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<div style="text-align: -webkit-left;"><font class="Apple-style-span" face="Times"><span class="Apple-style-span" style="font-size: 16px;">Dear Gary,</span></font></div><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">I just
read your interesting <i style="mso-bidi-font-style:normal">Science</i>
article. I was intrigued by your comments about cerebral cortex that “its basic
logic remains unknown” and that “there is little evidence that such uniform
architectures can capture the diversity of cortical function in simple
mammals”. I was also struck by your comments that cortical models “might
include circuits for shifting the focus of attention, for encoding and
manipulating sequences, and for normalizing the ratio between the activity of
an individual neuron and a set of neurons…and for working memory storage,
decision-making, storage and transformation of information via population
coding…, alongside machinery for hierarchical pattern recognition”. You also
commented about such matters as “temporal synchrony among neural ensembles…to
precisely controlled recurrent interactions between the prefrontal cortex and
basal ganglia…”</span></p><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Actually,
there is an emerging unified laminar cortical theory that embodies all of these
properties, and that has been used to provide unified explanations and
predictions about psychological, anatomical, neurophysiological, biophysical,
and some biochemical data. This theory, whose various component models are
often unified under the general heading of LAMINART theory, has been getting
rapidly developed since the first article about it appeared in <i style="mso-bidi-font-style:normal">Trends in Neurosciences</i> in 1997.</span></p><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:Arial">The name
LAMINART acknowledges the synthesis of concepts about the design of laminar
cortical architectures with more long-standing principles and mechanisms of </span><span style="font-size:12.0pt;font-family:Times">Adaptive Resonance Theory, or ART, which
began as a cognitive and neural theory of how the brain autonomously learns to
categorize, recognize, and predict objects and events in a changing world. As
illustrated in the review article Grossberg (2012, <a href="http://cns.bu.edu/~steve/ART.pdf">http://cns.bu.edu/~steve/ART.pdf</a>),
</span><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:
Helvetica;mso-bidi-language:EN-US">ART is arguably the currently most highly
developed cognitive and neural theory available, with the broadest explanatory
and predictive range. It has been getting progressively developed since I
introduced it in 1976 to propose a solution to the classical <i style="mso-bidi-font-style:normal">stability-plasticity dilemma</i>. This
proposed solution enables ART to carry out fast, incremental, and self-stabilizing
unsupervised and supervised learning in response to a changing world.</span></p><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">ART specifies mechanistic links between processes of
consciousness, learning, expectation, attention, resonance, and synchrony
during both unsupervised and supervised learning. ART provides functional and
mechanistic explanations of such diverse topics as laminar cortical circuitry;
invariant object and scenic gist learning and recognition; prototype, surface,
and boundary attention; gamma and beta oscillations; learning of entorhinal
grid cells and hippocampal place cells; computation of homologous spatial and
temporal mechanisms in the entorhinal-hippocampal system; vigilance breakdowns
during autism and medial temporal amnesia; cognitive-emotional interactions
that focus attention on valued objects in an adaptively timed way;
item-order-rank working memories and learned list chunks for the planning and
control of sequences of linguistic, spatial, and motor information; conscious
speech percepts that are influenced by future context; auditory streaming in
noise during source segregation; and speaker normalization. Brain regions that
are functionally described include visual and auditory neocortex; specific and
nonspecific thalamic nuclei; inferotemporal, parietal, prefrontal, entorhinal,
hippocampal, parahippocampal, perirhinal, and motor cortices; frontal eye
fields; supplementary eye fields; amygdala; basal ganglia: cerebellum; and
superior colliculus. Due to the complementary organization of the brain, ART
does not describe many spatial and motor behaviors whose matching and learning
laws differ from those of ART.</span></p><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Given Randy O‘Reilly’s comments about Leabra, it is
also of historical interest that I introduced the core equations used in Leabra
in the 1960s and early 1970s, and they have proved to be of critical importance
in all the developments of ART.</span></p><p class="MsoNormal" style="text-align:justify;mso-layout-grid-align:none"><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:Helvetica;
mso-bidi-language:EN-US">To illustrate how LAMINART illustrates the type of
laminar cortical theory that your <i style="mso-bidi-font-style:normal">Science</i>
article discusses, let me refer interested readers to a few archival articles. </span><span style="font-size:12.0pt;font-family:Times">LAMINART proposes how all cortical
areas combine bottom-up, horizontal, and top-down interactions, thereby
beginning to functionally clarify why all granular neocortex has a
characteristic architecture with six main cell layers, and how these laminar
circuits may be specialized to carry out different types of biological
intelligence.<span style="mso-spacerun:yes"> </span>In particular, this
unification shows how variations of a shared laminar cortical design can be
used to explain and simulate psychological and neurobiological data about
vision, speech, and cognition:</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:503.8pt"><b style="mso-bidi-font-weight:normal"><i style="mso-bidi-font-style:normal"><span style="font-size:12.0pt;font-family:Times">Vision.</span></i></b><span style="font-size:12.0pt;font-family:Times"> The 3D LAMINART model integrates
bottom-up and horizontal processes of 3D boundary formation and perceptual
grouping, surface filling-in, and figure-ground separation with top-down
attentional matching and oscillatory dynamics in cortical areas such as V1, V2,
and V4 (Cao and Grossberg, 2005; Fang and Grossberg, 2009; Grossberg, 1999;
Grossberg and Raizada, 2000; Grossberg and Swaminathan, 2004; Grossberg and
Versace, 2008; Grossberg and Yazdanbakhsh, 2005; Raizada and Grossberg, 2001).
It is arguably the currently most highly developed vision model with the
broadest explanatory and predictive range, laminar or not. This model, as well
as the other models listed below, also makes multiple predictions about the
functional roles that are played by identified cortical cells in all of these
visual processes.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:503.8pt"><b style="mso-bidi-font-weight:normal"><i style="mso-bidi-font-style:normal"><span style="font-size:12.0pt;font-family:Times">Speech.</span></i></b><span style="font-size:12.0pt;font-family:Times"> The cARTWORD model proposes how
bottom-up, horizontal, and top-down interactions within a hierarchy of laminar
cortical processing stages, modulated by the basal ganglia, can generate a
conscious speech percept that is embodied by a resonant wave of activation that
occurs between acoustic features, acoustic item chunks, and list chunks (Grossberg
and Kazerounian, 2011). Chunk-mediated gating allows speech to be heard in the
correct temporal order, even when what is consciously heard depends upon using
future context to disambiguate noise-occluded sounds, as occurs during phonemic
restoration.</span></p><p class="MsoNormal" style="text-align:justify"><b style="mso-bidi-font-weight:
normal"><i style="mso-bidi-font-style:normal"><span style="font-size:12.0pt;
font-family:Times">Cognition.</span></i></b><span style="font-size:12.0pt;
font-family:Times"> The LIST PARSE model describes </span><span style="font-size:12.0pt;
font-family:Times;mso-bidi-font-family:TimesNewRomanPSMT;color:black;
mso-bidi-language:EN-US">how bottom-up, horizontal, and top-down interactions
within the laminar circuits of lateral prefrontal cortex may carry out working
memory storage of event sequences within layers 6 and 4, how unitization of
these event sequences through learning into list chunks may occur within layer
2/3, and how these stored sequences can be recalled at variable rates that are
under volitional control by the basal ganglia (Grossberg and Pearson, 2008). In
particular, the model uses variations of the same circuitry to quantitatively
simulate human cognitive data about immediate serial recall and immediate free
recall, delayed free recall, and continuous distracter free recall; and monkey
neurophysiological data from the prefrontal cortex obtained during sequential
sensory-motor imitation and planned performance.</span></p><p class="MsoNormal" style="text-align:justify"><b style="mso-bidi-font-weight:normal"><i style="mso-bidi-font-style:
normal"><span style="font-size:12.0pt;font-family:Times">Prefrontal-basal
ganglia interactions. </span></i></b><span style="font-size:12.0pt;font-family:
Times">In addition to the thalamcortical interactions embodied in the above
models, neocortical interactions with other subcortical structures have been
developed as part of this emerging theory, notably cognitive-emotional interactions,
reinforcement learning, and gating of plans and movements. These are also
reviewed in Grossberg (2012). Here I will just mention one of these models that
focuses on the kinds of prefrontal-basal ganglia interactions that you
mentioned in your <i style="mso-bidi-font-style:normal">Science </i>article.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;mso-layout-grid-align:
none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">The lisTELOS model
builds upon, and unifies, the working memory and basal ganglia circuits of the
LIST PARSE and TELOS models. In particular,<b style="mso-bidi-font-weight:normal"><i style="mso-bidi-font-style:normal"> </i></b>Silver et al. (2011) have
incorporated an item-order-rank spatial working memory into a comprehensive
model of how sequences of eye movements, which may include repetitions, may be
planned and performed. Similar mechanisms may be expected to control other
types of sequences as well, for reasons that are reviewed in Grossberg (2012). The
lisTELOS model's name derives from the fact that it unifies and further
develops concepts from LIST PARSE about how item-order-rank working memories
store lists of items, and of how TELOS model properties of the basal ganglia (Brown
et al., 1999, 2004) help to balance reactive vs. planned movements by
selectively gating sequences of actions through time.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;mso-layout-grid-align:
none"><b style="mso-bidi-font-weight:
normal"><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:
TimesNewRomanPSMT;color:black;mso-bidi-language:EN-US">Shunting dynamics and
ratio processing.</span></b><span style="font-size:12.0pt;font-family:Times;
mso-bidi-font-family:TimesNewRomanPSMT;color:black;mso-bidi-language:EN-US"> The
kind of shunting dynamics that enables automatic computation of activity ratios
has been a critical component of all models that my colleagues and I have
developed since my foundational article (Grossberg, 1973) first mathematically
proved how this works in both non-recurrent and recurrent networks. Indeed, ART
models may be viewed as self-organizing production systems that carry out a
novel kind of probabilistic hypothesis testing and decision-making that is
designed to work in response to big non-stationary data bases.</span></p><p class="MsoNormal" style="text-align:justify"><b style="mso-bidi-font-weight:
normal"><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:
TimesNewRomanPSMT;color:black;mso-bidi-language:EN-US">New computational
paradigms. </span></b><span style="font-size:12.0pt;font-family:Times;
mso-bidi-font-family:TimesNewRomanPSMT;color:black;mso-bidi-language:EN-US">These
examples illustrate an emerging unified theory of how variations of a shared
laminar neocortical design can carry out multiple types of biological
intelligence. Semi-classical models, such as deep learning, have been very
useful in technology, but have little to offer in explaining how our brains
have evolved to control autonomous adaptive behaviors. This weakness of deep
learning is partly explained by the fact that these laminar cortical models embody
revolutionary new computational paradigms that I have called Laminar Computing
and Complementary Computing, which underlie natural computational realizations
for biological systems that have evolved to autonomously and stably adapt in
real time to a rapidly changing and unpredictable world.</span></p><p class="MsoNormal" style="text-align:justify"><span class="Apple-style-span" style="font-family: monospace; white-space: pre; "><span style="font-size:12.0pt;font-family:Times;color:windowtext">Indeed, </span><span style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:Times">LAMINART embodies a new type of hybrid between <i style="mso-bidi-font-style:normal">feedforward </i>and<i style="mso-bidi-font-style:
normal"> feedback</i> computing, and also between <i style="mso-bidi-font-style:
normal">digital </i>and <i style="mso-bidi-font-style:normal">analog </i>computing for processing distributed data. These properties go beyond the typesof Bayesian models that are so popular today. They underlie the fast but stable self-organization that is characteristic of cortical development and life-long learning. Their circuits </span><span style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:Times;color:windowtext">"run as fast as they can": they behave like a real-time probabilistic decision circuit that operates as quickly as possible, given the evidence. There is thus a trade-off between certainty and speed. They operate in a fast feedforward mode when there is little uncertainty, and automatically switch to a slower feedback mode when there is uncertainty. Feedback selects a winning decision that enables the circuit to speed up again, since activation amplitude, synchronization, and processing speed both increase with certainty.</span></span></p><pre style="margin-right:-.05in;line-height:normal;tab-stops:4.75in 6.75in"><span style="font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:Times;color:windowtext">LAMINART also embodies a novel kind of hybrid computing that simultaneously realizes the stability of digital computing and the sensitivity of analog computing. The coherence that is derived from synchronous storage in interlaminar and intercortical feedback loops provides the stability of digital computing...</span><span style="font-size:
12.0pt;mso-bidi-font-size:10.0pt;font-family:Times;color:windowtext">the feedback loop exhibits hysteresis that can preserve the stored pattern against external perturbations</span><span style="font-size: 12pt; color: windowtext; "><font class="Apple-style-span" face="Symbol">...</font></span><span style="font-size:
12.0pt;mso-bidi-font-size:10.0pt;font-family:Times;color:windowtext">while preserving the sensitivity of analog computation.</span></pre><p class="MsoNormal" style="text-align:justify"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">I should add that the new models are also of interest
in technology, and indeed have been embodied in the software and hardware
applications of many companies during the past few decades. A great deal of
additional exciting research remains to be done to develop a unified software
and hardware platforms for multiple types of autonomous adaptive intelligence.
These promise to revolutionize computer science in general, and the design of
autonomous adaptive mobile robots in particular.</span></p><div><br></div><div><font class="Apple-style-span" face="Times" size="4">Best,</font></div><div><font class="Apple-style-span" face="Times" size="4"><br></font></div><div><font class="Apple-style-span" face="Times" size="4">Steve</font></div><div><font class="Apple-style-span" face="Times" size="4"><br></font></div><div><font class="Apple-style-span" face="Times" size="4">Stephen Grossberg</font></div><div><font class="Apple-style-span" face="Times" size="4">Wang Professor of Cognitive and Neural Systems</font></div><div><font class="Apple-style-span" face="Times" size="4">Professor of Mathematics, Psychology, and Biomedical Engineering</font></div><div><font class="Apple-style-span" face="Times" size="4">Director, Center for Adaptive Systems</font></div><div><font class="Apple-style-span" face="Times" size="4">Boston, University</font></div><div><font class="Apple-style-span" face="Times" size="4"><a href="http://cns.bu.edu/~steve">http://cns.bu.edu/~steve</a></font></div><p class="MsoNormal" style="text-align:justify"><span style="font-size:12.0pt;
font-family:Times;mso-bidi-font-family:TimesNewRomanPSMT;color:black;
mso-bidi-language:EN-US"><o:p> <span class="Apple-tab-span" style="white-space:pre"> </span></o:p></span><span class="Apple-style-span" style="line-height: 18px; "><b style="mso-bidi-font-weight:normal"><span style="font-size:12.0pt;line-height:
150%;font-family:Times">References</span></b></span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
28.0pt 56.0pt 84.0pt 112.0pt 140.0pt 168.0pt 196.0pt 224.0pt 3.5in 280.0pt 308.0pt 336.0pt;
mso-layout-grid-align:none"><span style="font-size:12.0pt;font-family:Times;
mso-bidi-font-family:TimesNewRomanPSMT;mso-bidi-language:EN-US">Brown, J.,
Bullock, D., and Grossberg, S. (1999). How the basal ganglia use parallel
excitatory and inhibitory learning pathways to selectively respond to unexpected
rewarding cues. <i>Journal of Neuroscience,</i> <i style="mso-bidi-font-style:
normal"><span style="mso-bidi-font-weight:bold">19</span></i>, 10502-10511.<o:p></o:p></span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
28.0pt 56.0pt 84.0pt 112.0pt 140.0pt 168.0pt 196.0pt 224.0pt 3.5in 280.0pt 308.0pt 336.0pt;
mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Brown, J.W.,
Bullock, D., and Grossberg, S. (2004). How laminar frontal cortex and basal
ganglia circuits interact to control planned and reactive saccades. <i>Neural
Networks</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:
bold">17</span></i>, 471-510.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
28.0pt 56.0pt 84.0pt 112.0pt 140.0pt 168.0pt 196.0pt 224.0pt 3.5in 280.0pt 308.0pt 336.0pt;
mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Cao, Y. and
Grossberg, S. (2005). A laminar cortical model of stereopsis and 3D surface
perception: Closure and da Vinci stereopsis. <i>Spatial Vision</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">18</span></i>,
515-578.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
28.0pt 56.0pt 84.0pt 112.0pt 140.0pt 168.0pt 196.0pt 224.0pt 3.5in 280.0pt 308.0pt 336.0pt;
mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Fang, L. and Grossberg, S. (2009). From stereogram to surface: How the
brain sees the world in depth. <i>Spatial Vision</i>, <i style="mso-bidi-font-style:
normal"><span style="mso-bidi-font-weight:bold">22</span>,</i> 45-82.</span></p><p class="MsoNormal" style="text-align:justify"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S. (1973). Contour enhancement, short-term
memory, and constancies in reverberating neural networks. <i>Studies in Applied
Mathematics</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:
bold">52</span></i>, 213-257.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:0in 4.5pt"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S., and Kazerounian, S. (2011). Laminar
cortical dynamics of conscious speech perception: A neural model of phonemic
restoration using subsequent context in noise. <i>Journal of the Acoustical
Society of America</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">130</span></i>, 440-460.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S., and Pearson, L. (2008). Laminar
cortical dynamics of cognitive and motor working memory, sequence learning and
performance: Toward a unified theory of how the cerebral cortex works. <i>Psychological
Review, </i><i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:
bold">115</span></i>, 677-732 .</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S., and Raizada, R. (2000).
Contrast-sensitive perceptual grouping and object-based attention in the
laminar circuits of primary visual cortex. <i>Vision Research,</i> <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">40</span></i><b>,</b>
1413-1432.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S., and Swaminathan, G. (2004). A laminar
cortical model for 3D perception of slanted and curved surfaces and of 2D
images: development, attention and bistability. <i>Vision Research</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">44</span></i>,
1147-1187.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:TimesNewRomanPSMT;
mso-bidi-language:EN-US">Grossberg, S., and Versace, M. (2008). Spikes,
synchrony, and attentive learning by laminar thalamocortical circuits. </span><i><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:TimesNewRomanPS-ItalicMT;
mso-bidi-language:EN-US">Brain Research</span></i><span style="font-size:12.0pt;
font-family:Times;mso-bidi-font-family:TimesNewRomanPSMT;mso-bidi-language:
EN-US">, </span><i style="mso-bidi-font-style:normal"><span style="font-size:
12.0pt;font-family:Times;mso-bidi-font-family:TimesNewRomanPS-BoldMT;
mso-bidi-language:EN-US;mso-bidi-font-weight:bold">1218</span></i><span style="font-size:12.0pt;font-family:Times;mso-bidi-font-family:TimesNewRomanPSMT;
mso-bidi-language:EN-US">, 278-312.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Grossberg, S., and Yazdanbakhsh, A. (2005). Laminar
cortical dynamics of 3D surface perception: Stratification, transparency, and
neon color spreading. <i>Vision Research</i>, <i style="mso-bidi-font-style:
normal"><span style="mso-bidi-font-weight:bold">45</span></i>, 1725-1743.</span></p><p class="MsoNormal" style="text-align:justify;tab-stops:4.5pt .25in"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Raizada, R. and Grossberg, S. (2003). Towards a theory
of the laminar architecture of cerebral cortex: Computational clues from the
visual system. <i>Cerebral Cortex</i>, <i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">13</span></i>, 100-113.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
4.5pt;mso-layout-grid-align:none"><span class="Apple-style-span" style="font-family: Times; font-size: 16px; ">Silver,
M.R., Grossberg, S., Bullock, D., Histed, M.H., and Miller, E.K. (2011). A
neural model of sequential movement planning and control of eye movements:
Item-order-rank working memory and saccade selection by the supplementary eye
fields. <i>Neural Networks</i>,<b> </b><i style="mso-bidi-font-style:normal"><span style="mso-bidi-font-weight:bold">26</span></i>, 29-58.</span></p><p class="MsoNormal" style="text-align:justify;mso-pagination:none;tab-stops:
4.5pt;mso-layout-grid-align:none"><span style="font-size:12.0pt;font-family:
Times;mso-bidi-language:EN-US"><o:p> ***********************************</o:p></span></p><p class="MsoNormal"><span style="font-size:12.0pt;font-family:Times"><o:p> </o:p></span></p>
<!--EndFragment--><div><div>On Nov 2, 2014, at 4:57 PM, Gary Marcus wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><meta http-equiv="Content-Type" content="text/html charset=utf-8"><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">New piece in Science, reevaluating the “canonical cortical computation” hypothesis: <a href="http://www.sciencemag.org/content/346/6209/551.short" class="">http://www.sciencemag.org/content/346/6209/551.short</a> (First paragraph pasted in below)<div class=""><br class=""><div class="">And lot of further detail didn’t fit: <a href="http://biorxiv.org/content/early/2014/10/31/010983" class="">http://biorxiv.org/content/early/2014/10/31/010983</a></div><div class=""><br class=""></div><div class="">I love a discussion here on the group, especially re: the Table of possible computation and their neural realizations, in the Supplement. We plan to crowd-source a more detailed version of that table; please contact me if you are interested in contributing.</div><div class=""><br class=""></div><div class="">Cheers,</div><div class="">Gary</div></div><div class=""><br class=""></div><div class=""><span style="color: rgb(51, 51, 51); font-family: 'Lucida Grande', arial, helvetica, sans-serif; font-size: 13px; line-height: 19px; background-color: rgb(255, 255, 255);" class="">The human cerebral cortex is central to a wide array of cognitive functions, from vision to language, reasoning, decision-making, and motor control. Yet, nearly a century after the neuroanatomical organization of the cortex was first defined, its basic logic remains unknown. One hypothesis is that cortical neurons form a single, massively repeated “canonical” circuit, characterized as a kind of a “nonlinear spatiotemporal filter with adaptive properties” (</span><em style="margin: 0px; padding: 0px; border: 0px; outline-style: none; font-size: 13px; font-family: 'Lucida Grande', arial, helvetica, sans-serif; line-height: 19px; vertical-align: baseline; color: rgb(51, 51, 51);" class="">1</em><span style="color: rgb(51, 51, 51); font-family: 'Lucida Grande', arial, helvetica, sans-serif; font-size: 13px; line-height: 19px; background-color: rgb(255, 255, 255);" class="">). In this classic view, it was “assumed that these…properties are identical for all neocortical areas.” Nearly four decades later, there is still no consensus about whether such a canonical circuit exists, either in terms of its anatomical basis or its function. Likewise, there is little evidence that such uniform architectures can capture the diversity of cortical function in simple mammals, let alone characteristically human processes such as language and abstract thinking (</span><em style="margin: 0px; padding: 0px; border: 0px; outline-style: none; font-size: 13px; font-family: 'Lucida Grande', arial, helvetica, sans-serif; line-height: 19px; vertical-align: baseline; color: rgb(51, 51, 51);" class="">2</em><span style="color: rgb(51, 51, 51); font-family: 'Lucida Grande', arial, helvetica, sans-serif; font-size: 13px; line-height: 19px; background-color: rgb(255, 255, 255);" class="">). Analogous software implementations in artificial intelligence (e.g., deep learning networks) have proven effective in certain pattern classification tasks, such as speech and image recognition, but likewise have made little inroads in areas such as reasoning and natural language understanding. Is the search for a single canonical cortical circuit misguided?</span></div><br class=""><br class=""><div apple-content-edited="true" class="">
<div style="color: rgb(0, 0, 0); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div style="color: rgb(0, 0, 0); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div style="color: rgb(0, 0, 0); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class=""><div class=""><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class="">Gary Marcus</span></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class="">Professor of Psychology and Neural Science</span></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class="">New York University</span></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class="">Visiting Cognitive Scientist</span></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class="">Allen Institute for Brain Science</span></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><div style="margin: 0in 0in 0.0001pt;" class=""><span style="letter-spacing: 0px;" class=""></span></div><div style="margin: 0in 0in 0.0001pt;" class=""><span class="Apple-style-span" style="font-family: Helvetica;">Editor, </span><a href="http://press.princeton.edu/titles/10306.html" class="">The Future of the Brain (2014)</a></div></div><div style="margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><span style="font-family: Helvetica, sans-serif;" class=""><a href="http://garymarcus.com/" class="">http://garymarcus.com/</a></span></div></div><div class=""><div style="orphans: 2; widows: 2; margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><a href="http://www.newyorker.com/contributors/gary-marcus" class="">New Yorker essays</a></div></div></div><div style="orphans: 2; widows: 2; margin: 0in 0in 0.0001pt; font-family: 'Times New Roman', serif;" class=""><a href="http://query.nytimes.com/search/sitesearch/?action=click&contentCollection=Opinion®ion=TopBar&WT.nav=searchWidget&module=SearchSubmit&pgtype=article#/%22gary+marcus%22/since1851/articles/1/allauthors/relevance/opinion/" class="">New York Times op-eds</a></div><div class=""><span style="font-family: Helvetica, sans-serif;" class=""><br class=""></span></div></div><br class="Apple-interchange-newline"></div><br class="Apple-interchange-newline"></div><br class="Apple-interchange-newline"><br class="Apple-interchange-newline">
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<span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div><div><div>Stephen Grossberg</div><div>Wang Professor of Cognitive and Neural Systems</div><div>Professor of Mathematics, Psychology, and Biomedical Engineering</div><div><div>Director, Center for Adaptive Systems <a href="http://www.cns.bu.edu/about/cas.html">http://www.cns.bu.edu/about/cas.html</a></div></div><div><a href="http://cns.bu.edu/~steve">http://cns.bu.edu/~steve</a></div><div><a href="mailto:steve@bu.edu">steve@bu.edu</a></div></div></div></div><div><br></div></div></span></div></span><br class="Apple-interchange-newline"></span><br class="Apple-interchange-newline">
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