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<p class="MsoNormal">Fred,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I have heard your arguments before. That’s a fairly standard argument. You are ignoring neuroscience evidence. Remember that there is a long history of single cell recordings. And some of those studies led to Nobel prizes. Here’s a list
of some from Wikipedia: <a href="https://en.wikipedia.org/wiki/Single-unit_recording">
Single-unit recording - Wikipedia</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="color:#202122;mso-list:l0 level1 lfo1;background:white">
1928: One of the earliest accounts of being able to record from the nervous system was by <a href="https://en.wikipedia.org/wiki/Edgar_Adrian" title="Edgar Adrian"><span style="color:#0645AD">Edgar Adrian</span></a> in his 1928 publication "The Basis of Sensation".
In this, he describes his recordings of electrical discharges in single nerve fibers using a <a href="https://en.wikipedia.org/wiki/Lippmann_electrometer" title="Lippmann electrometer"><span style="color:#0645AD">Lippmann electrometer</span></a>. He won the
<span style="background:yellow;mso-highlight:yellow">Nobel Prize in 1932</span> for his work revealing the function of neurons.<sup><a href="https://en.wikipedia.org/wiki/Single-unit_recording#cite_note-11"><span style="color:#0645AD">[11]</span></a></sup><o:p></o:p></li><li class="MsoNormal" style="color:#202122;mso-list:l0 level1 lfo1;background:white">
1957: <a href="https://en.wikipedia.org/wiki/John_Carew_Eccles" title="John Carew Eccles"><span style="color:#0645AD">John Eccles</span></a> used intracellular single-unit recording to study synaptic mechanisms in motoneurons (for which he won the
<span style="background:yellow;mso-highlight:yellow">Nobel Prize in 1963</span>).<o:p></o:p></li><li class="MsoNormal" style="color:#202122;mso-list:l0 level1 lfo1;background:white">
1959: Studies by <a href="https://en.wikipedia.org/wiki/David_H._Hubel" title="David H. Hubel"><span style="color:#0645AD">David H. Hubel</span></a> and <a href="https://en.wikipedia.org/wiki/Torsten_Wiesel" title="Torsten Wiesel"><span style="color:#0645AD">Torsten
Wiesel</span></a>. They used single neuron recordings to map the visual cortex in unanesthesized, unrestrained cats using tungsten electrodes. This work won them the
<span style="background:yellow;mso-highlight:yellow">Nobel Prize in 1981</span> for information processing in the visual system.<o:p></o:p></li><li class="MsoNormal" style="color:#202122;mso-list:l0 level1 lfo1;background:white">
<span style="color:black">Moser and O’Keefe Nobel prize (grid and place cells): <span style="background:yellow;mso-highlight:yellow">
<a href="https://www.nobelprize.org/prizes/medicine/2014/press-release/">The 2014 Nobel Prize in Physiology or Medicine - Press release</a></span></span><span style="background:yellow;mso-highlight:yellow"><o:p></o:p></span></li></ul>
<p class="MsoNormal" style="margin-left:.5in;background:white"><span style="color:#202122"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122">So you are questioning some ground-breaking work in this area. You might have an argument with the Nobel committee also. But take a hard look at the single-cell experiments first before
you pick a fight with them.<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122">By the way, the concept cell findings were verified with repeated experiments, something you expect in science. Take a look at the Reddy and Thorpe paper if you have not done so yet.
Plus, bear in mind the Nobel prizes are not given on the basis of some fluke experiments. So, you are, in effect, arguing against the findings of Hubel and Wiesel and others.
<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:#282828;background:#F7F7F7">Reddy, L., and Thorpe, S. J. (2014). Concept cells through associative learning of high-level representations. <i>Neuron</i> 84, 248–251. doi: 10.1016/j.neuron.2014.10.004<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#202122"><o:p> </o:p></span></p>
<p class="MsoNormal">All the best,<o:p></o:p></p>
<p class="MsoNormal">Asim Roy<o:p></o:p></p>
<p class="MsoNormal">Professor, Information Systems<o:p></o:p></p>
<p class="MsoNormal">Arizona State University<o:p></o:p></p>
<p class="MsoNormal"><a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=" target="_blank">Lifeboat
Foundation Bios: Professor Asim Roy</a><o:p></o:p></p>
<p class="MsoNormal"><a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=" target="_blank">Asim
Roy | iSearch (asu.edu)</a><span style="color:#202122"><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<div>
<div style="border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0in 0in 0in">
<p class="MsoNormal"><b>From:</b> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu>
<b>On Behalf Of </b>Rothganger, Fred<br>
<b>Sent:</b> Thursday, February 22, 2024 1:58 PM<br>
<b>To:</b> connectionists@mailman.srv.cs.cmu.edu<br>
<b>Subject:</b> Connectionists: Grandmother neurons<o:p></o:p></p>
</div>
</div>
<p class="MsoNormal"><o:p> </o:p></p>
<div>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">Electrical recordings are an extremely sparse sample of the vast number of neurons in a human brain (or even rodent brain). Perhaps the following paper will help
illustrate the level of caution we should exercise in interpreting the roles of particular recorded neurons:<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p> </o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><a href="https://urldefense.com/v3/__https:/doi.org/10.1371/journal.pcbi.1005268__;!!IKRxdwAv5BmarQ!Zj7aotyhoDkFdDrww9wsD967R0kDbKTRMcQc1ik-bqde5ELbAchRzLF3HYMStO7wp5jdSMrIyBrHOtzUNCaGiLk$">https://doi.org/10.1371/journal.pcbi.1005268</a></span><o:p></o:p></p>
</div>
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<p class="MsoNormal"><o:p> </o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">Just to offer my humble opinion on other topics in this thread:</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">* It is more likely that any given neuron is functioning as part of a larger ensemble rather than acting alone to represent some concept. That ensemble is more likely
to be a dynamical system, and its "representations" are regions or attractors in the state space of the overall system.</span><o:p></o:p></p>
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<div>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">* Another way to frame "meaning" is the actions a physical system, such as an animal, takes in the world. Neural states have meaning because they have a direct causal
relationship with our body, which in turn has a causal relationship with the environment around us. Specific example: my act of emitting a certain pattern of sounds may change your behavior, which may result in me receiving some food. (Ordering dinner at a
restaurant.)</span><o:p></o:p></p>
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