Progress in understanding of how the brain works
Yehouda Harpaz
last updated 1 Jun 2013
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Progress in understanding of how the brain works

Most of the discussion in this site of current research are highly critical (Errors, Myths). This page is intended to highlight what looks to me like progress.

1. The brain is always active

[6 Aug 2007]

1.1) In my model I wrote as one of the major hypotheses that the System (i.e. the brain) is always active (Major Hypothseis 6, 4.5.3). I also wrote that it looks to me obvious (

1.2) At least 99% of published articles in the area are effectively based on the assumption that the activity of the brain when it does not explicitly do something is of no interest. However, this seems to change. For example, in this article (Spontaneous Activity Associated with Primary Visual Cortex: A Resting-State fMRI Study, Wang et al, Cerebral Cortex Advance Access published online on June 29, 2007 ; Full text here) they look at activity of the resting brain, and say that "This confirmation supports the perspective that brain is a system intrinsically operating on its own, and sensory information interacts with rather than determines the operation of the system."

1.3) A much stronger support to their conclusion is the observation that the activity of the "resting brain" is far larger than the changes that are normally reported, which they discuss in the paragraph following the abstract. As the references that they mention (one from 1955) show, that is an old fact. What is progress is the fact that they are actually looking at a resting brain, and interpret it as ".. operating on its own..".

1.4) They (and apparently the papers by Raichle that they quote) are still worried of "going too far". They say "Therefore, as suggested by Raichle and colleagues, in terms of overall brain functions, the ongoing intrinsic activity within various brain systems may be at least as important as the activity evoked by external stimuli (Raichle and Gusnard 2005; Raichle and Mintun 2006)." Thus the intrinsic activity is only ".. at least as important..", rather than the obvious "much more important", so we still have some distance to go. But there is a progress in the right direction.

2. Do neurons "represent" anything?

[6 Oct 2007]

In this article (Churchland MM, Shenoy KV (2007) Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology. 97:4235-4257doi ), they find that the neurons in the premotor and motor cortex show complex and heterogenous activity. From this, they suggest the possibility that these neurons don't represent anything.

As discussed here, neurons don't represent anything, but many cognitive scientists and neurosceintists seem to be unable to comprehend this possibility. The authors of the article above are clrearly capable to comprehending it. They present alternative view, but seem to regard their observations as showing that the neurons that they look at do not represent anything. They present the same view in other articles (list of publications, for example the one about "Reference frames for reach planning in macaque dorsal premotor cortex").

The fact that they positively think that the neurons do not represent is progress. The progress is limited, however, because it is still based on the assumption that showing correlation between neural activity and something else (behaviour, stimulus) shows representation. Therefore "representationalists" can still believe that these neurons represent something else.

3. looking at ensembles of neurons.

[2 Dec 2007]

In this article (Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles, Jones et al, PNAS | November 20, 2007 | vol. 104 | no. 47 | 18772-18777 (open accesss article)), they analyze ensembles of neurons, and show that the ensemble response correlates much better with the input than single-neuron analysis. They stress that the single-neuron analysis that is normally used loses information.

Their data is about taste in rats, and it is not obvious how it is going to generalize to other senses. The number of neurons in each "ensemble" is also pretty small (10). But it is encouraging to see researchers that look at ensembles, and explicitly state that single-neuron analysis loses information. They explicitly state that the coherent state sequences that they see do not represent sensory codes, which is also progress.

4. "The Brain Activity Map Project and the Challenge of Functional Connectomics"

[29 Aug 2012]

That is the title of this article (doi) (A. Paul Alivisatos, Miyoung Chun, George M. Church, Ralph J. Greenspan, Michael L. Roukes, Rafael Yuste; Neuron - 21 June 2012 (Vol. 74, Issue 6, pp. 970-974)). The main point about this is that they recognize that you need to look at the network rather than individual neurons to understand what it does. For example, they start the summmary by saying: "The function of neural circuits is an emergent property that arises from the coordinated activity of large numbers of neurons."

They criticize ciurrent studies, and for example say (end of first paragraph of "Emergent Properties of Brain Circuits"):

However, neural circuits can involve millions of neurons, so it is probable that neuronal ensembles operate at a multineuronal level of organization, one that will be invisible from single neuron recordings, just as it would be pointless to view an HDTV program by looking just at one or a few pixels on a screen.
Thus they suggest quite strongly that single neuron studies are useless. That is definitely progress.

As to their suggestion, I think they are over-optimistic about our ability to measure activity in living networks. I suspect that recognizing this limit is the reason that they and other reasearchers did not advocate network research like that until now, and perfomed and supported single-neuron research even when it is clear that it is useless (in complex animals, that means almost always), and that they would not feel able to say it without the optimistic predictions.

Because of the technical issues, it is not obvious that their suggestion is the best approach. But thinking about the actual real networks rather than about single neurons or artifical networks is a large step forward.

4. "The importance of mixed selectivity in complex cognitive tasks"

[1 Jun 2013]

See here. Looks like real progress.