Sergio: "He also uses Bayesian statistical methods, which I don't agree with, because Bayes was a human and I want to know what in his brain made it possible for him to develop such a wonderful theory, not the theory itself. But Friston uses Bayesian methods because he doesn't know about my work, the entropy principle, or the inference that follows."
A: Having convergent validity with Friston is a good sign, because he is a preternaturally brilliant scientist. As you review his work, I'll be looking forward to hearing more about the ways in which your model is compatible/incompatible with Friston's version of the "Bayesian brain." In general, I've been compelled by the idea that cortex is a particular kind of self-orginizing Bayes network, where the symbolic level is continuous with--and emerges from via experience--the sub-symbolic level. However, I think that machine learning approaches will fail to develop sufficiently robust causal reasoning for broad applicability unless they copy the human model by making their learning systems embodied. Embodiment provides useful inductive constraints that provide a toehold for the bootstrapping process that overcomes the challenge of impoverished stimuli and eventually leads to the full flourishing of higher level cognition. I suspect that organisms are such effective learners because they begin with a sense of their own embodiment as a kind of prototypical object, from which they can partially generalize to other dynamics in the world. I think they pay attention to this object because it is directly connected to the mechanisms of reinforcement. The body provides an initial set of values that constrains which of the countless aspects of evolving generative models will be optimized. I think they're able to learn the invariant properties of this object in the first place through hierarchical pattern abstraction, which is only sufficiently powerful in light of the fact that the cortical heterarchy allows for triangulation/mutual-constraints/useful-priors from multiple sensory channels, and more specifically an integration of these multimodal inputs through sensorimotor coupling. Sergio: "In the interest of science, I think it would be important for him to know. Do you know him, can you introduce me to him?" A: Unfortunately, I don't know him personally, and I don't even know his work in depth (it's on my reading list). However, I am compelled by the idea of the brain as a control system that uses free-energy-minimization/successful-prediction-maximization for an embodied agent as it engages in sensorimotor--broadly construed--coupling to navigate the environment in which it is embedded. Theoretically, cortex could efficiently select for utility maximizing sequences by minimizing the “free energy” of the underlying processes (Friston, 2010; Hawkins, 2011; Kozma, Puljic, Balister, Bollobas, & Freeman, 2004). In Hawkins' HTM model (2004), if a minicolumn’s inputs are predicted in advance via stimulation of specific inhibitory interneurons within the column, then only those neurons without their respective inhibitory interneurons activated will increase their firing rates. However, if a sufficient number of non-predicted inputs occur, and a percolation threshold is surpassed, the entire column will become active, resulting in a cascade of activity-predictions in functionally connected columns. My hypothesis: Depending on the degree of functional connectivity with inhibitory interneurons of midbrain neuromodulatory nuclei (Watabe-Uchida, Zhu, Ogawa, Vamanrao, & Uchida, 2012), any dynamic that causes overall activity to be reduced should result in decreased inhibition of the production of these neuromodulators. This net disinhibition would enhance the most robustly active patterns, strengthen the connections underlying these patterns (i.e., reinforcement), and thus increase the efficiency of the dynamics contributing to successful prediction (i.e., minimized error signals). Although this activity-minimizing algorithm could potentially result in stasis, regulatory nuclei of the hypothalamus and midbrain would stimulate these inhibitory interneurons to the degree that action is needed to restore homeostatic balances. Thus an organism could not remain permanently inactive, as physiological signals such as hunger would result in stimulation of these regulatory nuclei, whose activity can be thought of as signifying the distance from homeostatic set points, or as signifiers of biologically specified predictions for which deviations result in error signals. Over time, cortical dynamics resulting in the minimization of error signals from these regulatory nuclei will become distributed across the cortical heterarchy as habitual predictions. The impact of these habitual predictions on overall functioning would constitute the evolving utility function of the organism. Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews. Neuroscience, 11(2), 127–138. doi:10.1038/nrn2787 Hawkins. (2011). Hierarchical Temporal Memory: including HTM Cortical Learning Algorithms. Whitepaper Numenta Inc. Retrieved from http://www.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf Kozma, R., Puljic, M., Balister, P., Bollobas, B., & Freeman, W. (2004). Neuropercolation: A Random Cellular Automata Approach to Spatio-temporal Neurodynamics. In P. Sloot, B. Chopard, & A. Hoekstra (Eds.), Cellular Automata, Lecture Notes in Computer Science (Vol. 3305, pp. 435–443). Springer Berlin / Heidelberg. Retrieved from http://www.springerlink.com/content/jq3d3uj89p9ql7cf/abstract/ Watabe-Uchida, M., Zhu, L., Ogawa, S. K., Vamanrao, A., & Uchida, N. (2012). Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons. Neuron, 74(5), 858–873. doi:10.1016/j.neuron.2012.03.017 -A On Aug 15, 2012, at 11:50 AM, Sergio Pissanetzky <[email protected]> wrote: > > Adam, > > Thanks a lot. You are right on target. In the following weeks or months I > will be studying Karl Friston's work. He is a theoretical neuroscientist > interested in that gray area between Physics and Neuroscience, and therefore > of direct interest to me. Here is a quote from a paper by Daunizeau et. al., > speaking about and in the context of Friston's seminal work: > > "...the functional role played by any brain component (e.g., cortical area, > sub-area, neuronal population or neuron) is defined largely by its > connections ... In other terms, function emerges from the flow of information > among brain areas ... effective connectivity refers to causal effects, i.e., > the directed influence that system elements exert on each other (see Friston > et al. 2007a for a comprehensive discussion)." > > This is, precisely, the kind of things that I can predict for the brain. > Predictions that I have made, and published, are nearly identical to > Friston's, except that mine came from Physics and his came from observation. > Agreement between experiment and prediction is a strong confirmation of both. > When it is inter-disciplinary, it becomes fundamental. > > I note that Friston has recognized the role of causality, of the flow of > information, the principle of free energy for action and perception, of > active inference, in the brain. He uses causal models to infer architecture > of the brain. I have been trying to draw conclusions from Physics about these > same things, and so far, it seems to me, I have not been too far. He also > uses Bayesian statistical methods, which I don't agree with, because Bayes > was a human and I want to know what in his brain made it possible for him to > develop such a wonderful theory, not the theory itself. But Friston uses > Bayesian methods because he doesn't know about my work, the entropy > principle, or the inference that follows. In the interest of science, I think > it would be important for him to know. Do you know him, can you introduce me > to him? > > > Jim, > > So far, I have only made four claims, one corollary, and two conjectures. > They are listed in Section 2 of my Complexity paper. I also apply the four > fundamental principles of nature, causality, self-organization (or symmetry), > least-action, and entropy (or 2nd. law of Thermodynamics). These are > discussed some more in myhome page. I believe this pretty much takes care of > all of Physics. If you know any law or experiment that contradicts my > assumptions, the correct action would be for you to publish a paper > explaining your views and let the scientific community decide. Note that in > Physics, one single experiment that contradicts a theory may mean the > collapse of the entire theory. Or, more usually, the emergence of a new > theory of which the old one is a particular case. > > You ask me to prove all I say before saying it. You should tell the same to > the AGI people. AI started 60 years ago, under the assumption that > intelligence will be conquered by computers. With no proof. So they devoted > themselves to writing programs. Sixty years later, AGI emerges, and is still > using the same assumption. With no proof. You post your study of an algorithm > on an AGI blog. Why would you do that? Because you think the study is a > contribution to AGI. There is no proof of that. Science doesn't work like > that. There is a thing called scientific discourse, where scientists > communicate freely about their ideas. You are essentially telling me to but > off because you seem to dislike my conclusions, or else. I can't hide in a > hole, sorry. > > Isn't it time to try something different? Please, be patient, and keep trying > to understand what I am saying. I know it is not easy and I appreciate your > efforts to remain calm. If it is any consolation, it was very difficult for > me too, back in 2005. > > I believe the outcome of my post - Adam telling us about Friston - overrides > everything else you've said. Had I not advanced my hypotheses about the > brain, this contact with Adam would not have been established. You would have > undermined my chance to participate in our quest for understanding what we > are, and the chance of Science to advance one more step ahead. > > Sergio. > > > From: Adam Safron [mailto:[email protected]] > Sent: Wednesday, August 15, 2012 9:56 AM > To: AGI > Subject: Re: [agi] Uncertainty, causality, entropy, self-organization, and > Schroedinger's cat. > > You have already acknowledged the fact that the brain uses a lot of energy so > why would you continue to insist that you know exactly how the brain acts to > conserve energy without any experience in the field of neural science? > > Karl Friston's work may be relevant to this discussion: > http://www.fil.ion.ucl.ac.uk/~karl/#_Free-energy_principle > > Best, > -Adam > > On Aug 15, 2012, at 4:49 AM, Jim Bromer <[email protected]> wrote: > > > Sergio, > I am making an effort to try to understand what you are saying. I am also > trying to avoid making personal attacks. However, I have major problems when > someone claims that he has -the answer- when he does not have -the proof-. > So I have been making more personal criticisms about your attitude about your > own theory, not to to win the argument or to personally trounce you, but to > see if you are able to acknowledge that you cannot possibly be certain about > your theory without actually making it do what you say it can do. Once you > acknowledge some serious uncertainty about the theory, or I come to the > conclusion that you are unable to do that, I want to try to figure out what > your theory is about. > > I did not understand this at first, but now I think that you are saying that > the response a person makes in situations where some uncertainty exist, will > be an invariant given those situations. Is that right or is it wrong? > Regardless of the knowledge someone has about what might follow, the response > that a person chooses in the face of uncertainty is one in which the entropy > of the information that the person has about the situation will be minimized > so that the useful information is retained. Is this essentially right? It > should be obvious that this is going to be an imperfect process given that > some situations are more complicated than others. Isn't that right? > > Is it possible that your theory is only a physical-reaction-of-the-brain > response to a problem of overwhelming uncertainty and therefore not a sound > theory derived from insight? > > Two more criticisms. > One is that you are choosing some of the laws of physics while ignoring > others and then claiming that these laws that you have chosen explain how the > brain works. The brain is obviously a complicated organ, so how can you > claim that your choice of abstractions from physics can explain it? > > Secondly. We learn from previous experiences. We learn that we do have > choices. And we learn that many of the choices we have can be made without > immediately threatening our survival. Why aren't my choices based on insight > (right or wrong)? Knowledge that is only derived from the essence of an > abstract system is usually pretty frail. Isn't it possible that the mind is > physical organ capable of dealing with insight and therefore capable of > reacting in ways that are less efficient than your theory is suggesting. You > have already acknowledged the fact that the brain uses a lot of energy so why > would you continue to insist that you know exactly how the brain acts to > conserve energy without any experience in the field of neural science? (I am > not saying that we must not talk about such things, I am only saying that we > cannot honestly claim that our knowledge of the basics of neural science are > absolutely correct.) > > Jim Bromer > AGI | Archives | Modify Your Subscription > > > AGI | Archives | Modify Your Subscription > > > AGI | Archives | Modify Your Subscription ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
