Kybernetes I, NEURON: the neuron as the collective Lance Nizami, Article information: To cite this document: Lance Nizami, (2017) "I, NEURON: the neuron as the collective", Kybernetes, https://doi.org/10.1108/ K-09-2016-0265 Permanent link to this document: https://doi.org/10.1108/K-09-2016-0265 Downloaded on: 13 November 2017, At: 11:24 (PT) References: this document contains references to 100 other documents. To copy this document: permissions@emeraldinsight.com Access to this document was granted through an Emerald subscription provided by Token:Eprints:WIJMBCKUWXVE56AG3XFH: For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. 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The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) I, NEURON: the neuron as the collective Lance Nizami Independent Research Scholar, California, USA Abstract Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists. Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized. Findings – Anthropomorphization arose because single neurons were cast as "observers" who "identify", "categorize", "recognize", "distinguish" or "discriminate" the stimuli, using math-based algorithms that reduce ("decode") the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without "decoding", there is supposedly no perception. However, "decoding" is both unnecessary and unconfirmed. The neuronal "observer" in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective. Research limitations/implications – Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect. Practical implications – A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches. Originality/value – A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain. Keywords Consciousness, Anthropomorphization, Observer, Decoding, Neuron, Reductionism Paper type Research paper 1. Introduction The anthropomorphization of the neuron is defined as the casting of the neuron as a human being. As such, the neuron hypothetically does things that are done by awake, alert and behaving human beings. Here, "behaving" implies conscious choices, not mere reflexes. The anthropomorphization of parts of the human body undoubtedly began in ancient times. However, the modern anthropomorphization of some or all of the sensory nervous system has an identifiable origin, namely, "What the frog's eye tells the frog's brain" (Lettvin et al., 1959). In this alleged conversation, a behavioral output (speech), which might be encouraged by sensations (here, the percepts that are images), in fact replaces the voltage spikes that triggered those selfsame images. This rearrangement of cause-and-effect is laughable. And yet it is taken seriously and is pervasive; among the 2,170þ citations of Lettvin et al. (1959) (GoogleScholar) – well-above the 100-count that already represents above-average popularity in the field – consider "What the frog's eye tells the human brain" The author sincerely thanks Dr Claire S. Barnes PhD for her valuable help and the two anonymous Reviewers for their thought-provoking suggestions. Neuron as the collective Kybernetes © EmeraldPublishingLimited 0368-492X DOI 10.1108/K-09-2016-0265 The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0368-492X.htm D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) (Weisstein, 1969), "What the frog's eye tells the monkey's brain" (Humphrey, 1970), "What the retinal ganglion cells tell the visual cortex" (van Rullen and Thorpe, 2001), "What the frog's nose tells the frog's brain" (Restrepo, 2004), "How much the eye tells the brain" (Koch et al., 2006; original italics) and "What the [rodent's] whiskers tell the tactile brain" (Estebanez et al., 2014). Such titles perpetuate the liberties taken by Lettvin et al. (1959). Anthropomorphization is a form of metaphor, i.e. a literary device. Anthropomorphization also represents a lack of scientific distinction. At first, this might be blamed upon laziness. But anthropomorphization is a form of Reductionism (more on this below), and it connotes shades of religious belief, in particular the puerile, unsupported notion that individual neurons are conscious (animism). Hence anthropomorphization distracts from other, more credible explanations for consciousness and the mind, as will be shown. How can we successfully abandon anthropomorphization? To do so, we must first understand why it persists, which in turn requires understanding how it entered the literature in the first place. To those ends, the present work scrutinizes more than 90 central peer-reviewed sensory-neuroscience articles, many of them highly cited, taken from respected sources in a literature which extends to thousands of documents. Sometimes quotations are used to capture the original flavor of the work, as well as to avoid any accusation of misrepresentation. The "present tense" is used, also, because the subject matter remains fresh. Some unexpected insights arise from this critical examination. 2. The decision-making, criterion-holding neuron The investigation formally begins not with Lettvin et al. (1959), but nonetheless in the same year, with Professor Theodore H. Bullock's influential contribution to the seminal 1959MIT Symposium on Principles of Sensory Communication (published in 1961, as Sensory Communication; re-printed in 2012; countless citations). Bullock (1961, p. 718) imagines a population of higher-level neurons – that is, neurons perhaps closer to the brain than to the eye, ear, nose, tongue or skin – in which each neuron is a "decision" unit. That is, each neuron is "like a military general" (Bullock, 1961, p. 718), insofar as it must "deliver an unequivocal answer" about whether some "criterion" is met for further neuronal action (Bullock, 1961, p. 718) (an action, for example, such as activating a physical movement). To Bullock (1961, p. 719), "Every neuron is a decision-making element when it changes from a silent to an impulse–firing state" (Bullock, 1961, p. 721; italics supplied). Again, according to Bullock, this represents "recognition of a predetermined criterion" (Bullock, 1961, p. 724). Figure 1 represents a typical sensory-neurophysiology experiment. Stimuli are applied to a test animal by the neuroscientist/experimenter. The animal's receptors (e.g. cone photoreceptor cells in the eye or inner-hair-cells in the ear) each synapse with (i.e. electrically connect to) one or more parallel "primary afferent" neurons, which produce a train of voltage spikes which follow along subsequent afferent (i.e. brain-going) neurons, in a chain leading to the brain. Any of those neurons may be "tapped" using an electrode to record the voltagespike train for examination. Remarkably, Bullock's (1961) "recognition of a predetermined criterion" by single neurons is still proselytized (Barlow, 1994). The notion even infiltrates books that promote "systems views" of biology (for example, Miller, 2015, p. 115). Indeed, Bullock's notion appears often in the sensory-physiology literature, reworded as "discriminating" and/or "distinguishing" (Werner and Mountcastle, 1965 [490þ citations], p. 382; Darian-Smith et al., 1968; Walløe, 1970; Kenton and Kruger, 1971; Hannam and Farnsworth, 1977; Golomb et al., 1994; Heller et al., 1995 [190þ citations], p. 192; Gnadt and Breznen, 1996; Buracas et al., 1998 [370þ citations]; Gehr et al., 2000; Averbeck et al., 2003; Chacron et al., 2003, p. 804; Osborne et al., K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) 2004 [120þ citations]; Faghihi et al., 2013, p. 5). For example, a neuron "distinguishes the current stimulus from other candidates" (Arabzadeh et al., 2004 [130þ citations], p. 6011); more specifically, "Single auditory neurons rapidly discriminate conspecific communication signals" (Machens et al., 2003, p. 341). 3. The detective, informant, journalist, grandmother, messenger, marketer or signalman The anthropomorphization of the neuron has many forms. The neuron "recognizes" (Werner and Mountcastle, 1965, p. 383). The neuron "detects", as if a detective (Crick and Koch, 2003 [1,270þ citations], p. 120; Sadeghi et al., 2007, p. 779) or (in a similar vein) is said to "identify" (Darian-Smith et al., 1968; Kenton and Kruger, 1971; Gochin et al., 1994 [160þ citations], p. 2327; Rolls et al., 1996 [120þ citations]; conveys "levels of information about stimulus identity" in Chechik et al., 2006 [170þ citations], p. 361). To Saal et al. (2009, p. 8029), the neuron is "highly informative", as if an informant. The neuron "reports", as if a journalist (Arabzadeh et al., 2004, p. 6011; for the whole retina in Warland et al., 1997 [380þ citations], p. 2336, after the style of Lettvin et al., 1959). Neurons are said to "discover" (Gütig, 2016). Neurons can even be "grandmotherish" (Skaggs et al., 1992 [320þ citations], p. 1031), after another notion from the Lettvin laboratory (Barlow, 1994), namely, the "grandmother neuron". The notion from Lettvin et al. (1959) of conversing neurons permeates the literature. The neuron "tells" (Naka and Sakai, 1993, p. 75; Rieke et al., 1997 [2,880þ citations], p. 102). It "communicates" (Passaglia and Troy, 2004, p. 1227; e.g. "to other neurons" in Rolls et al., 2010, p. 23). That is, "Neurons must communicate voltage signals to one another through their connections (synapses)" (Burton, 2000/2008, p. 25; italics supplied); indeed, the eye's retinal ganglion cells are, after Lettvin et al. (1959), "communicating the incoming light pattern to the cortex and beyond" (Baddeley, 2000/2008, p. 5). Notwithstanding what might Figure 1. Schematizes the kind of experiments that Lettvin et al. (1959) and Bullock (1961) and their successors performed N e u r o n E x p er i m e nt e r Test Animal Spike t ra inSt imul i E l e c t r o n i c r e c o r d e r R e c o r d i n g e l e c t r o d e S e n s o r y r e c e p t o r ( e n c o d i n g i n t o s p i k e t r a i n ) S p ik e t r a i n Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) be "beyond" the cortex (e.g. neuronal feedback loops?), an afferent sensory neuron generally "communicates information" (Atick and Redlich, 1990 [480þ citations], p. 311) or it "conveys information" (MacKay and McCulloch, 1952 [340þ citations]; Walløe, 1970, p. 746; Tolhurst, 1989, p. 409; Gochin et al., 1994; Rieke et al., 1997, p. 101; Arabzadeh et al., 2006 [130þ citations], p. 9217; Victor, 2006; Saal et al., 2009, p. 8022), or it "carries information" (McClurkin et al., 1991 [140þ citations]; Heller et al., 1995; Arabzadeh et al., 2004, p. 6012; Rolls et al., 2010, p. 23). But to emphasize the actual information carrier: "A train of spike discharges contains and communicates complex information to the brain" (Naka and Sakai, 1993, p. 78; italics supplied). The spike train ostensibly contains messages (Optican and Richmond, 1987 [390þ citations]; van Rullen and Thorpe, 2001 [410þ citations]). Of course, messages are a tool of propaganda, and neurons manipulate information (Victor, 2006, p. 313) – as if a marketer or a propagandist. How exactly is "information" to be "communicated" if the distances are long? The answer is that the neuron "signals", as if a signalman on a train, aircraft or sailing-vessel (Werner and Mountcastle, 1965, p. 395; Kenton and Kruger, 1971; Hannam and Farnsworth, 1977; Naka and Sakai, 1993, p. 76; Averbeck et al., 2003, p. 145; Passaglia and Troy, 2004, p. 1217; Aldworth et al., 2012, p. 6; for cortical stages, see Tovée et al., 1993 [300þ citations], p. 648). Figure 2 shows the neuron in some of its many roles. 4. Interim summary: I, Neuron Individual neurons have been cast as human beings. Neurons allegedly decide, judge, recognize, categorize, detect, distinguish and/or discriminate. Neurons are military generals, detectives, informants, journalists, grandmothers, messengers, marketers or signalmen. Why did these attributions occur? Figure 2. The neuron (here, one more-typical of the brain), acting in its many roles K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) 5. Understanding anthropomorphization: introducing the observer of the neuronal spike train In understanding the reasons underlying the anthropomorphization of the neuron, the most convenient place to start is not at the historical beginning of neurophysiological recordings (roughly a century ago) but rather with MacKay and McCulloch (1952). MacKay and McCulloch explored a central tenet of neuroscience, namely, that if a single neuron's rate of firing of voltage spikes is sensitive to some measurable characteristic of the stimulus then that respective firing rate (or something derived from it) may reflect that respective stimulus characteristic. An example of "stimulus characteristic" is the intensity of a sinusoidal soundpressure-wave (a pure tone), represented in the voltage-spike-firing rates of the most peripheral afferent auditory neurons (Nizami, 2002, 2003, 2005a, 2005b, 2014, 2015a, 2015b; Nizami and Schneider, 1997, 2000). MacKay and McCulloch (1952, p. 132) seek to infer "the number of possibilities distinguishable by the receiver". The word "receiver" appears importantly elsewhere as "central detector" (Werner and Mountcastle, 1965, p. 388, p. 392). More importantly yet, the receiver/detector appears also as the observer. For example, Heller et al. (1995, p. 188) ask, "How much could be determined about the stimulus by observing the response for a specific amount of time?" (italics supplied), supposing that "The messages transmitted by single neurons need to be read" (Heller et al., 1995, p. 188) so that stimulus features "can be discriminated on the basis of a neuron's response" (Heller et al., 1995, p. 192). In Lass and Abeles (1975), the observer examines the spikes arriving at the (colloquially) "downstream" end of any single neuron of the frog's sciatic nerve, and "tries to deduce" (Lass and Abeles, p. 61) what "upstream" spike-evoking electrical impulses were applied (by the experimenters). Van Rullen and Thorpe (2001, p. 1257) echo Lass and Abeles (1975): "We take the position of an imaginary observer 'listening' to the pattern of spikes coming up the optic nerve and trying to derive information about the input image" (original internal quotation marks). Likewise, Arabzadeh et al. (2006, p. 9216) mention "an observer who 'reads off' the spike train signals" (original internal quotation marks). Victor (2006), a math modeler, summarizes many ways in which "an observer of the neural response" (Victor, 2006, p. 304) allegedly "derives information". Unfortunately, Victor and other authors fail to mention who/what is the observer. Hence, the following analysis. 6. Understanding anthropomorphization: the observer "decoding" 6.1 "Decoding" or "reconstruction" or "estimation" of the stimulus In an influential early paper, Stein (1967 [230þ citations], p. 824) declares that "information is decoded" (italics supplied). "Decoding" is also called "reconstruction". Regardless of name, it ostensibly takes "information" and makes it "available to higher order neurons" (Stein, 1967, p. 824). That is, reconstruction is by neurons for neurons. There is much to say about this. Arabzadeh et al. (2004, p. 6015), echoing Stein, assume that "Decoding must occur for the successful transmission of information from one group of neurons to a second group". Reconstruction is no passing fad; Rogers et al. (2001, p. 448) declare that accurate reconstruction is "The central problem for the neurophysiologist". If so – given a chain of neurons, leading from a sensory receptor cell up into the brain – what is ultimately reconstructed, and why? MacLeod et al. (1998 [270þ citations], p. 693) explain (original internal quotation marks): Our inferences about brain mechanisms underlying perception rely on whether it is possible for the brain to "reconstruct" a stimulus from the information contained in the spike trains from many neurons. Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) So, it is the stimulus that is ultimately reconstructed! As Zhang et al. (1998 [440þ citations], p. 1017) declare (italics supplied): Reconstruction is a useful strategy for analyzing [the] data recorded from populations of neurons, in which external physical variables such as the orientation of a light bar on a screen, the direction of hand movement in space, or the position of a freely moving animal in space are estimated from brain activity. So, at the brain, "decode" and "reconstruct" are effectively synonymous with "estimate". Or identify, or recognize, or categorize, or distinguish, or discriminate or whatever. But these are anthropomorphic capabilities, and further, they are often assigned to single, often peripheral, neurons, which would altogether leave the brain with nothing to do. This issue is important and will be revisited later. Meanwhile, the brain is kept busy by Seriès et al. (2009, p. 3273), who simplify decoding to a two-step process: We assume that perception can be described using an encoding-decoding cascade. The encoding stage represents the transformation between the external sensory stimuli and the activity of a population of neurons in sensory cortex, while the decoding stage represents the transformation from that activity to a perceptual estimate. The meaning of "perceptual estimate" was left unexplained; the implication (there and elsewhere), however, is that perception is estimation. The idea of the senses as "estimators" recurs frequently in the literature (for many examples, see Baddeley, 2000/2008). Figure 3 schematizes "reading" and "decoding" according to neuroscientists. 6.2 "Estimates" by observers: neuron(s) observing neurons How is "estimation" done? Rieke et al. (1995 [270þ citations], p. 263) represent long-standing assumptions when they ask "how accurately an observer of the spike train in a single [sensory] afferent can estimate the sensory stimulus" (italics supplied). So, the estimator is the observer. But the observer is where, or whom, or what, in practicality? Bialek et al. (1991 [1,020þ citations], p. 1854) answer: "All of an organism's information about the sensory world comes from real-time observation of the activity of its own neurons". To repeat: realtime observation of the activity of its own neurons. Again, reconstruction is by neurons for neurons. But which neurons observe which? For flies, the motion-sensitive neurons of the eye – or the neurons that they connect to, brain-ward – can "read" a hypothetical "neural code" (Bialek et al., 1991, p. 1855), making them observers (of neural codes). Figure 3. "Reading" and "decoding" (see text and compare to Figure 1), which allows the animal to "estimate the stimulus". Such estimates are, of course, not the actual stimuli (which are flashes of light, sound-pressure waves, etc.) St imul i Test Animal R e a d i n g a n d d e c o d i n g Est imated St imul i N e ur o n S e n s o r y r e c e p t o r ( e n c o d i n g i n t o s p i k e t r a i n ) S p i k e t r a i n K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) Observation of neurons and decoding by neurons has been promoted well into the twentyfirst century (DiCaprio et al., 2007; Aldworth et al., 2012; So et al., 2012). Averbeck et al. (2003, p. 143) spoke of "how many levels of a behavioral factor [. . .] can be discriminated by the activity of single neurons or small ensembles [i.e. through decoding]" (italics supplied). Arabzadeh et al. (2004, p. 6015) echo Bialek et al. (1991) when they wonder "how the response of a barrel cortex neuronal population [i.e. in a rat's brain] could be most efficiently decoded, or 'read off', by the target neurons" (original internal quotation marks). The "target neurons" were not named – which is not surprising, given that barrel-cortex neurons are found at the cortex, hence being "target neurons" themselves. Passaglia and Troy (2004, p. 1217) echo Lettvin et al. (1959) when they declare that "Retinal ganglion cells [. . .] encode their messages with trains of optic nerve impulses [i.e. spike trains], which target neurons in the brain must decipher in a reliable and timely manner" (italics supplied). 7. Neuron as observer as neuroscientist 7.1 "Estimates" as guesses by observers How is decoding done, in practicality? Bialek et al. (1991) tell us that neurons "read" a hypothetical "neural code" by means of "building a (generally nonlinear) filter that operates continuously on the spike train to produce a real-time estimate of the unknown stimulus waveform" (Bialek et al., 1991, p. 1855). That is quite something, and of course it is what Bialek et al. (or their assistants) do in the laboratory to produce "perceptual estimates". Bialek et al. presume that individual neurons, or the whole brain itself, do what scientists accomplish altogether as a laboratory collective, with the indispensable support of an entire society. The Bialek et al. presumption is typical of the literature. For example, Walløe (1970, p. 747) states that "The decoder receives a train of impulses and makes estimates about the mean frequency [i.e. of spike firing in the neuron]", but on his next page Walløe reveals his mean-frequency equations, thereby revealing himself as the actual decoder. Osborne et al. (2004, p. 3210, p. 3218) speak of what neurons can discriminate, but they betray who does the discriminating when they mention "the average extent to which the neural response allows us to identify the stimulus" (p. 3212; italics supplied). Similarly, Seriès et al. (2009, p. 3275) refer to an "aware decoder", the word "aware" suggesting one or more of the Seriès et al. staff. The literature abounds in such examples. Reconstruction algorithms, such as those used by Bialek et al. (1991), are based upon mathematical models. The models are varied, numerous and too complicated to describe here; Victor (2006) provides a long review, one which only partially covers the gamut. This plethora suggests some underlying uncertainty on the part of the authors – and indeed, "The intrinsic neuronal code that carries visual information and the perceptual mechanism for decoding that information are not known" (McClurkin et al., 1991, p. 675). Nor are they known for any other senses. Hence, "perceptual estimates" are man-made guesses. This is rarely admitted and slyly so. For example, Alkasab et al. (1999, p. 103) express their desire to quantify "how much better than chance an observer could guess the stimulus" from neuronal spike trains. Likewise, Wiener et al. (2001, p. 8215) wonder "how well an observer can guess which stimulus elicited any particular observed [neuronal] response". Similarly, Nelken et al. (2005 [130þ citations], p. 200) use a putative decoder that "receives a spike train as input and guesses which stimulus evoked the spike train". Recall from above "the perceptual mechanism for decoding" (McClurkin et al., 1991, p. 675), namely, the observer who makes guesses – who is the experimenter (Nizami, 2014, 2015a). Consider that the "receiver" of MacKay and McCulloch (1952), for example, is actually one or both of MacKay or McCulloch – whomever observed and analyzed the recorded spike trains. In Lass and Abeles (1975), likewise, the "observer" is one or both of Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) Lass and Abeles. If this point remains unclear, consider a few simple word-substitution exercises. Chechik et al. (2006, p. 359) declare that "Groups of neurons interact to code information about the stimuli". The passage clarifies if the word "neurons" is replaced by the phrase "graduate students or postdocs". Chechik et al. (2006, p. 359) further state that "Neurons at higher processing stations may become largely independent to allow for easier readout". Now replace "neurons at higher" by "laboratory staff at". Chechik et al. (2006) are echoed by Ciocchi et al. (2015, p. 560), who state that some particular part of the brain "computes diverse information" and then sends "computations transmitted [sic]" to various brain "targets". Imagine now replacing brain by "laboratory staff" and "brain targets" by "assistants". Figure 4 schematizes the reality of "reading" and "decoding". The anthropomorphized neuron that performs "decoding" effectively becomes all of society, all "observers". Figure 5 shows this relation. The experimenter/observer is not separable operationally from the greater collective of observers that is the laboratory personnel. The laboratory, likewise, is not operationally separable from the greater society in which it is embedded. When the neuron is cast as "observer", it effectively becomes the universe of humans surrounding itself. 7.2 Why "decode" at all? Like many, Zhang et al. (1998, p. 1037) ponder "which reconstruction algorithm is actually used by the biological system". Like many, however, they avoid pondering an even more important question, namely, why any reconstruction algorithm "is actually used by the biological system". Poor writing in the literature makes things worse. Consider, altogether, the following passage: Figure 4. The reality of "reading" and "decoding" (compare to Figures 1 and 3) N e ur o n Test Animal Spike t ra inSt imul i E x p e r i m e nt e r / O b se r ver R e a d i n g a n d d e c o d i n g E l e c t r o n i c r e c o r d e r R e c o r d i n g e l e c t r o d e S e n s o r y r e c e p t o r ( e n c o d i n g i n t o s p i k e t r a i n ) S p i k e t r a i n K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) Of course, we do not consider the role of the visual system in general as being to reconstruct the image in the brain. Rather, this reconstruction [i.e. in the laboratory] should be seen as a form of benchmark – a test of the potential of a particular code (van Rullen and Thorpe, 2001, p. 1257). Note well the vagueness of "image". Is it the actual perceived object or the percept in the mind, i.e. the "seeing" of the object? The first choice amounts to the actual generation of the stimulus by the looking organism, which is altogether ludicrous (to everyone except Kenneth Howard Norwich; as described in Nizami, 2009 and Winer et al., 2002). However, if "image" refers to the percept, which is what the brain has already "constructed", then why "reconstruct" it? To-date, there is no empirical evidence that the brain "decodes" anything, or that it even needs to (Nizami, 2014, 2015a). As Naka and Sakai (1993, p. 79) state, with respect to their animal research subject and the manner in which they (and others) have quantified retinal spike trains, "The catfish has no notion of 'multiplexed signals' or of 'cross-correlation'; these notions are ours". If, indeed, reconstruction refers to the computations done by the experimenters, how is the experimenters' evaluation of "the potential of a particular code" (van Rullen and Thorpe, 2001, p. 1257) even relevant? In fact, the experimenter's "estimation" (see above) provides a stimulus identity/categorization (e.g. "green Granny Smith apple") from the experimenter's list of given stimuli. But this is not the experienced percept (the internal picture that we call "apple"), and as such, it is of no use to anyone besides the experimenter. "Decoding" has, since the time of Lettvin et al. (1959), become inseparable from the notion of "information processing". This is evident in many of the papers cited up to this juncture. Recall Bialek et al.'s (1991) statement (Section 6.2 above) that "All of an organism's information about the sensory world comes from real-time observation of the activity of its Figure 5. The neuron as the collective: the neuron as an observer (the two-sided arrow indicates "equivalence") and hence as the collectives that are the observer O ve r a l l O bs er ve r s ( L a b C o l l ec t i ve ) S o c i e t y a s O bs er ver ( G l o ba l Co l l e c t i ve ) E x p er i m e nt e r / O b se r ver N e ur o n a s O bs er ver Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) own neurons" (italics supplied). The following section explains why "information processing" mandated "decoding". These details need to be aired because neuroscientists have not been forthcoming on this issue. 8. The "informative" neuron 8.1 Information Theory as used by neuroscientists Neuroscientists use Shannon Information Theory to quantify "the ability of [physiological sensory] receptors (or other signal-processing elements) to transmit information about stimulus parameters" (Smith et al., 1983, p. 82). For the unfamiliar reader, a brief recapitulation of Shannon Information Theory and its use in neurophysiology is appropriate, as follows. Shannon himself (Shannon, 1948) did not deal with neurons but rather with a "general communication system" which includes "An information source which produces a message or sequence of messages to be communicated to the receiving terminal" (original italics). The sent messages are groups of symbols, such as letters arranged into words and then into sentences (Shannon, 1948). The "general communication system" contains an encoder (the Transmitter), subsequent to the Information Source, and a later decoder (the Receiver). A symbol received is from the set of symbols that can be sent. Transmission is imperfect; not all symbols are received as they were sent. Information Transmitted can be computed after the transmission is complete, by knowing what symbols "k" (Events) were sent, what symbols "j" (Outcomes) were received, and how many times a "k" was received as a "j". The latter frequencies, called Njk, can be arranged in two dimensions as a so-called "confusion matrix". Figure 6 shows the communication engineers' confusionmatrix (Shannon, 1948). Neuroscientists, too, use a confusion matrix in computing Information Transmitted. But their matrix differs somewhat from Shannon's, as will be described. First, note the rationale behind neuroscientists' use of Information Theory. During a sensory stimulus, a responsive neuron fires a train of voltage spikes. Each application of the same stimulus can evoke a different spike train, making the neuron's response to the stimulus look "confused". This in turn would cause the value of Information Transmitted to be less than its possible maximum, but it would nonetheless exceed zero, because the neuronal spike train has some measurable characteristics that are observed to change systematically with change in some Figure 6. The communication engineers' confusion matrix, whose rows and columns have the same labels (compare to Figure 7) K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) property of the stimulus, such as physical intensity. The most popular of the spike-train measures is the mean stimulus-evoked spike count, taken over a sub-interval of the duration of the stimulus (right up to the full stimulus duration). There are other spike-train measures used for computation of Information Transmitted, such as the latency of the first stimulusevoked spike (Furukawa and Middlebrooks, 2002; Nelken et al., 2005; Saal et al., 2009) or the interspike intervals (Lu and Wang, 2004) or "the principal components of the temporal waveform of the response" (Optican and Richmond, 1987, p. 168; also McClurkin et al., 1991; Tovée et al., 1993; Tovée and Rolls, 1995; Rolls et al., 1996). Regarding the construction of the neuroscientists' confusion matrix, Werner and Mountcastle (1965) provide the archetype. Their spike-train measure is taken as Shannon's "Outcome" in response to the stimulus, and the stimulus is labelled as Shannon's "Event" according to whatever measurable stimulus property is being varied, such as the intensity. In Werner and Mountcastle (1965), the Events are in fact called stimulus categories, which can (in practice) be ranges of the stimulus property in question. Likewise, the Outcomes are in fact called response categories, and are always ranges of the used spike-train measure (e.g. total count of evoked voltage-spikes). Figure 7 shows the neuroscientists' confusion matrix (after Werner and Mountcastle, 1965, p. 387). Contrast all of this to Shannon (1948), who specifies that each Event is from a limited set of distinct things (such as symbols), each evoking an Outcome that is one of the possible Events. But Werner and Mountcastle (1965) and their successors interpret Events as elements of a continuum, such as stimulus intensity; they likewise take Outcomes as magnitudes of a different continuum, one that describes a neuron's response to the stimulus, such as spike count. Altogether, the WernerMountcastle interpretation produces an Information Transmitted whose meaning is unclear, and which therefore is unlikely to be the actual Information Transmitted (Nizami, 2012, 2014). This conclusion, which arises from the particulars of the computations, applies equally well regardless of the used spike-train measure. The vagueness of theWerner-Mountcastle interpretation of Information Transmitted is a fatal flaw. Neuroscientists fail to see this. Some assemble the confusion matrix by working backwards to infer whichever Event evoked each particular neuronal Outcome, using a decoder (presumed to be used by the Figure 7. The neuroscientists' confusionmatrix, whose rows and columns have different labels from each other (compare to Figure 6) Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) brain) that "receives a spike train as input and guesses which stimulus evoked the spike train" (Nelken et al., 2005, p. 200; see also Georgopoulos and Massey, 1988; Rieke et al., 1995; Roddey and Jacobs, 1996; Warland et al., 1997; Buracas et al., 1998; Furukawa and Middlebrooks, 2002; Averbeck et al., 2003; Passaglia and Troy, 2004; DiCaprio et al., 2007; Sadeghi et al., 2007; Lawhern et al., 2011; So et al., 2012). Unfortunately, as noted in Section 7 above, such decoding is essentially guesswork. And even if it were error-free, the computed Information Transmitted would still be unclear, thanks to the aforementioned departures of theWerner-Mountcastle method from Shannon's (1948) original computations. 8.2 "A neural system that could carry out categorization followed by specific identification": Sugase and colleagues Sugase et al. (1999 [670þ citations]) introduce their work by stating that "Information Theory was used to investigate how well the neuronal responses could categorize the stimuli" (Sugase et al., 1999, p. 869). They then perform various classic Werner-Mountcastle computations of Information Transmitted, following many Werner-Mountcastle-style assumptions about what constitute "stimuli" and "neuronal responses" and so forth. They then conclude (Werner and Mountcastle, 1965, p. 872) that "Our result demonstrates a physiological precursor for a neural system that could carry out categorization followed by specific identification". The term "physiological precursor" presumably constitutes the neuronal spike trains evoked by their stimuli. However, the "neural system that could carry out categorization followed by specific identification" is proposed without any correlates from physiology, and in fact constitutes Sugase et al. themselves, who carry out the categorization to compute Information Transmitted, followed by the "specific identification" to infer the stimulus, i.e. to do "decoding". And 15 years later, in Sugase-Miyamoto et al. (2014), two of the original four authors of Sugase et al. (1999) join with two new co-authors to provide similar experiments, followed by similarly-mistaken claims. The notion that a neuron's response to the stimulus is "confused", hence amenable to Information Theory, is another way of saying that the neuron is "uncertain". Loss of uncertainty is, in Information Theory, gain of information (Shannon, 1948) – or, as some neuroscientists interpret it, a gain of knowledge, as follows. 9. The conscious, "knowing" neuron and its alternative, emergence 9.1 The neuron, conscious and knowing but alone and uncertain As represented in the Bialek et al. (1991) quotation (Section 6.2 above) about "real-time observation of the activity of its own neurons", neuroscientists imagine that the observer of neuronal spike trains is another neuron. Such an "observer neuron" is presumably conscious. It is also sometimes "ideal", insofar as it achieves the best detection or discrimination of stimuli thatmathematics allows. In short, the neuron becomes a mathematician, thanks to an algebraically convenient set of assumptions, offered within Signal Detection Theory (SDT) (Green and Swets, 1966/1988). SDT is used by Geisler et al. (1991), among many, who "apply an ideal-observer analysis to arbitrary discrimination stimuli [sic]", altogether involving "single neurons in discrimination tasks" (Geisler et al., 1991, p. 335). Note well that the discriminations are presumed to be performed by single neurons, although small neural populations may serve also (for example, Deneve et al., 1999 [370þ citations]; Seriès et al., 2009). Of course, the actual SDT discriminations are done by laboratory personnel, who examine neuronal spike-trains with the aid of books and computers. Now, SDT is a statistical method, with which the sensory nervous system could be "assumed to have some uncertainty" (Geisler et al., 1991, p. 335) – uncertainty about what stimuli were available and at what probabilities of appearance, just as in neuroscientists' use of K D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) Information Theory (Section 8). Geisler et al. (1991, p. 344) declare that, therefore, "later neurons in the sensory system might have implicit knowledge" (italics supplied). Or, as stated by SDT enthusiasts Gold and Shadlen (2001 [760þ citations]), neurons in "brain structures involved in planning for action" (and therefore influenced by sensory input) are "affected by knowledge of prior probabilities and expectation of reward in decisionmaking" (Gold and Shadlen, 2001, p. 13). Such language is hardly rare; for some references, see Gold and Shadlen (2001). The alleged "knowledgeable" decision-making neurons need not be at the brain. Hagins (1965, p. 405 [150þ citations]) presumes that a visual organ's photoreceptorcell's synapse is "Operating as a decision-making device like some part of the retina [sic] or brain". That is, the retina of the eye "knows" (Atick and Redlich, 1992 [630þ citations]; see, for example, the "eye's mind" in Norwich, 1978, p. 162). Of course, this doctrine has an obvious flaw: if the sort of neurons in question are not at the brain, then the brain is redundant to the function in question, that is, it has nothing to do (as noted by Ashby, 1995, among others). 9.2 Emergence Neuroscientists' assignment of consciousness to individual neurons is Reductionist. And it fails. The Reductionist approach describes the mind in terms of distinct pieces rather than as something that arises from a much larger number of points working simultaneously as a coordinated system. But there is hope in the latter concept, called emergence, which relates to self-organization (von der Malsburg, 1997; Chapters 6, 23, and 24 of Ramage and Shipp, 2009). As John (1976, p. 1) declares (original italics, and original internal quotation marks): I believe that "mind", under which rubric are subsumed such phenomena as consciousness, subjective experience, the concept of self, and self-awareness, is an emergent property of sufficiently complex and appropriately organized matter. In other words, consciousness emerges from the activity of vast numbers of interacting neurons. Likewise, from vast numbers of interacting consciousnesses emerge societies, whether animal or human. One part of human society is the laboratory group, containing the neuroscientist. MacKay and McCulloch (1952, p. 135) presage "emergence" in the course of expressing some last words on "information": "If cerebral activity is the stochastic process it appears to be [i.e. incorporating some inherent randomness], [then] the informationally significant descriptive concepts when once discovered seem likely to have as much relation to the parameters defining the states of individual neurons, as concepts such as entropy and temperature have to themotions of individual gasmolecules – and little more". Let us assume that individual neurons need not mutually describe – i.e. that they need not be conscious – to interact. Neurons need not be military generals, detectives, informants, journalists, grandmothers, messengers, marketers or signalmen. Single-neuron consciousness had been implied by Lettvin et al. (1959) and his successors, who could not believe that consciousness could emerge from non-consciousness – for example, that consciousness emerges at the larger scale; that mind emerges in brain. 10. Anthropomorphization as Reductionism Anthropomorphization is a form of Reductionism. Reductionism is the attempt to explain a complicated behavior or action, emanating from a complicated mechanism, in terms of simpler behaviors or actions, emanating from simpler mechanisms. Reductionism gives us a Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT ) doctrine: that the stimulus-evoked voltage-spike trains of single neurons, perhaps even those neurons that are physiologically peripheral, are all that is needed to account for all sensory relations associated with the stimuli. Examples of peripheral neurons are the primary sensory afferents, with their attached sensory receptor cell(s); examples of such sensory relations are the "Psychophysical Laws"; and examples of the presumptive linking of the two can be found in Zwislocki (2009), for hearing-science. The ongoing presumption, i.e. that psychophysical laws derive from single primary sensory afferents alone, represents a remarkably narrow "neural Reductionism", which provides nothing for sensory neurons further brain-wards to do (Ashby, 1995). There were contemporaries of Lettvin et al. (1959) who warned against attempting to understand sensations through single-neuron-centered mathematical modeling, such as schemes for encoding and decoding. For example, Rapoport (1956, p. 315) pointed out "the prodigious gap between the conceptual "nervous systems" of mathematicians and engineers and the real nervous tissue of the physiologist" (original internal quotation marks). That gap remains prodigious after 60 years. Walter and Gardiner (1970, p. 347) repeated the warnings (original italics): It is important to emphasize the need to distinguish conceptually between the coding hypothesized to be operating within an information processing system, and the coding which we, as observers, choose to use in measuring and describing that system. Note well the recognition of observer. What Walter and Gardiner call the "information processing system" is, to them, the test animal, in particular its sensory system(s) which connect the outside world to its brain. Walter and Gardiner (1970, p. 353) further admonish us, "This distinction between a variable which is informative to us as observers, according to a coding scheme which we develop, and a variable which can be hypothesized to be informative to the animal, but perhaps with an unknown coding scheme, is a distinction to be borne in mind continuously". Clearly, that distinction has been lost. 11. Summary and conclusions For more than half a century, neuroscientists have granted neurons the ability to decide, judge, recognize, categorize, detect, distinguish and/or discriminate. Neurons have been military generals, detectives, informants, journalists, grandmothers, messengers, marketers or signalmen, among other roles. All of this is anthropomorphization. It arose because single neurons were cast as "observers" who "estimate" the stimulus by means of "decoding". Decoding is the mathematical reduction of the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without these algorithmically-generated "guesses", there is allegedly no perception. Of course, perception is not guesswork, and there is no evidence or justification for "decoding". Indeed, the observer is the neuroscientist not the neuron. Interpreting neurons as "observers" endows neurons with consciousness. 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(2009), Sensory Neuroscience: Four Laws of Psychophysics, Springer ScienceþBusiness Media, NewYork, NY. Corresponding author Lance Nizami can be contacted at: nizamii2@att.net For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com Neuron as the collective D ow nl oa de d by D oc to r If tik ha r N iz am i A t 1 1: 24 1 3 N ov em be r 20 17 ( PT )