Comments on: Do Bayesian statistics rule the brain?

By: Peter vanderMade

Peter vanderMade — Fri, 18 Sep 2009 04:56:10 +0000

In late 2007 I patented a technology that uses standard binary gates, and uses feedback to change the input parameters in a similar manner to the way STDP functions in biological synapses. The synapses are dynamic and the output is integrated over time in a neuron. A single neuron with 20 synapses requires just over 4200 gates. Over the last couple of years I have been fine-tuning this system and developed a hierarchy of such devices in FPGA. I have been looking at intensity as a second learning mechanism. Having read these papers I consider that STDP or BCM learning may be too limited – that feedback from higher regions in the hierarchy need to be considered in the equation as well.
BTW. ‘On Intelligence’ also introduces the idea of prediction, through advance triggering of columns before sensory perception arrives.
On the subject of stubborness; I believe that the brain forms ‘belief systems’ that feed back into high level sensory perception and modify how that data is handled. Eye witness accounts, related to experience and that persons’ belief systems appear to support that statement

By: Tom Michael

Tom Michael — Mon, 16 Jun 2008 15:21:58 +0000

This seems like a very interesting theory. Certainly, when faced with little visual information, we instinctively perform a saccade (eye movement) to face the stimulus to allow more visual information, but I don’t think it follows that this can be extended to a grand theory of the brain.
For example, when we display heuristic bias and confirmation bias. We can be faced with a problem that causes Cognitive Dissonance by presenting us with information which conflicts with our prior knowledge of a given situation (perhaps Cognitive Dissonance is analogous to the prediction error in the Friston theory).
However, if we then use an heuristic to “solve” the problem, or display a confirmation bias, we are doing the exact OPPOSITE of what Friston’s theory would predict. In this case, we reject conflicting evidence which disagrees with our prior knowledge and predictions, which also serves to reduce the unpleasant cognitive dissonance.
This means there are two ways to reduce cognitive dissonance or prediction errors
1) Accumulate new information and change our predictions or knowledge
2) Ignore the new information and continue to use our old predictions and knowledge, producing stereotypes, irrational beliefs and stubborness
Although as scientists we try to use option 1 the majority of the time, as social scientists we are aware that many people use option 2 quite a lot of the time.
I would suggest that if Friston’s theory held for “higher” cognitive functions such as Reasoning, then we would be far more rational human beings than is in fact the case.

By: tom

tom — Mon, 02 Jun 2008 13:24:51 +0000

Thanks for the link Anibal, an interesting paper. It doesn’t actually mention Bayes directly of course, which would kind of support my point…

By: Anibal

Anibal — Mon, 02 Jun 2008 10:59:37 +0000

Anne K. Churchland (Patricia and Paul Churchland¬¥s daughter both of them are distinghuised [neuro]philosphers) has published an article in “Nature” about how the underpinnings of the neurobiology of decision making it is in consonant with a probabilistic view of the functions of the brain.
It is a current theme within the neurobiology and cognitive neuroscience community to see the brain as a bayesian machine.
Link:
http://www.nature.com/neuro/journal/v11/n6/abs/nn.2123.html

By: tom

tom — Mon, 02 Jun 2008 07:11:33 +0000

I’ve never really understood bayes-fever. I can’t see that the framework gives us scope to make any more detailed general prediction than “there will be both top down and bottom up elements involved in perception”. For specific tasks it’s nice to have an optimality theory, since it gives you a standard for comparison, but the way the brain works in any specific domain is always going to be a matter for investigation.

By: abhik

abhik — Sat, 31 May 2008 18:38:44 +0000

If we’re talking about Bayesian stats and the brain, I should mention Jeff Hawkin’s book ‘On Intelligence’. His HTM model is essentially Bayesian networks with two enhancements: 1) time (ala DBNs) and 2) hierarchal models. It’s a good pop-sci introduction to the subject.