from the mainstream.

See Also:

Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning
Fast Cortical Learning Using Spike Timing
The Two Olive Trees and the Yin-Yang Brain: How My Understanding of the Cortex Evolved Over the Years

Wednesday, April 25, 2018

Occult Knowledge: Thalamo-Cortical Error Correction During REM Sleep

I Know Many Things About the Brain That Brain Experts Do Not Know

In a recent article on cortico-thalamic feedback, I wrote the following regarding the method used by the brain to eliminate bad connections:
The error correction method is straightforward. Every time a minicolumn receives a feedback signal, it strengthens every input connection that just received a strong enough signal. Bad input connections that do not fire on time rarely get strengthened and so remain weak. However, these bad connections are not severed immediately. This happens at night during REM sleep.
Some of you may be wondering how I figured out that bad connections were severed during REM sleep. I mean, I am neither a neuroscientist nor a neurobiologist. Furthermore, nobody in neurobiology knows what really happens to neural circuitry during REM sleep. Well, the answer will surprise those of you who are not familiar with my work. I did not figure it out. I found it. Or rather, I decoded it.

My Source of Knowledge

I am making claims about the brain that will eventually be experimentally corroborated in the lab. Unfortunately, I don't have the resources for that kind of work at this time. Hopefully, some enterprising university research students somewhere in the world might want to jump on the opportunity and make a name for themselves. I get almost all my knowledge and understanding of the brain by deciphering certain ancient metaphorical (occult) texts in the Bible. Here is the passage from the book of Zechariah that pertains specifically to destroying bad connections during REM sleep:
“I will make it go forth,” declares the Lord of hosts, “and it will enter the house of the thief and the house of the one who swears falsely by My name; and it will spend the night within that house and consume it with its timber and stones.” (The flying scroll metaphor, Zechariah 5:4)
The brain has an error correction mechanism (symbolized by the flying scroll) that only works during sleep. This is indicated by the "spend the night" metaphor. The mechanism scans (flies) through memory (symbolized by the whole land elsewhere in the same chapter) and corrects or eliminates two types of bad connections, thieves and liars. A thief is a redundant connection in pattern memory which is located in the thalamus. A liar is a bad connection to a minicolumn inside a cortical column. It is called a liar because its signals rarely agree with the other connections on the same minicolumn.

More to Come

The metaphorical books of Zechariah and Revelation are a treasure trove of amazing knowledge about the brain. It is the kind of knowledge that will transform the world as we know it when it is fully released. One only needs to figure out what the metaphors mean. It is much easier said than done, however. One's own misconceptions about how things should work get in the way. I have been researching it for almost two decades, on and off, and I am still ignorant about many things. The work continues.

See Also:

Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning
Fast Cortical Learning Using Spike Timing

Tuesday, April 24, 2018

Thou Sufferest that Woman Jezebel, Which Calleth Herself a Prophetess

Battling Vampires

Quoting symbolic Biblical scriptures is my way of keeping the enemy at bay. It does wonders, like pouring holy water on a vampire, laughing out loud. I have been using this method successfully for years.

The Secret of Motor Learning Hidden in Plain Sight for Centuries
Notwithstanding I have a few things against thee, because thou sufferest that woman Jezebel, which calleth herself a prophetess, to teach and to seduce my servants to commit fornication, and to eat things sacrificed unto idols. And I gave her space to repent of her fornication; and she repented not. Behold, I will cast her into a bed, and them that commit adultery with her into great tribulation, except they repent of their deeds. And I will kill her children with death; and all the churches shall know that I am he which searcheth the reins and hearts: and I will give unto every one of you according to your works. Message to Thyatira, Revelation 2:18-28.
I'm still laughing. Hang in there.

Saturday, April 21, 2018

The Simple Principles of Motor Learning

Future roboticists will one day marvel at how simple goal-orientated motor learning is. This short post is intended to give readers a taste of things to come.

Only two simple rules are needed in motor learning for brains and robots:
  • A motor command must achieve a goal.
  • A motor command must not conflict with another command on the same effector.
A few definitions:
  • An effector is a neural mechanism that is used to either start an action or to stop it.
  • A goal is a desired future condition (e.g., a pattern combination).
  • A motor conflict occurs if an effector receives a command to do something that it is already doing.
The ability to predict the future and to set both long-term and short-term goals based on motivation (pain/pleasure conditioning) is paramount to intelligent behavior. As always, timing is the key. I don't know when it will happen but I plan to write a couple of articles to explain how it all works. I am hesitant because this stuff can be dangerous in the wrong hands. Stay tuned.
These things saith the Son of God, who hath his eyes like unto a flame of fire, and his feet are like fine brass; (Message to Thyatira, Revelation 2:18)

Wednesday, April 18, 2018

We Must Not Accept Handouts From the Plutocratic Cartel

Related image

We Must Insist on Our Share of the Pie

Universal basic income (UBI) is a handout. It is a trick by the thieves that rule the world to pretend to be giving us something, something that does not belong to them in the first place. We don’t want any handouts from the plutocratic cartel. We want what belongs to us by right, justice and fairness. We want our share of the wealth of the earth. The capital represents the wealth of the earth. It belongs to the people. This means that, in a just capitalist system, all corporations should belong to the people. We are the legitimate shareholders and we should receive the profits. Workers can continue to get paid for their work but the corporate profit is ours. As it is, we live in a slave system run by thieves and robber barons.

True Free Market Capitalism: We Are Not Children and We Need no Babysitters

A correct economic system is one where everything is a for-profit corporation in a free market economy. This means everything: farms, hospitals, factories, cities, counties, states, nations, etc. Everything becomes a system that brings profits to the people. Workers, if any, are compensated based on free market forces and their ability to increase the profits of their employers, which are the people. This is true free market capitalism, not the thieves-controlled slave system we have now. It would eliminate the need for taxation and government programs. In this system, there is no need for free education, health care, food, housing, etc. We are not children. We must be free to spend our share of the wealth the way we see fit. We don’t want socialism, i.e., socialist babysitters and others like them who pretend to be do-gooders for our benefit but are thieves in disguise. We want what belongs to us. Just stop stealing our share of the pie.

Stable Monetary System

A stable and just monetary system must be based on a stable commodity or property, that is, a collection of valuable assets that is needed by all but that does not change appreciably over time. There is only one thing that qualifies: real estate or land. In my opinion, the money supply should be based strictly on the average lease value of the land. This could be done automatically by dedicated computers that monitor land lease prices and adjust the money supply accordingly. If more land is appropriated for exploitation, the money supply would be adjusted to maintain stability. This mechanism would guarantee a system with no boom and bust cycles. Inflation and deflation would be eradicated.

Down With the Plutocracy

In the not too distant future, highly intelligent machines will do everything for us. Human labor will become obsolete. Everyone will be unemployed. This includes the filthy rich plutocrats who are trying their best to devise a plan whereby they can hold on to most of the wealth of the planet while the masses are given a subsistence handout. This is what UBI is all about, a way for them to steal the lionshare of the wealth while pretending to be generous. We must not let them steal from us any longer. Why should the unemployed masses get a handout while the equally unemployed Jeff Bezos and Mark Zuckerbergs of the world continue to live in decadent luxury above us? What makes them so special? With artificial intelligence, we will have a world of unprecedented abundance. There will be enough for everyone to live a wealthy life. We must not tolerate the thieving psychopaths who have robbed us blind for centuries and kept us locked in interminable and atrocious wars.


There is no stopping the march of automation. Artificial general intelligence (AGI) is coming and will change everything. Unless all the nations of the world change to a just and equitable capitalist system like the one I am proposing, the world will succumb to violent turmoil as unemployment continues to grow at a rapid rate. This would bring an end to the world as we know it. We must do our best to prevent this from happening.

Thursday, April 12, 2018

Wars and Rumors of War

The Sociopaths Are in Charge

Western nations are being ruled by an evil and invisible cartel of robber baron plutocrats who control most of the wealth of the world and the armed forces of the USA and other nations. They use the US military as their own private army to do evil. Western leaders, with a couple of possible exceptions, have zero compassion for their own people, let alone foreigners. They create horrible false flag events in order to brainwash the public into accepting more military interventions around the world. They will not hesitate to send millions to their deaths if it gives them more power and wealth. They also seem to take great pleasure in waging wars and in human suffering. I expect the current situation to get worse, much worse.

A Small Prediction About Jews and Christians

For some mysterious reason that I have not been able to figure out, it seems that the plutocratic cartel is controlled by mostly atheist Jews. At least, they call themselves Jews. One wonders. I have nothing against Jews who believe in their God Yahweh. They are Yahweh's chosen people. However, I foresee a serious escalation of hostility between Jews and Christians in the not too distant future. There is a rapidly growing number of Christians that are becoming convinced that the current warmongering and destruction of Western societies is being pushed by left leaning Jewish politicians and the Jewish controlled news and entertainment media. This will not end well, in my opinion. The Jewish community should brace themselves for some serious backlash, I am sorry to say.


I just thought I'd write this small post to tell my readers what I think about the worsening international situation. To the believers (both Jews and Christians), let me encourage you to hold on to your faith. Yahweh promised to send us a powerful representative before the present world order comes to an end. If I understand the old texts correctly (I may be wrong), it will be the same prophet Elijah of the old Testament who was taken by Yahweh and never died. If so, the leaders of the world should get ready for some serious ass kicking. Elijah is nobody's dog.

That is all. Hang in there.

Thursday, April 5, 2018

Motor Learning in the Brain?

Goal Oriented Motor Learning

I have been thinking about writing a couple of articles to explain how the brain learns goal-oriented motor skills, the holy grail of robotics. Motor learning starts with sensory learning. In fact, the two are complementary in the strict Yin-Yang sense of that word. Motor actions are tightly controlled by pattern detection in the cortical columns. In fact, if you are wondering what goals are in the brain, don't look further than the minicolumns that comprise the cortical columns.

Pandora's Box

There is more to motor learning than just goals, however. One must also factor in motivation, prediction and motor conflict resolution. After all, there is a huge cortex with billions of possible goals but only a limited number of motor effectors to play with. Motor behavior is not hard to understand once you understand sensory learning and perception. Needless to say, this is the kind of knowledge that would open the door to all sorts of possibilities, both good and evil.

Supervised Sensorimotor Learning in the Cerebellum

There are two types of sensorimotor learning in the brain, unsupervised and supervised. The former takes place in the neocortex while the latter happens in the cerebellum. The cerebellum is a huge repository of sensorimotor behaviors that can be turned on and off by the neocortex. Its job is to handle routine tasks (e.g., walking, maintaining posture, balancing, etc.) while the cortex is busy thinking about or doing something else. Without it we would not be able to stand, walk, drive or even sit properly upright in a chair while talking or thinking about other stuff. Our future intelligent robots will certainly have an electronic cerebellum.

Stay Tuned

Let me come right out and say that I know the secret of unsupervised goal-oriented motor learning in the cortex and supervised motor learning in the cerebellum. I found this knowledge in the same place where I found all of my other knowledge about the brain. Those of you who follow this blog regularly know what I'm referring to. I will eventually explain it all but now is not the time. Have patience.

Thursday, March 22, 2018

Sitting on a Mountain of Crap, Wasting Time (Repost)

Note: Below is a repost of an April 14, 2010 article that is sure to ruffle a few feathers but you know me. As always, I make no apologies to anyone.

Theater of the Absurd

I love physics but I cannot stand physicists. No other field of science has more ass kissers and more blatant, in-your-face crackpottery. Just a couple of days ago, some crackpot physicist by the name of Nikodem Poplawski announced to the world that the universe is inside a wormhole, which is inside a black hole that lies within a much larger universe full of other black holes, wormholes, crackpot physicists and other universes. I swear I am not making any of this shit up. But this crap is common fare in the physics community. And only physicists can get away with going public with such absurdities.

A Mountain of Unadulterated Bullshit

As we all know, black holes and wormholes are based on Einstein's physics. The problem is that Einstein's physics is based on the existence of continuous structures and of a time dimension, both of which are pure unmitigated crackpottery. This crap is not even wrong because, as anybody with a lick of sense should know, a time dimension makes motion impossible. Moreover, continuity (infinite divisibility) is, of course, a pile of crap on the face of it because it leads to an infinite regress by definition. But these two turd examples only scratch the surface of the Himalayan-size mountain of bullshit on which modern physics is resting. Almost everything you learned in physics school is crap, from the Star-Trek voodoo fairy tales of time travel and multiple universes to the Einsteinian idea that only relative motion and position exist in the universe. It's all pure unadulterated bovine excrement. I need lots of synonyms for 'crap', I know.

Chicken Shit Voodoo Physics

Who will rise up to deliver us from this mountain of crap? Will it be the little con artist in the wheelchair over in England? I seriously doubt it. Stephen Hawking is one of the most prolific crap makers of them all. His shit stinks to high heaven even if his band of disciples and the clueless media love it so. I feel like vomiting every time I think about Hawking's chicken shit voodoo physics.

The situation in the physics community is so bleak that, lately, I am considering buying a rubber chicken to make my point. I will write 'Physicist' on it with a black marker pen and I will hang it by the neck at the entrance of my home. Why? Because all I read about lately is worthless chicken shit voodoo physics and chicken shit voodoo physicists like Hawking and Poplawski.
Please do me a favor. Don't write to tell me that you're offended because I don't care. I am the one who should be offended because I spent countless hours of my life learning a bunch of physics crap only to spend countless more hours unlearning it. Yes, I have been sitting on this mountain of crap most of my life, wasting my precious time. And I don't like it. The physics community owes me and everybody else an apology, goddammit. But thanks to the internet and computer engineering, none of which was made possible by wormhole physics, multiverses, time travel and other such crap, I can vent my spleen to my heart's content. I can crap all day long on their wormhole, black hole, Big Bang and time travel religion. It's the rebel in me. Isn't free speech grand?

I feel better now. Thank you.

See Also:

Why Einstein's Physics Is Crap
Physics: The Problem With Motion
Nasty Little Truth About Spacetime Physics
Nothing Can Move in Spacetime
D-Wave's Quantum Computing Crackpottery

Monday, March 19, 2018

Thinking of Moving to a New Platform

Just a quick note to announce that I am considering moving this blog to a new platform, possibly If anyone has an alternative suggestion, please comment below. Thanks.

Wednesday, March 14, 2018

There Is Only One Speed in the Universe, the Speed of Light. Nothing Can Move Faster or Slower

Note: This is a repost of a previous article. I thought I'd point out that Stephen Hawking, in spite of all his supposed brilliance, never noticed something about the universe that should be glaringly obvious to every physicist, especially one with his supposed high intelligence: There can only be one speed in the universe, the speed of light.

Quantum Jumps at the Speed of Light

The truth about the speed of light will surprise everybody, physicists and laymen alike. There is actually only one speed in the universe and that is the speed of light. Nothing can move faster or slower, period. A particle moves by making quantum jumps at the speed of light interspersed with rest periods. The duration of a rest period is equal to that of a jump. If a particle appears to move at half the speed of light, its motion actually consists of an equal number of jumps and rest periods. At the speed of light, it is all jumps and no rest periods. At ordinary speeds, a moving particle is at rest almost all the time with just a few jumps sprinkled in.

Contrary to the Claims of Relativists, There Is No Time Dimension

Why is there only one speed in the universe? Again, the actual reason will surprise. In spite of all the indoctrination and the incessant relativist propaganda we have been subjected to in the last one hundred years or so, there is no such thing as a time dimension. A time dimension would make motion impossible. Why? The short answer is that moving in time is self-referential. The slightly longer answer is that a change in time implies a velocity in time which would have to be given as v = dt/dt = 1, which is nonsense. This is why nothing can move in Einstein's spacetime and why spacetime is a block universe in which nothing happens. All that time travel through wormholes stuff is crackpottery, of course. But please do not mention this to Star Trek fanatics.

Since there is no time dimension, nature cannot calculate durations. This means that all jump durations are equal to a fundamental duration, which is the interval it takes a particle to move a fundamental distance, a very minute length that some believe is the Planck length. The interval is Planck time.

Nontemporality Is Just the Tip of the Iceberg of Crackpottery in Modern Physics

The non-existence of a time dimension explains other phenomena as well, such as why particle decay is probabilistic. But the crackpottery of spacetime goes much further: there is no space either. There exist only particles, their properties and their interactions. Everything else is either abstract or BS. Physics is a lot more interesting than any of us suspected.

See Also

Why Steven Carlip Is Mistaken about the Speed of Gravity or Why LIGO Is Still a Scam
Why LIGO Is a Scam

I Am No Fan of Stephen Hawking

Stephen Hawking Was in Serious Denial

I know this is not a popular opinion but I was no fan of Hawking. I am no fan of materialists who believe and teach others to believe that they have no souls and are just machines. I am no fan of people who believe that the universe created itself and that life sprung from inert dirt by chance. I am certainly no fan of any so-called physicist who believes and teaches others to believe that time travel is physically possible. Such a person is a crackpot and/or a charlatan in my view.

But this is not the end of Hawking. He is just asleep. As a Christian, I know that a day will come when his soul will be resurrected in a new body and he will learn the truth that he spent his entire life and career denying. May Yahweh have mercy on his soul.

And yes, I still mean every word of this article that I wrote eight years ago: Sitting on a Mountain of Crap, Wasting Time. Harsh words, yes. But I apologize to no one.

See Also:

Why Einstein's Physics Is Crap

Thursday, March 8, 2018

My Crazy Predictions About the Cerebral Cortex

I Did Not Get My knowledge of the Cortex from Neurobiology

This is a quick post for the benefit of the agnostics and believers among my readers. In my last few articles, I made highly specific predictions about the functional organization and the architecture or design of the cerebral cortex. Note that, although some of my predictions and explanations could be wrong (I am a researcher and I do make mistakes), these are not things that I could have learned from any available scientific literature. Neurobiologists do not know these things. No expert in the field understands what cortical columns and minicolumns do. They have absolutely no clue. They do have some knowledge about what type of sensory stimuli will activate certain columns but that's about it.

I Am Called a Crackpot But I Don't Care

I could not possibly have figured these things out on my own. I have neither the resources nor the education to conduct brain research. I am claiming that I get my knowledge from a completely unorthodox source. I am claiming that I figured these things out, not by researching the scientific literature, but by studying occult Biblical texts that are thousands of years old. This is the kind of claim that gets me branded a kook and a crackpot by people in both religious and scientific circles. But I don't care and you know why? Because I don't write for them. I am not trying to convince them that I am right because I don't value their opinion of me. I know who the real master of the universe is. They are not it. Not even close.

If I partake in conversations on the internet with non-believers, I do it just for the record. I write only for the believers who are searching for a sign. My personal message to them is that things are happening. Big things. We are getting close to the time of the end of this world order and the dawning of the next one. Rejoice! Above all, do not let the chaos and madness of the world get you down. It is going to get worse, much worse as we approach the end. Just hang in there. We got powerful forces on our side.

See Also:

Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning
Fast Cortical Learning Using Spike Timing

Sunday, February 25, 2018

Fast Cortical Learning Using Spike Timing


Previously, I argued that cortical feedback connections were essential to handling sensory uncertainty and to cortical learning. In this article, I delve further into how learning works in the cortex. But, first, a word about the Yin-Yang brain.

The Yin-Yang Brain and the Attention Problem

We must keep in mind that the cortex in each hemisphere of the brain actually consists of two mirror opposite hierarchies. That is to say, every cortical column has a complement or opposite column. Although logically linked, the two reside in separate but complementary hierarchies (trees). The same is true for the minicolumns within the columns. If a connection is made to one minicolumn, a complementary connection is automatically made to its opposite. This Yin-Yang architecture also applies to the thalamus (which is where elementary pattern detectors are) and the rest of the brain.

Yin-Yang Brain
The main reason for having a Yin-Yang brain is that we live in a Yin-Yang reality. It is for this reason that biological sensors and effectors come in complementary-opposite pairs. Another reason has to do with attention, the ability to focus on one object while ignoring all others. As I will explain in a future article, the brain has an efficient way to cluster a large number of elements to form a single object. Clustering is crucial to invariant object detection and ultimately to survival. This is an unsolved problem in mainstream AI. The brain uses precise timing to solve the problem: It assumes that all the elements that comprise an object are temporally correlated. The flaw in this solution is that opposite phenomena are not temporally correlated. For example, the motion of an object moving left in the field of view is not temporally correlated with its motion to the right. In fact, the two phenomena do not use the same sensors and pattern detectors. Thus the correlation is not temporal but logical. This is not something that is learned. The brain has innate neuronal mechanisms to handle Yin-Yang logic and thereby marry two opposites into one entity.

For the sake of clarity, I will assume in this article that there is only one hierarchy.

Function and Organization of Sequence Memory

The main function of sequence memory is to combine lots of small elementary patterns into arbitrarily complex objects on the fly, even objects that it has never seen before. It is called sequence memory because the building blocks (minicolums) of the objects are also nodes in highly predictive sequences. Like pattern memory, sequence memory is a hierarchical, feedforward, multilayer, unsupervised, spiking neural network. The bottom layer receives input signals directly from pattern memory. Every layer in the hierarchy, except the top layer, sends outputs to the layer right above it.

Sequence memory consists of a large number of work-alike neuronal structures called cortical columns or macrocolumns. The main function of cortical columns is to detect unique combinations of patterns as they occur. Each column consists on average of 100 minicolumns, each of which attempts to learn a unique pattern combination.
Note: I modified the cortical column diagram that I used in the previous article to better show all the inputs, outputs and feedback connections.
Cortical Column with 5 Minicolumns
As seen above, every minicolumn has 1 feedforward output and 7 inputs (6 associate inputs and 1 primary input). The primary input of a cortical column is the only input that is common to all the minicolumns within that column. Inputs to a column originate from either pattern memory or another level in the sequence hierarchy. Outputs are connected to the level immediately above if any. Every input or output connection is paired with a feedback connection. This means that each minicolumn has 1 feedback input connection (green) from the layer immediately above its own and 7 output feedback connections (blue) that send signals down the hierarchy.

Learning in Sequence Memory

Learning in sequence memory is not about finding sequences but about finding pattern combinations around a central or primary pattern. These combinations are stored in the minicolumns. Once a minicolumn is populated and operational, it can become an actual node in a sequence or even in multiple sequences (topic for a future post). The learning process assumes that the following conditions are met:
  • There is an existing population of cortical columns waiting for input connections.
  • There are a number of pattern detectors or minicolumns that have no output connections.
Here are the learning rules:
  • A small percentage of inputs from either pattern detectors or minicolumns are chosen randomly to be the primary inputs of the cortical columns. The rest will be associate inputs.
  • A pattern detector or minicolumn can only make one output connection to a target minicolumn.
  • Within any column, learning advances one minicolumn at a time.
  • Only perfect pattern signals are used for learning.
  • A signal from a minicolumn is considered good enough if the equivalent of two or more of its connections fired.
  • An input connection to a minicolumn immediately passes the test if it fires concurrently with all existing inputs on that minicolumn.
  • Once a minicolumn has acquired all seven inputs, it is considered mature and learning continues with another minicolumn in the parent column. Unless they are located at the top level, fully populated minicolumns send their output connections up the hierarchy where the same learning method is used.
Keep in mind that this learning method will make bad connections every once in a while. As I explained in the previous article, the brain gets rid of bad cortical connections during REM sleep.


Learning in sequence memory is extremely fast for several reasons. First, a connection needs only pass the concurrence test once. Second, the connection is not severed if it fails the test; it can be tested on the same minicolumn multiple times. Finally, a huge number of connections can be tested simultaneously. The requirement that a minicolumn fires if only an equivalent of 2 or more of its input connections fire is very powerful. This is what allows us to recognize abstract art and see objects in the clouds. How the equivalence is computed is slightly more complicated than it sounds but I'll leave that to a future post. Stay tuned.

See Also:

Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning

Thursday, February 22, 2018

Solving the Mysteries of Reciprocal Corticothalamic Feedback and Cortical Learning


Neurobiologists have observed (see references at bottom) that neurons in the thalamus, the part of the brain that receives input connections from the body's sensors, not only send output connections to cortical columns in the cerebral cortex, but receive reciprocal feedback inputs from the same columns. No one knows why this happens. What follows is a novel hypothesis that explains the function of the corticothalamic feedback connections as an essential part of the mechanism of sensory perception and learning.
Important note: I am neither a neurobiologist nor a neuroscientist. I get almost all my understanding of the brain by deciphering ancient Judeo-Christian occult texts. If this bothers you, then this article is obviously not meant for you. Sorry.
A Model of the Perceptual System of the Brain and the Cortex

In order to understand why the brain's perceptual system uses feedback signals, it is essential to have an idea of what it is trying to do, how it is organized and the function of its subsystems.

The diagram above is the hypothesized perceptual model. It posits that the thalamus (pattern memory) is where the brain stores a huge number of elementary pattern detectors. These send their output signals directly to the cortex (sequence memory) where they connect to a myriad neuronal structures called minicolumns. These are contained inside bigger structures called cortical columns. There are approximately 100 million cortical columns in the human brain and each has 100 minicolumns on average. Each minicolum consists of 6 associate inputs and 1 primary input. The role of a cortical column is to learn as many pattern combinations as possible. Every connected minicolumn in a column is a different manifestation of the primary input of the column. The green arrow in the diagram represents the feedback signals that return to the origins of the feedforward signals, which are the pattern detectors in the thalamus. The number of feedback connections is equal to that of the feedforward connections.

Cortical columns (see previous article) are arranged in a feedforward hierarchy of up to 20 levels or regions. Pattern signals arrive at the bottom or entry level and percolate up the hierarchy as far as they can go. The activation of a topmost minicolumn signifies that a complex object or pattern has been detected. Normally, many top minicolumns in the hierarchy will fire simultaneously depending on the complexity of the object. Think of an object as a mountain with many peaks and plateaus. How they are clustered together to form a single object is the subject of a future article. Keep in mind that the exact composition of an object is not learned. It is composed instantly even if the brain have never seen it before.
3D Reconstruction of 5 Cortical Columns in Rat Vibrissal Cortex
(Credit: Marcel Oberlaender et al)
What is important to realize is that pattern detection does not occur until and unless a minicolumn has fired. This is how the brain handles sensory uncertainty. The problem is that pattern signals arriving from the thalamus are rarely perfect due to occlusions, noise and other accidents. The minicolumns are, likewise, rarely perfect. The brain solves the uncertainty problem by using a threshold level in its minicolumns that must be reached or surpassed in order to warrant a detection event. When this happens, a topmost minicolumn emits a feedback signal that quickly cascades down the hierarchy one level at a time, branching out as it does, all the way down to the source pattern detectors in the thalamus. The signal branches out because every one of the 7 inputs to a minicolumn is paired with a reciprocal feedback output directed down the hierarchy. In other words, when a minicolumn fires or receives a feedback signal from above, it immediately outputs 7 feedback signals down the hierarchy. This grows exponentially at each level.

Solving the Mystery of Reciprocal Corticothalamic Feedback

Two questions comes to mind. First, why does the cortex use feedback signals? Second, why must the feedback signals travel all the way down into the thalamus? Why-type questions are always the best. The answers we are looking for in this case depend on gaining a good understanding of the cortical learning process:

Reciprocal Corticothalamic Feedback
  • The most important reason for having feedback signals, as explained earlier, is that this is the fastest and most energy-efficient way to solve the uncertainty problem. The solution is to enlist the contribution of many parallel inputs during the detection process. A high enough number of signals arriving at a topmost minicolumn is enough to overcome uncertainty. Contrary to common wisdom, the brain is not a probability thinker but a cause-effect thinker. The brain assumes a perfect and deterministic world. When we recognize grandma, it's not 50% or 90% grandma. It's either grandma or no grandma.
  • The cortex is the seat of episodic memory. When a minicolumn receives a feedback signal, it immediately records a memory trace and the time of the activation. This is crucial because this recording affords us not only a way to recall past events but also makes it possible to predict the future. Of course, the memory trace dissipates quickly unless it is rehearsed repeatedly.
  • Learning in the cortex consists of forming pattern combinations one minicolumn at a time. It is important that learning be as fast as possible. Random inputs are connected to a minicolumn and tested to see if they arrive concurrently. If an input passes the test only once, it immediately becomes a permanent connection. While this learning method is very fast, it can result in erroneous connections because of chance occurrences. There must be a way to correct the errors.
  • The error correction method is straightforward. Every time a minicolumn receives a feedback signal, it strengthens every input connection that just received a strong enough signal. Bad input connections that do not fire on time rarely get strengthened and so remain weak. However, these bad connections are not severed immediately. This happens at night during REM sleep.
  • Finally, the reason that the thalamus receives feedback signals is that connections to the first level of the cortical hierarchy are learned in the thalamus. The reason for this is that learning (searching for viable connections) in the thalamus is faster and easier due to the sheer number of pattern neurons. The thalamic connections must also be strengthened by feedback signals and disconnected during REM sleep if they don't behave as expected and are therefore weak.

To sum up, feedback signals are an integral part of the brain's cortical learning mechanism and its ability to process imperfect sensory signals. In the cortex, they contribute to episodic memory. In the thalamus, their only function is to strengthen good connections and disconnect bad ones. In a future article, I will go over how the cortex clusters large numbers of minicolumns to form invariant objects or concepts. Clusters are also part of the brain's attention mechanism.
And I answered the second time and said to him, “What are the two olive branches (clusters) which are beside the two golden pipes, which empty the golden oil from themselves?” (Zechariah 4:12)
See Also:

Fast Cortical Learning Using Spike Timing
Feedback Connections to the Lateral Geniculate Nucleus and Cortical Response Properties
Emerging views of corticothalamic function
Stuff I've Been Working on: The Cortical Column
Fast Unsupervised Pattern Learning Using Spike Timing

Sunday, February 18, 2018

Stuff I've Been Working on: The Cortical Column

The Cortical Column

Understanding the organization and function of the cortical column is essential to figuring out how the brain works. What follows are partial results of my brain/intelligence research over the years. Unlike deep neural nets, the brain can instantly see a complex object that it has never seen before. How does it do it? It learns lots of small elementary patterns (lines, edges, bits of sounds, etc.) by creating simple sensors that reside in the thalamus.

All elementary patterns come in opposite/complementary pairs. They are the building blocks of all objects. The brain can instantly reuse them to detect any complex object on the fly. This is crucial to survival. Object detection is the job of millions of cortical columns. These are organized into two yin-yang or mirror hierarchies of up to 20 levels. The object detection process is fast and simple and requires little computation. Signals from pattern detectors simply percolate up the hierarchy according to their temporal signatures. An entire detection process, from elementary pattern detections to recognition feedback signals, takes about 10 milliseconds.

Each column can learn dozens of small pattern combinations stored in minicolumns. The combinations in every column revolve around a single pattern detector called the primary input. Only one combination can be detected at a time. Each minicolumn has one output that is sent to a higher layer. Each also receives a feedback connection from the layer above it. Feedback signals are recognition events that serve to correct incomplete pattern detections. How the combinations are learned is the topic for a future article.

The cortical hierarchy is a magnificent machine. It can do all sorts of beautiful and wonderful things that I cannot go into in this article. I will conclude by adding that an activated topmost minicolumn in the knowledge tree (a branch) represents a complex sensed object or pattern at a point in time.

I don't know when but there will be more to come. Stay tuned.

I'm Working on Stuff

A Little Taste of What I'm Working On

That's all.

Friday, February 9, 2018

Busy Days

I'm Still Alive

I'm just busy developing a smartphone application for the hearing impaired market. It's slow going but I'm hoping I can use it to raise enough money for much bigger robotics projects I have planned for the future. Stay tuned.

Friday, February 2, 2018

People Ask Me, What Do You Have Against Deep Learning?

Yes, I Cannot Stand Deep Learning

I got a closetful of criticisms against deep learning. I have written about them in the past. I will not list them here because what would be the point? I am not really against the technology of deep learning per se. It is useful for what it does. I am just against the idea advanced by mainstream AI that deep learning is a step toward artificial general intelligence (AGI) or human-like intelligence. In this context, let me just say that, if you are researching AGI, deep learning must be thrown away like yesterday's garbage for this one specific reason if for no other: A deep neural net learns complex patterns but the brain does not. The brain can instantly see a new complex pattern without learning it. Let me say this again for emphasis because it is crucial to my position:
A deep neural net learns complex patterns but the brain does not. The brain can instantly see a new complex pattern without learning it.
Huh? That's right. In fact, almost everything the brain sees is new, that is, seen from different angles or under different lighting conditions. There aren't enough neurons and synapses in the brain to store all the possible patterns that it would need to learn in order to interact with the world. We can instantly see complex objects or patterns that we have never seen before. A deep learning system would be blind to them. We only remember high level bits and pieces of the patterns that we see. Most of the low level details are either forgotten or are written over by new experiences.

As the late philosopher and AI critic, Hubert Dreyfus, was fond of saying, the brain does not model the world. The world is its own model. The brain simply learns how to see it. There is huge difference between the two, one that I hope will, one day, be common knowledge in the scientific community. Dreyfus was saying this decades ago. He was at least a hundred years ahead of mainstream AI.

See Also:

The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI

Friday, January 26, 2018

A Surprising Secret About Sequence Learning in the Brain

This Is Not the Way It Works

Note: This article is not meant for atheists or materialists. It is for believers only. Sorry.

When thinking about how sequence learning might work in the brain, most people would imagine some sort of neuronal mechanism that strings activated nodes (minicolums) together, as one would string pearls to form a necklace. Well, this is not the way it works. Surprisingly, the brain has no special mechanism for sequence learning. The brain forms the pearls that will go in the necklace but does not string them together. Sequence learning occurs automatically. That is to say, the pearls know where they belong in their sequence. It gets even better. If the order of the sequence changes for whatever reason, the pearls will automatically rearrange their positions. How is that possible?

The Timer

The trick is to use a timer. I have always maintained that the brain is essentially a massive timing mechanism. The hippocampus can generate special spike trains that are sent out to the cortex and used for timing purposes. When a node is activated, the time of its activation is immediately recorded and the node is given an initial activation strength. This is what neurobiologists and psychologists refer to as a memory trace. Unless reawakened multiple times and strengthened, the trace dissipates and the memory is gone. What is interesting is that, during recollection, the brain can use the same spike trains internally to reactivate the nodes in the exact order in which they were activated.
Wake up, and strengthen the things that remain, which were about to die; for I have not found your deeds completed in the sight of My God. So remember what (how) you have received and heard; and keep it, and repent. Therefore if you do not wake up, I will come like a thief, and you will not know at what hour I will come to you.

Rev 3:2-3, Message to Sardis

Friday, January 19, 2018

Re: The High Priest, the Golden Menorah and the Cortical Column

I apologise for taking down the previous article about the cortical column. I just don't think it is the right time for that knowledge to be released. Not yet. Sorry.

Friday, January 12, 2018

I Understand the Cortical Column

Just a quick post for the record. My original model of the cortical column was in error. The column does not represent a sequence of nodes, as I assumed back then, but a single node in a long sequence. Each minicolumn is a different manifestation of that one node. I was right about one thing: only one minicolumn can be activated at a time. The activation of a minicolumn is very deterministic and highly predictive: it has only one successor and one predecessor. That's all for now.

Have a good weekend.

Thursday, January 11, 2018

I Have a Dream

Yaskawa Electric’s industrial robot Motoman.
Photograph by Yoshikazu Rsuno — AFP/Getty Images

I Would Like to Build a Robot Cook

I need money to finance a robotics project. I would like to build an intelligent robot cook, one that is smart enough to walk into any equipped kitchen and fix a meal of scrambled eggs with bacon, toast and coffee, and clean up afterwards. Mainstream artificial intelligence practitioners enjoy bragging about their achievements using deep learning and other AI techniques. They want us to believe that they are making progress toward true intelligence by making machines that can beat a human expert at board games like chess or GO. Don't let them fool you. None of it has anything to do with artificial general intelligence (AGI). In spite of the hype, their brand of AI could not be used to perform the simplest tasks that humans have no trouble with. The robot cook I am talking about is hopelessly beyond their capabilities.

Raising the Money

I want to obtain the necessary funds for my project without raising suspicions from ill-intentioned parties. My current plan is to offer a device for hearing impaired individuals that would enhance foreground speech while muting all background noises, including other voices. Many hearing impaired people have trouble tuning out unwanted noises from a conversation. They don't so much have a hearing problem as a problem with their attention mechanism. This is not a problem that can be solved with ear plugs at this time. It requires significant computing resources which can only be provided by a more powerful device such as a smartphone.

Smartphone App

My plan is to market this product as a smartphone app. The worldwide hearing aids market is in the billions of dollars. Such an app could potentially help me raise all the funds I need for my robotics project. I refuse to accept any investment from third parties. Wish me luck.