<p>In the abstract of the paper you quote, they say (edited by me a bit to make it more brief:</p>

<blockquote>
  <p>First we show that a neuron with several thousand synapses...can recognize hundreds of independent patterns of cellular activity... We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials...and patterns detected on basal and apical dendrites act as predictions... We then present a network model based on neurons with these properties that learns time-based sequences.</p>
</blockquote>

<p>Basically, they are showing a bunch of things that are true in some simulations they have run, which are based on some biologically plausible assumptions. There is no reason to doubt their simulations, and Hawkins is a credible person.</p>

<p>The basic assumptions they make are grounded in known and agreed upon concepts in neuroscience: the anatomy of different cell types, for example, the effects of synapses at different places in the cell. The whole synthesis of their work, though, is just a hypothesis, and that's also how they are presenting it. It wouldn't make sense, and Hawkins would not claim, that this is "accepted": it's a proposed idea that they show some properties of <em>in silico</em>. Even if some cells in the brain do work this way, it doesn't necessarily mean it is an organizing principle, either. If you're interested, Hawkins' book <em>On Memory</em> tells a bit more.</p>

<p>You can add a bit of skepticism if you'd like because these authors work for a company that sells patented machine learning technologies based on their neuroscientific work, but I don't think that's a reason to throw them out entirely, and their academic publications seem sound. I like to use <a href="https://scholar.google.com/scholar?cites=12178982597363125669&amp;as_sdt=2005&amp;sciodt=0,5&amp;hl=en" rel="nofollow noreferrer">Google Scholar</a> to track citations of a given paper; if you follow that link or find the paper there on your own, you'll see it has been cited ~150 times, though many of those citations are coming from a more computational/machine learning angle, which makes sense: that's the main application for the paper. It's not really proposing any novel biology, since the biology is mostly based on things already known.</p>

CC BY-SA 4.0
2019-07-29T23:46:02.167Z
Thanks for the considered response, but I think it largely misses the point. See edit.
david.pfx
CC BY-SA 4.0
2019-07-29T23:51:59.893Z
@david.pfx See the cited works on Google Scholar via the link I provided if you're interested in seeing what work has built on this one. As I wrote at the end of my answer "many of those citations are coming from a more computational/machine learning angle, which makes sense: that's the main application for the paper. It's not really proposing any novel biology, since the biology is mostly based on things already known."
Bryan Krause
CC BY-SA 4.0
2019-07-28T23:47:08.773Z
2019-07-28T23:47:08.773Z
Bryan Krause
<p>I ran across this article <a href="https://www.frontiersin.org/articles/10.3389/fncir.2016.00023/full" rel="nofollow noreferrer">Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex</a>, which leads to <a href="https://en.wikipedia.org/wiki/Numenta" rel="nofollow noreferrer">Wikipedia</a> and <a href="https://numenta.org" rel="nofollow noreferrer">HTM</a>.</p>

<p>To my reading, this provides a credible theory of sensory encoding and neocortical processing, along with a mechanism for sequence memory. Seems too good to be true.</p>

<p>Am I being gullible? Is this research regarded as credible? Is it the subject of academic research by others quoting this work?</p>

<hr>

<p>So what I'm really looking to do is to quantify and qualify the extent to which this work has influenced the work of others; the extent to which others have either built upon it or proposed alternatives or ignored it entirely. For example, is there any academic thought leader or highly-respected group that has reviewed this material and written in judgment?</p>

CC BY-SA 4.0
2019-07-27T13:45:24.443Z
Yes Hawkins is credible, but he isn't claiming to have everything worked out. He is proposing an idea is all, and showing how it might work. What do you mean by "accepted"?
Bryan Krause
<computational-modeling>
Is HTM (Hierarchical Temporarl Model) accepted as credible?
david.pfx
# Is HTM (Hierarchical Temporarl Model) accepted as credible?

(Asked by: username_0 on 2019-07-27)

I ran across this article [Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex](https://www.frontiersin.org/articles/10.3389/fncir.2016.00023/full), which leads to [Wikipedia](https://en.wikipedia.org/wiki/Numenta) and [HTM](https://numenta.org).

To my reading, this provides a credible theory of sensory encoding and neocortical processing, along with a mechanism for sequence memory. Seems too good to be true.

Am I being gullible? Is this research regarded as credible? Is it the subject of academic research by others quoting this work?

---

So what I'm really looking to do is to quantify and qualify the extent to which this work has influenced the work of others; the extent to which others have either built upon it or proposed alternatives or ignored it entirely. For example, is there any academic thought leader or highly-respected group that has reviewed this material and written in judgment?

**Tags:** computational-modeling

> **Comments:**
> **username_1**: Yes Hawkins is credible, but he isn't claiming to have everything worked out. He is proposing an idea is all, and showing how it might work. What do you mean by "accepted"?  

---

## 1 answers

### Answer by username_1 on 2019-07-28. Score: 1

In the abstract of the paper you quote, they say (edited by me a bit to make it more brief:

> First we show that a neuron with several thousand synapses...can recognize hundreds of independent patterns of cellular activity... We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials...and patterns detected on basal and apical dendrites act as predictions... We then present a network model based on neurons with these properties that learns time-based sequences.

Basically, they are showing a bunch of things that are true in some simulations they have run, which are based on some biologically plausible assumptions. There is no reason to doubt their simulations, and Hawkins is a credible person.

The basic assumptions they make are grounded in known and agreed upon concepts in neuroscience: the anatomy of different cell types, for example, the effects of synapses at different places in the cell. The whole synthesis of their work, though, is just a hypothesis, and that's also how they are presenting it. It wouldn't make sense, and Hawkins would not claim, that this is "accepted": it's a proposed idea that they show some properties of *in silico*. Even if some cells in the brain do work this way, it doesn't necessarily mean it is an organizing principle, either. If you're interested, Hawkins' book *On Memory* tells a bit more.

You can add a bit of skepticism if you'd like because these authors work for a company that sells patented machine learning technologies based on their neuroscientific work, but I don't think that's a reason to throw them out entirely, and their academic publications seem sound. I like to use [Google Scholar](https://scholar.google.com/scholar?cites=12178982597363125669&as_sdt=2005&sciodt=0,5&hl=en) to track citations of a given paper; if you follow that link or find the paper there on your own, you'll see it has been cited ~150 times, though many of those citations are coming from a more computational/machine learning angle, which makes sense: that's the main application for the paper. It's not really proposing any novel biology, since the biology is mostly based on things already known.

> **Comments:**
> **username_0**: Thanks for the considered response, but I think it largely misses the point. See edit.  
> **username_1**: @username_0 See the cited works on Google Scholar via the link I provided if you're interested in seeing what work has built on this one. As I wrote at the end of my answer "many of those citations are coming from a more computational/machine learning angle, which makes sense: that's the main application for the paper. It's not really proposing any novel biology, since the biology is mostly based on things already known."  

---

