tag:blogger.com,1999:blog-9192597712746432631.post4237670768462600789..comments2024-03-09T09:06:35.288+00:00Comments on Notes from Two Scientific Psychologists: Brains Don't Have to be Computers (A Purple Peril)Andrewhttp://www.blogger.com/profile/16732977871048876430noreply@blogger.comBlogger21125tag:blogger.com,1999:blog-9192597712746432631.post-3428133368339782682017-07-14T16:25:25.062+01:002017-07-14T16:25:25.062+01:00Of course brains do not have to be computers. Brai...Of course brains do not have to be computers. Brains are too advanced to be something simple like computers are. Computer is just a big calculator, human's brain is much more than that.borelioza diagnostykahttp://boreliozaonline.pl/diagnostyka/diagnostyka-boreliozy/noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-4556448066720818372015-08-16T17:10:46.070+01:002015-08-16T17:10:46.070+01:00This comes up a lot. I've started to think tha...This comes up a lot. I've started to think that actually, the reverse should be true; if evolution spends time building one kind of system, it's more likely to simply recruit that system over and over rather than build a new one from scratch.Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-24164032050668519402015-08-15T21:47:51.365+01:002015-08-15T21:47:51.365+01:00This may be a fairly naive suggestion, but it seem...This may be a fairly naive suggestion, but it seems plausible that much of the brain is evolved to work in the non representational way REC posits, while recent neural innovations allow more algorithmic, representational processes. Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-84687047459534295022015-08-13T10:10:17.366+01:002015-08-13T10:10:17.366+01:00This recent book on what the hell computation is m...This recent book on what the hell computation is might be of interest to people: http://philosophyofbrains.com/2015/08/10/is-computation-abstract-or-concrete.aspxAndrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-73714669108310697142015-07-11T10:29:21.042+01:002015-07-11T10:29:21.042+01:00I would like to see an argument that why difficult...<i>I would like to see an argument that why difficult non-linear transformations as posited by Chris Eliasmith are not computational. I mean the ones involved in SPAUN: http://www.sciencemag.org/cgi/doi/10.1126/science.1225266</i><br />SPAUN works on images for it's "vision", so it's already off on the wrong foot. <br /><br />Also I thought this was revealing:<br /><i>A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains.</i><br />Brains are only equally complex to behaviour if they are entirely responsible for behaviour. But they aren't, and anyway complex behaviour can emerge from simple systems. It sounds like SPAUN is just trying to do too much of the work.Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-87009633692232066302015-07-11T10:26:22.223+01:002015-07-11T10:26:22.223+01:00Well, solving the problem computationally is physi...<i>Well, solving the problem computationally is physically the same thing as doing it dynamically in all physical computers </i><br />In computers, sure. But that doesn't mean brains are computers or that their dynamical activity is computational. <br /><br /><i>Again, planimeter is a trivial example: the area is obviously the current state of the wheel, as you stated. But it's a trivially easy computation.</i><br />It's actually not; <a href="http://www3.amherst.edu/~tleise/Planimeter/LinearAndPolarPlanimeters.pdf" rel="nofollow">the maths describing this is actually quite complicated</a>. Planimeters measure the area of complex shapes, where length x width doesn't mean anything. Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-8882034218357677372015-07-10T20:13:06.169+01:002015-07-10T20:13:06.169+01:00Well, solving the problem computationally is physi...Well, solving the problem computationally is physically the same thing as doing it dynamically in all physical computers Computers are physical systems that evolve in time, and some of them are quite strange (analog/digital for example). For a deeper look on the connection between dynamics and computation, see here: https://global.oup.com/academic/product/computation-dynamics-and-cognition-9780195090093?cc=pl&lang=en&<br /><br />Note that computation need not be symbolic: analog neural networks (sometimes realized on neuromorphic chips) are not symbolic but they're part and parcel of current computational neuroscience.<br /><br />Again, planimeter is a trivial example: the area is obviously the current state of the wheel, as you stated. But it's a trivially easy computation.<br /><br />I would like to see an argument that why difficult non-linear transformations as posited by Chris Eliasmith are not computational. I mean the ones involved in SPAUN: http://www.sciencemag.org/cgi/doi/10.1126/science.1225266Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-18676079629191999912015-07-10T17:26:04.961+01:002015-07-10T17:26:04.961+01:00I mean accurately. Solving a problem computational...I mean accurately. Solving a problem computationally and dynamically is two different things and involve different components doing different things. If your system is dynamical but your description is computational all your science on the system will ask the wrong questions, eg where in the planimeter is area calculated?Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-36312466855004086362015-07-08T11:30:46.347+01:002015-07-08T11:30:46.347+01:00And let me be clear how I think the design can be ...And let me be clear how I think the design can be used to decide such questions. Imagine that someone says that the vacuum cleaner is supposed to compute random number by rotating the particles of dust. Now, of course, the vacuum cleaner doesn't have the function to do so, and rotating particles of dust inside the cleaner is not essential to the capacity of the cleaner to clean, though it is one of the ways to build vacuum cleaners. However, there is much more in the cleaner, such as pipes, brushes, suction nozzles, which are not explainable with the hypothesis that what it's doing is computing random numbers. As for the planimeter, are there any parts that do not contribute to getting the value that corresponds to the volume of the area? If I were compare it to other measuring devices, such as a standard ethanol thermometer, it would be clear that the thermometer computes, at best, a very trivial computation, and it's something like an identity transformation. These computations are trivial and not so informative about the design of these devices, so adding a computational gloss to the mechanistic explanation of such devices may be counterproductive: no new explanatory or predictive power. But for neural systems, the situation is different; they do compute very complex transformations, and have immense computational powers (at least in, say, Eliasmith's account of neural ensembles).Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-68132270716844718542015-07-08T11:19:05.622+01:002015-07-08T11:19:05.622+01:00The design is important, but look at Dershovitz...The design is important, but look at Dershovitz's and Gurevich on algorithms in constructive proofs in geometry. These are step-by-step procedures, and some people argue that they operate on incomputable reals.Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-10126174963654056332015-07-08T11:15:44.565+01:002015-07-08T11:15:44.565+01:00Well, there is one important reason: there are mod...Well, there is one important reason: there are models of computation that accommodate both analog and digital computing, notably Abstract State Machines. Mathematically, this difference does not change much. See this paper for example: http://link.springer.com/chapter/10.1007/978-3-642-29952-0_49<br /><br />Basically, this work is advanced by one of leading figures in computability, Nachum Dershovitz, and based on Yuri Gurevitch's very important work on evolving algebras (=abstract state machines).Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-89117984815612575102015-07-08T11:12:51.591+01:002015-07-08T11:12:51.591+01:00Computers do just change state; but they change st...Computers do just change state; but they change state so as to implement an algorithm. The planimeter changes state, but not so as to implement any algorithm. It's behaviour can be <i>described</i> with an algorithm, but that's not what it was designed to do. Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-34586223462495734622015-07-08T11:11:18.927+01:002015-07-08T11:11:18.927+01:00Analog computing does seem like an oxymoron. I rem...Analog computing does seem like an oxymoron. I remember learning about the antikythera and being really surprised that people called it a computer. <br /><br />I would hazard a guess that computability theorists call this computing for reasons of convenience and history, rather than anything more fundamental.Andrewhttps://www.blogger.com/profile/16732977871048876430noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-25044872414849507722015-07-07T15:53:46.858+01:002015-07-07T15:53:46.858+01:00Information, feedback, dynamic systems are all ver...Information, feedback, dynamic systems are all very general terms that apply to lots of physical systems. But this does not make them void nor metaphorical. There's nothing metaphorical about neural computation in Izhikevitch's models of neurons, for example.Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-72169383656881220342015-07-07T15:52:19.566+01:002015-07-07T15:52:19.566+01:00Well, if computability theorists call this 'co...Well, if computability theorists call this 'computation', why is this not computation? It's a whole field of enquiry, and lots of machines are analog computers. I don't like gerrymandering the terminology; let's use the terms as the general public does. And there's a lot of analog computers out there: https://en.wikipedia.org/wiki/Analog_computer<br />Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-66588158989079198232015-07-07T14:41:36.498+01:002015-07-07T14:41:36.498+01:00I think you might be right about the problem of de...I think you might be right about the problem of defining computation. But is there any value in defining it so loosely that any type of transformation is called computation? The term seems to lose any meaning at all, and if so, then it has no usefulness as a description of what the brain does. At best, computation is only a metaphor for what the brain does. It has served some utility as a metaphor, primarily in driving a lot of cybernetic-style cognitive psychology up through the 1980s, but I get the feeling that as a metaphor it has outlived its usefulness and we need a new metaphor. Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-78398191059827150542015-07-07T14:37:45.697+01:002015-07-07T14:37:45.697+01:00I feel like "analog computation" is an o...I feel like "analog computation" is an oxymoron. ?? A system can respond to some state of affairs outside of itself, and if it does so by analogy, is that computation? For example, in an old fashioned speedometer, a speed sensor responds by sending a voltage. The voltage is proportional to the speed of the car. The voltage drives a needle on a display. While this "output" (the display) is a transformation of the "input" (the speed of the car) where is the computation? What has been computed? If the brain responds to the world in a similar way - as something that measures certain properties in the world by analogy, of what need is there to invoke computation? Some might say that a planimeter is an analog computer but they would be wrong. It is a meter. It measures, it does not compute.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-24722555197748578612015-07-07T13:54:12.657+01:002015-07-07T13:54:12.657+01:00"[I]f you describe its activity computational..."[I]f you describe its activity computationally, you will not have accurately described how it produces area from the act of tracing" <br />"Accurately"? Or do you mean "completely"? What you might be getting at is that the computation of a device like a planimeter, or alternatively a physiological component like a brain region, is what we might otherwise call its behavior; i.e., the relationships between a system's inputs and outputs. This is a different "level of understanding" (to use Marr's terminology) as the algorithm by which that behavior is accomplished, or the material by which the algorithm is implemented. These levels, including the physics of the material itself, can in turn be viewed according to behavior, algorithm, and implementation. Your point about what level is most useful for understanding a system will depend on what exactly you mean by understanding, and what you want to do with the knowledge (treat illness? create artificial devices?). These topics have been taken up by far smarter people than I, at least a few of whom are cited in this article: http://www.ncbi.nlm.nih.gov/pubmed/25256163Nathan Inselhttp://ninsel.netnoreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-5896830657596349592015-07-07T12:58:44.140+01:002015-07-07T12:58:44.140+01:00Thanks for the post Andrew. How would you rigorous...Thanks for the post Andrew. How would you rigorously define a ‘computation’? Could we not describe a computer in a similar way to the planimeter? Say I ask the computer to add 1+1. To do this the computer has no idea what a number is. It simply changes state in a way that is governed by physical laws. I use the change in state to represent addition. But this is an abstract concept in my head, not one built into the computer. Nothing was ‘added’ in the hardware. If you described the computer as ‘adding’ something you would not have a good description of what the computer actually did. Different processors implement the same ‘computation’ in physically different ways. A description of one would not be a good description of the other. <br />However, it is useful to say the computer added two numbers, even though it didn’t, because the change in state of the computer corresponded to the abstract representation of something in my head. I think your description of a planimeter works very well for my computer: A dynamical system with a particular composition, organisation and calibration, and it’s the time-extended activity of this system in the context of an appropriate task that produces functional behaviour in smart fashion.<br />But then my computer isn’t performing computations? But by definition it is…. So maybe our problem lies in defining what a computation is? <br />Oscar Gilesnoreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-10063645070602937462015-07-07T11:59:52.556+01:002015-07-07T11:59:52.556+01:00Ah, and one more thing. If your description of a p...Ah, and one more thing. If your description of a process as computational is true, then the process is computational. Otherwise, such a description is not to be taken literally, but it's note *mere* description. If it's a useful false description, it's probably idealization. But is that really the case? I don't think so, as requirements used to talk of computation in the case of physical computers are exactly the same in computational neuroscience as in the field of biological computation in DNA computing. Marcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.comtag:blogger.com,1999:blog-9192597712746432631.post-78802714535226669442015-07-07T11:57:02.268+01:002015-07-07T11:57:02.268+01:00You are conflating computation and digital computa...You are conflating computation and digital computation. There is a branch of computability theory that deals with analog computation, and some of these people would say that planimeters are analog computers. Do you think they are confused to think so?<br /><br />Of course, input-output mapping is not enough. This is merely weak equivalence, and we need to know the structure of the process, too. See my paper for more detail:<br /><br />https://www.academia.edu/1825490/Is_the_mind_a_Turing_machine_How_could_we_tell<br /><br />For more details and complete story, see my 2013 book:<br /><br />https://mitpress.mit.edu/books/explaining-computational-mindMarcin Miłkowskihttps://www.blogger.com/profile/11617540925216664775noreply@blogger.com