Human Thoughts and Memory Are Gigantically Connective, But Brains Have Only Low Connectivity

The human mind and human memory are gigantically connective. A person's thoughts can quickly dart around between vastly different areas of human knowledge. For example, suppose you ask me, "How could modern technology have made a difference if the ancient Romans had possessed it?" Darting around instantly between many different pieces of knowledge in my memory, I might very quickly give an answer like this:

"It's easy to think of many different ways modern technology could have made a difference around the time of Julius Caesar and Octavian. Julius Caesar might have avoided his assassination, maybe by wearing a Kevlar vest and Kevlar collar, or maybe by installing metal detectors at the Senate building. Mark Antony might have won the battle of Actium, by having jet fighters bomb the ships of Octavian into pieces. The Romans might have used tanks and bombers to wipe out the barbarian hordes, preventing the fall of Rome from ever happening. And with smartphones and TV helping everyone to instantly communicate, there would been none of the 'too big an empire' problem that plagued the Romans. The Roman empire might have spread across half of Eurasia."

Or, to give another example, when asking myself, "Name some famous Johns," I quickly wrote down the following, extracting things from a variety of historical eras, and from both fact and fiction:

"Well there's Prince John and Little John in the Robin Hood story. And there's John the author of one of the gospels. Then there's John Updike, an American writer. And there's the famous assassin John Wilkes Booth. Then there's Pope John Paul II. And there's John Lennon.  And Johnny Walker and Johnny Carson. Then there's US presidents John Adams and John Quincy Adams. Then there's John Brown who raided Harper's Ferry. And don't forget the scientist John Dalton."

It seems that the human mind and human memory are gigantically connective. But does the human brain have any degree of connectivity that can explain the almost perfect connectivity of the human mind and human memory? A person might claim that the brain has perfect connectivity, on the grounds that it is possible to trace a path between any two regions of the brain. But it would be hasty to draw a conclusion about brain connectivity from so simple a fact. Analyzing how connective the brain is turns out to be a much more complicated task. 

Neurons in the brain can be analyzed as nodes in a network. With any network there are ways of quantifying how connective the network is. Some important questions may be asked to judge the connectivity of a network:

(1) What is the ratio between the total number of nodes in the network and the total number of connections between nodes in the network?

(2) What percentage of the total nodes in the network is the average node in the network directly connected to?

(3) What is the average time needed to communicate between two random nodes of the network?

(4) How reliably does a signal travel between two nodes in the network?

Let me give some very simple examples of answering some of these questions. Let's consider the very simple network shown below:

Here is a partial analysis of this network's connectivity:

Number of nodes: 7.

Number of connections: 12.

Average number of connections per node: 3.28.

What fraction of the total nodes in the network is the average node in the network directly connected to? 3.28 divided by 7, or .468

A network with  higher connectivity is shown below:

Here is a partial analysis of this network's connectivity:

Number of nodes: 7.

Number of connections: 17.

Average number of connections per node: 4.57.

What fraction of the total nodes in the network is the average node in the network directly connected to? 4.57 divided by 7, or .65, which is roughly two-thirds.

It is the last question that gives us the "bottom line" on how much connectivity the network has. The first network has a "bottom line" connectivity of only .468, but the second network has a substantially higher "bottom line" connectivity of .65.  A network with perfect connectivity would have a "bottom line" connectivity of 1.0. 

Now, having got a bit "warmed up" in analyzing the connectivity of networks, let us consider the question: just how connective is the human brain? We can use the same format as above.

Number of nodes: about 100 billion (which is the number of neurons in the human brain).

Number of connections: about 100 trillion (which is the number of synapses in the human brain, each neuron having an average of about 1000 synapses).

Average number of connections per node: about 1000. Although it is sometimes claimed there are thousands of synapses per neuron, the 2021 study here (Table 1) finds fewer than 100 connections per neuron in primates, finding 25 excitatory synapses per neuron in primates and 44 inhibitory synapses per neuron in primates.

What percentage of the total nodes in the network is the average node in the network directly connected to? 1000 divided by 100 billion, or 0.00000001.

We are left with a shockingly low "bottom line" number on the connectivity of the human brain. The human brain would seem to have a connectivity very, very many times lower than the two networks depicted above. The "bottom line" connectivity of the brain is a number only about 1 in 100 million. 

Here are some interesting findings from the neuroscience literature. The source here says, "Electrophysiological studies detect connections only in approximately 10% of pairs of neurons." This would seem to mean that when scientists check whether two neurons right next to each other are connected, they find that in only about 1 case in 10 are such neurons connected. Referring to a type of brain structure in which neurons are rather densely packed (pyramidal cells), the paper states, "Virtually all electrophysiological studies in vitro find connection probabilities of order 0.1–0.2 for pairs of nearby pyramidal cells."

Using older and different estimates about the number of brain cells and the number of connections (synapses) between brain cells, a scientific paper ("Is the brain really a small-world network?") states the following:

"On average, the density of human brain connectivity at the cellular level is very sparse. The average number of synapses of neurons (~104) (Braitenberg and Schüz 1998) divided by the number of neural elements (~1010) (Herculano-Houzel 2012) results in a very low average probability of any two neurons in the brain making contact (10−6), implying a highly dispersed network." 

The paragraph above is telling us that if you were to pick two random neurons in the brain, there would be only about 1 chance in a million that they are directly connected. It seems that the connectivity of neurons in the brain is very low, way too low to explain the almost perfect connectivity of ideas, thoughts and memories in the human mind. 

There are two other crucial factors we should consider when considering the connectivity of the brain:

(1) How fast do signals travel between neurons?

(2) How reliably does a signal travel when it passes between two neurons?

Considering the first of these questions, the widely quoted figure of about 100 meters per second for brain signals is very misleading. That is the fastest that a signal can travel in any part of the brain, when signals pass through myelinated axons. But most axons in the cortex are not myelinated, and most of the tissue in the brain consists of relatively slow dendrites. According to neuroscientist Nikolaos C Aggelopoulos, there is an estimate of 0.5 meters per second for the speed of nerve transmission across dendrites (see here for a similar estimate). That is a speed 200 times slower than the nerve transmission speed commonly quoted for myelinated axons. Then there is the enormous slowing factor caused by the need for brain signals to cross across synapses, serious "speed bumps" that should slow down brain signals very much. 

There is a scientific term used for the delay caused when a nerve signal travels across a synapse. The delay is called the synaptic delay. According to this 1965 scientific paper, most synaptic delays are about .5 milliseconds, but there are also quite a few as long as 2 to 4 milliseconds. A more recent (and probably more reliable) estimate was made in a 2000 paper studying the prefrontal monkey cortex. That paper says, "the synaptic delay, estimated from the y-axis intercepts of the linear regressions, was 2.29" milliseconds. It is very important to realize that this synaptic delay is not the total delay caused by a nerve signal as it passes across different synapses. The synaptic delay is the delay caused each and every time that the nerve signal passes across a synapse. 

Such a delay may not seem like too much of a speed bump. But consider just how many such "synaptic delays" would have to occur for, say, a brain signal to travel from one region of the brain to another. It has been estimated that the brain contains 100 trillion synapses (a neuron may have thousands of them).  So it would seem that for a neural signal to travel from one part of the brain to another part of the brain that is a distance away only 5% or 10% of the length of the brain, that such a signal would have to endure many thousands of such "synaptic delays" requiring a total of quite a few seconds of time. 

There is no reason to think that the average speed of signals in the brain should be much faster than the speed at which electrical signals travel around the brain during seizures. The paper here lists a speed of only about 1 millimeter per second for seizures in the human brain, saying, "Seizures propagate slowly to connected areas with speeds on the order of 1 mm/s."  There is no reason to think that some hypothetical brain signals involved in thinking would occur much faster than seizures. 

How reliably does a signal travel when it passes between two neurons? It has been repeatedly stated in neuroscience literature that brain signals travel across chemical synapses with a reliability of only .5 or smaller, and almost all synapses in the brain are chemical synapses.  In an interview, an expert on neuron noise states the following:

"There is, for example, unreliable synaptic transmission. This is something that an engineer would not normally build into a system. When one neuron is active, and a signal runs down the axon, that signal is not guaranteed to actually reach the next neuron. It makes it across the synapse with a probability like one half, or even less. This introduces a lot of noise into the system."

 A scientific paper tells us the same thing. It states, "Several recent studies have documented the unreliability of central nervous system synapses: typically, a postsynaptic response is produced less than half of the time when a presynaptic nerve impulse arrives at a synapse." Another scientific paper says, "In the cortex, individual synapses seem to be extremely unreliable: the probability of transmitter release in response to a single action potential can be as low as 0.1 or lower."

A 2020 paper states this:

"Neurons communicate primarily through chemical synapses, and that communication is critical for proper brain function. However, chemical synaptic transmission appears unreliable: for most synapses, when an action potential arrives at an axon terminal, about half the time, no neurotransmitter is released and so no communication happens... Furthermore, when neurotransmitter is released at an individual synaptic release site, the size of the local postsynaptic membrane conductance change is also variable. Given the importance of synapses, the energetic cost of generating action potentials, and the evolutionary timescales over which the brain has been optimized, the high level of synaptic noise seems surprising."

Such a result (a very serious brain physical shortfall) is surprising only to those who believe that your brain stores your memories and that your brain makes your mind.  Those who disbelieve such a thing may expect exactly such shortfalls to be repeatedly found. 

To summarize, there are three gigantic reasons why a human brain cannot be regarded as any kind of high-connectivity network:

(1) The "bottom line" connectivity of the brain (as defined above) is very low, with the average neuron being directly connected to fewer than 1 in a million of the brain's neurons, and as few as 1 in 100 million of the brain's neurons. 

(2) You cannot assume that this shortfall is fixed by signals traveling reliably between many neurons (such as from Neuron 1 to Neuron 2 to Neuron 3 to Neuron 4 to Neuron 5 to Neuron 6), because the reliability of signal transmission across synapses is so low that the signal would very probably be lost when even trying to pass across only four different neurons.

(3) Very serious slowing factors such as the low transmission speed of dendrites and synaptic delays should worsen brain connectivity even further. 

Your mind and memory are almost perfectly connective. But your brain has poor physical connectivity. Such a discrepancy is one of very many reasons for thinking that your brain cannot be the source of your mind. 

Postscript: The 2022 paper "What Kind of Network Is the Brain?" by John D. Mollon  and others gives us some facts that cast doubt on claims that the brain is a very highly connected network. We read this:

"Excluding callosal neurons, efferent neurons, and all non-pyramidal cells, they estimate that the total number of neurons making ipsilateral connections within one hemisphere is 6 × 10⁹. However, they estimate that there are only ~10⁸ axons in all the major long-range tracts combined. Thus, of all the cells that make cortico-cortical connections, most are local in their projections, and only ~2% have access to the long-range tracts within one hemisphere (and the proportion having access to any individual tract is likely to be still smaller) [46]. The proportion of non-efferent cells contributing axons to the corpus callosum is similarly ~2%.

The estimates obtained by Schüz and Braitenberg were based on classical histology, but they draw confirmation from a recent analysis of diffusion MRI (dMRI) data. Rosen and Halgren [48] analyzed tractography data for 1065 individuals in the Human Connectome Project. They calibrated their dMRI measure by reference to the known density of axons in the corpus callosum and the cross-sectional area of the corpus callosum of each individual (obtained by structural MRI). They then used this conversion factor to estimate the number of axons in the long-distance fasciculi. For each of the 360 'parcels' [49] of cortex, they calculated the number of fibers connecting to every other parcel. Long-range connections (callosal plus intra-hemispheric) were sparse, about 3.7% in total – a value close to Schüz and Braitenberg's estimate of 4%. The limited capacity of the long-distance tracts is difficult to reconcile with models that suppose the brain is a meta-net [9] or with accounts of memory in which cell assemblies depend on many long-range excitatory connections. [50]"

The 2022 paper "An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected" found that human cortical areas are "sparsely connected," contrary to popular depictions of a brain in which all the neurons are very highly connected. The paper makes this revealing statement:  "We find that the widespread cortical integration implied by behavioral and  mental coherence, and routinely observed in widespread physiological synchronization, belies a surprising small absolute number of long-range axons connecting cortical areas."  In other words, human mental performance tells one story, and your brain tells another story not expected from human mental performance.  This is just what we might expect if the brain is not the source of the human mind. 

"Brains Make Minds" Models All Flunk a Large Brain Scan Study

There recently appeared a study attempting to measure how well different neuroscientist theories about intelligence performed when trying to predict intelligence from brain scans. The theories were all just minor variations of the idea that intelligence is purely a product of the brain. All of the neuroscience theories tested flunked this test very badly. But the press release announcing the study failed to mention this big flop, and merely gave us a headline announcing that one of the theories performed better than the others.  

The study was entitled "Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach." The study used a surprisingly high number of subjects, about 300. In this respect the study was very different from the great majority of experimental neuroscience studies using brain scanning, which routinely use way too-small study group sizes. Nowadays experimental neuroscience studies mostly display an appalling failure to follow sensible standards. There is no standard being used for the minimum number of subjects that must be used. The great majority of published experimental neuroscience studies are junk science studies that use way too-small study group sizes, typically fewer than 15 subjects per study group. The results reported in such studies are mainly noise and false alarms. Do not ever make the very large mistake of assuming that an experimental neuroscience study must have been good science if it passed peer review and got published in a major science journal. Nowadays peer reviewers are letting all kinds of junk studies and poorly designed research get published in leading neuroscience journals.  The peer reviewers of neuroscience journals are typically scientists who themselves wrote papers using Questionable Research Practices such as a lack of a blinding protocol, unreliable techniques for measuring animal fear, and way-too-small study group sizes.  Such peer reviewers are reluctant to exclude papers for committing the same sins that were committed in the papers authored by the peer reviewers themselves. It's kind of like a situation in which tax cheaters who cheat on their taxes every year are in charge of auditing tax returns by other people. 

In the study "Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach" about 300 subjects were given a large variety of cognitive tests. The same subjects had their brains scanned. From features detected in brains, a group of neuroscience theories were used to make predictions about how well the subjects should have performed in intelligence tests. Graphs were created showing how well these predictions matched reality. 

The neuroscience theories tested against reality included the following:

(1) A "lateral PFC" theory assuming that intelligence mainly comes from the prefrontal cortex.

(2) A "Parieto-Frontal Integration" theory that "proposes that connectivity of a distributed frontoparietal network accounts for intelligence by enabling the integration of knowledge between frontal and parietal areas to support hypothesis generation and problem solving."

(3) A "Multiple Demand" theory that "incorporates more recent advances in understanding the network architecture of general intelligence by appealing to an even broader network of frontoparietal and cinguloopercular regions." 

(4) A "Process Demand" theory that "provides a novel framework centered on the idea that general intelligence reflects the engagement of multiple cognitive processes represented by the overlap (or shared connections) among brain networks." 

(5) A "Network Neuroscience" theory that proposes that intelligence "emerges from individual differences in the network topology and dynamics of the human connectome." 

The paper has some graphs showing how well these theories predicted intelligence.  We get two main types of graphs: scatter plot graphs and  correlation graphs shown as bar graphs. 

Before discussing the results, I must give a little primer on scatter plot graphs involving correlation. A scatter plot shows data items for which two numbers have been collected. For example, if you kept track of how much ice cream was sold on a store, while recording the temperature of each day, you could make a nice scatter plot comparing sales on the different days, and the temperature on each day; and you would see a nice correlation between hot weather and ice cream sales.  When there is a strong correlation, a scatter plot will look something like the graph below, showing a very clear correlation:

Graph 1: A scatter plot showing high correlation

When there is very little or no correlation, a scatter plot will look something like the graph below, with the points scattered all over the graph, with the points showing no very clear line:

                     Graph 2: A scatter plot showing little or no correlation

The study "Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach" has some scatter plots showing how well the various "brains make minds" models performed. The scatter plots all look like Graph 2 above, and show the models flunking the test by performing very poorly at predicting intelligence. 

Figure 4 of the paper shows the scatter plot below, where we see a failure of the "lateral PFC" model to perform impressively, without any clear trend line:

A bar graph next to this graph shows us that the predictive performance is dismal, with the performance seeming to be worst than what we would expect from mere guessing. Figure 5 of the paper looks like the scatter plot shown above, and shows very bad predictive performance of the "Parieto-Frontal Integration" theory, with no clear trend line. Figure 6 of the paper  looks like the scatter plot shown above, and shows very bad predictive performance of the "Multiple Demand" theory, with no clear trend line. 

Discussing the "Process Overlap" theory, the paper tells us that "we find evidence that whole-brain functional edges do a relatively poor job at predicting g [intelligence] compared with other connectivity profiles, with the best-performing model (Figure 7a) generating predictions of r = .11."  The r is a measurement of correlation, which can vary from r = 0 (no correlation) to r = 1 (perfect correlation). A correlation of only .11 is a negligible correlation. As a general rule of thumb, there is no good evidence of a causal relation unless you find some r value greater than .3, and the evidence for a relation is weak unless the r value is .5 or greater.

Finally the paper comes to displaying the performance of the theory that supposedly produces "the most robust predictions of general intelligence" of the theories: the "Network Neuroscience" theory. Unfortunately, the performance of this "best of the lot" winner is dismal. Figure 11 of the paper gives us this scatter plot showing the performance of this "Network Neuroscience" theory:

Again, we see a scatter plot failing to show any clear trend line. The bar graph included with this scatter plot further clarifies how badly the "Network Neuroscience" theory performs. In that bar graph we see that with most versions of the theory, the correlation level is actually less than 0, with a negative correlation.  That equals worst results than you would get from random guessing or throwing a dice.

The end of the "Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach" fails to accurately characterize these extremely poor results from all of the models. We read multiple times a totally unjustified use of the phrase "reliable predictions of g [intelligence]" that does not match any of the graphs shown. The paper should have had a conclusion section mentioning the abysmal predictive failure of all of the models tested. Instead the paper ends with some unjustified language contradicting the data it displays. It's as if the authors failed to study their own graphs, or failed to accurately describe them.  This is what happens very frequently in today's neuroscience literature: authors making claims (particularly in paper titles and paper abstracts) that do not match the data they have collected.  The very marginal and very weak association between cognitive scores and brains shown by a small subset of the data can easily be explained by factors having nothing to do with intelligence, because brain differences can cause things such as differences in perceptual ability, differences in muscle speed, and differences in manual dexterity, all of which can affect IQ test scores. 

The press release of the study gives us this headline: "Study: Network neuroscience theory best predictor of intelligence."  An accurate headline would have been this: "Models Assuming Brain-Based Intelligence All Flunk a Large Brain Scan Test." The reported results are quite consistent with the idea that your brain does not make your mind.  The press release basically does a cover-up job, by failing to mention the very bad predictive performance of all of the theories. 

We hear quotes from a neuroscientist who fails to mention the very bad failure of all of the "brains make minds" theories when predicting intelligence from brain scans.  Instead the neuroscientist gives us a little empty hand-waving by trying to explain problem-solving by mentioning "connections." A connection of brain cells does nothing to explain problem solving or intelligence. We know of countless highly-connected things that are utterly mindless, like the atoms in a crystal lattice.  The paper I have discussed suggests there is no robust correlation between brain connections and intelligence.  

The result should come as no surprise, as it matches a previous study of brain connectivity.  The study was announced on the Science Daily web site with this headline: "MRI scans of the brains of 130 mammals, including humans, indicate equal connectivity."

We read the following:

"Researchers at Tel Aviv University, led by Prof. Yaniv Assaf of the School of Neurobiology, Biochemistry and Biophysics and the Sagol School of Neuroscience and Prof. Yossi Yovel of the School of Zoology, the Sagol School of Neuroscience, and the Steinhardt Museum of Natural History, conducted a first-of-its-kind study designed to investigate brain connectivity in 130 mammalian species. The intriguing results, contradicting widespread conjectures, revealed that brain connectivity levels are equal in all mammals, including humans." 

Why a Brain Should Be Unable to Reliably Transmit Any Memory or Thought Signal

When neuroscientists attempt to describe electrochemical effects moving around in the brain, they describe it in terms of what is called an action potential.  An action potential is an electrical change in a neuron which can be transmitted to other nearby neurons.  Now, there is a related question very relevant to the issue of whether the brain can actually be the storage place of human memory or the source of human thought. This question is: can these action potentials make up reliable memory signals or thought signals that travel around in the brain?  For example:

1. Could a brain retrieve some memory information stored in one part of a brain, and send that information reliably (as a kind of coherent signal) from one part of the brain to another part of the brain (perhaps from one part storing the information to another part more involved in attention or current thought)? 
2. Could a brain send some information arising from thinking from one part of a brain to another part (something that would presumably be necessary for a brain to have complex thoughts combining simpler ideas)?

In previous posts on this site I have discussed a major reason for thinking that the answer to the first question must be: no. The reason is that information does not reliably transfer across the synapses that separate neurons. It has been established that action potentials only travel across synapses with a likelihood of about 50% or less (some estimates are as low as 10% or 20%).  So if the brain tried to retrieve detailed information (such as a sentence of text) from one part of the brain to another, and each synapse transmitted an action potential with a likelihood of less than 50%, than the information would not be reliably transmitted.  

A 2020 paper states this:

"Neurons communicate primarily through chemical synapses, and that communication is critical for proper brain function. However, chemical synaptic transmission appears unreliable: for most synapses, when an action potential arrives at an axon terminal, about half the time, no neurotransmitter is released and so no communication happens... Furthermore, when neurotransmitter is released at an individual synaptic release site, the size of the local postsynaptic membrane conductance change is also variable. Given the importance of synapses, the energetic cost of generating action potentials, and the evolutionary timescales over which the brain has been optimized, the high level of synaptic noise seems surprising."

Such a result (a very serious brain physical shortfall) is surprising only to those who believe that your brain stores your memories and that your brain makes your mind.  Those who disbelieve such a thing may expect exactly such shortfalls to be repeatedly found. 

In the brain, information would need to travel though very many synapses for even a short trip in the brain. What analogy can we give for such a setup, if each trip across a synapse occurs with low reliability? An analogy would be if I send an email from New York to Los Angeles, with the email passing through seven different computer servers, each of which transmits each particular character  with a reliability of less than 50%.  Under such a setup, it would be a lucky if a single word of my email got from New York to Los Angeles.  There would be such message garbling and loss of characters that it would be a kind of like trying to read a pen-written message on a piece of paper that had gone through a washing machine seven different times. 

There is another major reason for thinking that a brain should be unable to transmit any memory or thought signals. The reason is that most neurons have so many connections that there would be a signal overload preventing the reliable transmission of information. 

Let us consider three different devices that effectively transmit information: a computer with a simple web browser,  a radio and a television.  There is one very important thing common to each of these inventions: each is arranged so that signals are received from only one source at a time.  For example:

• A television set is arranged so that it can display TV signals from only one TV channel at a time.
• A radio is set up so that it can receive signals from only one radio station at a time.
• A computer with a simple web browser can display information from only one URL or web site at a time (let's ignore the not-so-simple web browsers that allow you to display different web sites in different tabs, and ignore the possibility of bringing up multiple instances of a web browser on the same computer). 

Now, let's imagine what chaos would result if these things were not arranged in such a way:

• If a television set were arranged so that it displayed TV signals from five or ten TV channels at the same time, you would see and hear such a confusion of pixels and sounds that you would not be able to understand or enjoy any of the channels.
• If a radio were set up so that it received signals from five or ten different radio stations at a time, you would probably get such a confusion of sounds you would not be able to understand or enjoy anything coming from the radio.
• If a computer used a web browser that displayed five or ten web pages all at the same time, the browser's screen would show such a confusion of pixels that you would not be able to understand anything. 

For example, if your TV set displayed five stations at the same time, you might see something like the jumble below, which would not be coherent, intelligible information. 

What we know about the physical arrangement of the brain tells us that the brain should suffer from the same type of problem described above. Since each neuron is bombarded with signals from many other neurons, most of which fire randomly, it should be impossible for neurons to accurately transmit thought or memory signals.  It has been estimated that the average neuron has 7000 connections to other neurons. Every neuron should be like some malfunctioning TV set that picks up simultaneously 100 different TV stations at the same time, resulting in an incomprehensible jumble like the jumble shown above. 

Below we see a diagram of a neuron. The yellow part is a myelinated axon, and the orange parts are dendrites.  

For anyone who thinks that a neuron receives an "action potential" (AP)  nerve signal only from an axon, the article here tells us the following:

"In fact, dendrites can be the site of AP initiation and propagation, and even neurotransmitter release. In several interneuron types, all functions are carried out by dendrites as these neurons are devoid of a canonical axon."

The wikipedia.org article on dendritic spikes tells us the following:

"In neurophysiology, a dendritic spike refers to an action potential generated in the dendrite of a neuron. Dendrites are branched extensions of a neuron. They receive electrical signals emitted from projecting neurons and transfer these signals to the cell body, or soma. Dendritic signaling has traditionally been viewed as a passive mode of electrical signaling. Unlike its axon counterpart which can generate signals through action potentials, dendrites were believed to only have the ability to propagate electrical signals by physical means: changes in conductance, length, cross sectional area, etc. However, the existence of dendritic spikes was proposed and demonstrated by W. Alden Spencer, Eric Kandel, Rodolfo Llinás and coworkers in the 1960s[1][2] and a large body of evidence now makes it clear that dendrites are active neuronal structures. Dendrites contain voltage-gated ion channels giving them the ability to generate action potentials."

Given such realities, we can describe a neuron as being subject to the most severe signal overload, like some TV set that is getting 100 channels at once, or some radio picking up 100 stations at once. Given the physical arrangement of neurons in brains, there is no chance that memory signals or thought signals could be reliably transmitted by neurons. Given many signal-slowing factors discussed at length here, it should be impossible for signals to travel through the human cortex at much faster than a snail's pace.  Yet humans can think and recall with the greatest speed and accuracy. This is shown by cases such as actors playing the role of Hamlet, who recall more than 4000 lines with perfect accuracy, and at high speed. It is also shown by calculation savants who do extremely complicated mathematical calculations in their mind very quickly with perfect accuracy. 

There are many historical cases of math prodigies that could calculate with incredible speed and accuracy.  The passage below describes the blazing fast and very accurate calculation powers of Zerah Colburn:

"This child undertook, and completely succeeded in, raising the number 8 progressively up to the sixteenth power. And in naming the last result, viz.: 281, 474, 976, 710, 656, he was right in every figure. He was then tried as to other numbers consisting of one figure, all of which he raised (by actual multiplication, and not by memory) as high as the tenth power, with so much facility and dispatch that the person appointed to take down the results was obliged to enjoin him not to be so rapid. With respect to numbers consisting of two figures, he would raise some of them to the sixth, seventh and eighth power....He was asked the square root of I06,929, and before the number could be written, he immediately answered, 327. He was then required to name the cube root of 268,336,125, and with equal facility and promptness he replied, 645. Various other questions of a similar nature, respecting the the roots and powers of very high numbers, were proposed by several of the gentlemen present, to all of which he answered in a similar manner. One of the party requested him to name the factors which produced the number 247,483: this he immediately did by mentioning the numbers 941 and 263 — which, indeed, are the only two numbers that will produce it...One of the gentlemen asked him how many minutes there were in forty-eight years; and before the question could be written down, he replied 25,228,800; and instantly added that the number of seconds in the same period was 1,513,728,000."

The passage below tells us about the incredibly fast and accurate calculation speed of  Jacques Inaudi, born in 1867:

"In his exercises of mental calculation, Mr. Inaudi is remarkable in two particulars, the complexity of his work and the rapidity with which he completes it. The greater number of questions given to him contain many figures. He will add in his head two numbers consisting of twelve figures each ; he will multiply two numbers composed of eight figures ; he will tell how many seconds there are in any promiscuously chosen number of years, months, days, and hours. These operations demand that he shall hold in his memory the exact problem and the partial solutions up to the time when the complete result is found. For such a considerable work as this, Mr. Inaudi gives an extremely short time, so short, indeed, as sometimes to produce the illusion of instantaneity. The following paragraph has been published concerning him. 'He adds in a few seconds seven numbers of eight or ten figures each; he subtracts one number from another each composed of twenty-one figures in less than a minute; he finds as rapidly the square root or the cube root of numbers consisting of from eight to twelve figures, if these numbers are perfect squares or cubes; it takes a little longer for the last-named work if there is a remainder necessitating a fractional part to the answer. He finds with incredible celerity the sixth or the seventh root of large numbers. He will multiply or divide in less time than it takes him to announce the results. As an example of what has been said, we give the following: He was asked the number of seconds in 18 years, 7 months, 21 days and 3 hours. The response was given in thirteen seconds.' "

The gap between the physical shortcomings of the brain and the realities of the most impressive human mental performance is like the gap between Earth and Jupiter. It is therefore foolish to continue the speech custom of saying that thinking and recall comes from brains, a custom that is an example of hollow hubris.  It would be far wiser for us to say, "Humans have magnificent mental powers, and we don't know where they come from."

Study Finds Equal Brain Connectivity in All Mammals

Observational realities frequently conflict with attempts to correlate brain size and intelligence. In a scientific paper a scientist states, "After correcting for body height or body surface area, men's brains are about 100 g heavier than female brains in both racial groups."  After adjusting for size, male brains are 7% larger, but there is not even a 3% difference in intelligence between males and females. Elephants have brains several times larger than human brains,  but elephants are not as intelligent as  humans. Removing half of a human brain in a hemispherectomy operation has no major effect on intelligence, as discussed in the posts here.  Crows have high intelligence despite tiny brains, and a lack of a neocortex. 

Sometimes it is argued that the real measure of cognitive ability is brain connectivity (the degree to which brain cells are connected with each other).  It has been suggested that maybe humans are smarter than other mammals because our neurons are better connected. But a new study indicates that the brains of humans are not better connected than the btains of other animals. The study is announced on the Science Daily web site with this headline: "MRI scans of the brains of 130 mammals, including humans, indicate equal connectivity."

We read the following:

"Researchers at Tel Aviv University, led by Prof. Yaniv Assaf of the School of Neurobiology, Biochemistry and Biophysics and the Sagol School of Neuroscience and Prof. Yossi Yovel of the School of Zoology, the Sagol School of Neuroscience, and the Steinhardt Museum of Natural History, conducted a first-of-its-kind study designed to investigate brain connectivity in 130 mammalian species. The intriguing results, contradicting widespread conjectures, revealed that brain connectivity levels are equal in all mammals, including humans." 

A Professor Assaf is quoted as stating, ""Many scientists have assumed that connectivity in the human brain is significantly higher compared to other animals, as a possible explanation for the superior functioning of the 'human animal.'" But it turns out that this assumption (a natural one from the idea that your brain is the source of your mind) just isn't true. 

So we have the brain connectivity of mice, the brain connectivity of cows, the brain connectivity of sheep. This is another reason for believing that the human mind (so vastly superior to the mind of such animals) is not produced by the human brain.