Literature DB >> 24939580

Thinking in circuits: toward neurobiological explanation in cognitive neuroscience.

Friedemann Pulvermüller1, Max Garagnani, Thomas Wennekers.   

Abstract

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain-mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.

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Year:  2014        PMID: 24939580      PMCID: PMC4228116          DOI: 10.1007/s00422-014-0603-9

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  102 in total

Review 1.  Active perception: sensorimotor circuits as a cortical basis for language.

Authors:  Friedemann Pulvermüller; Luciano Fadiga
Journal:  Nat Rev Neurosci       Date:  2010-04-09       Impact factor: 34.870

Review 2.  Emerging concepts for the dynamical organization of resting-state activity in the brain.

Authors:  Gustavo Deco; Viktor K Jirsa; Anthony R McIntosh
Journal:  Nat Rev Neurosci       Date:  2011-01       Impact factor: 34.870

3.  Discrete combinatorial circuits emerging in neural networks: a mechanism for rules of grammar in the human brain?

Authors:  Friedemann Pulvermüller; Andreas Knoblauch
Journal:  Neural Netw       Date:  2009-01-30

4.  [Capabilities of an associative storage system compared with the function of the brain (author's transl)].

Authors:  G Willwacher
Journal:  Biol Cybern       Date:  1976-11-30       Impact factor: 2.086

5.  Models for the brain.

Authors:  P J van Heerden; D J Willshaw; H C Longuet-Higgens; O P Buneman
Journal:  Nature       Date:  1970-01-10       Impact factor: 49.962

6.  A spiking network model of short-term active memory.

Authors:  D Zipser; B Kehoe; G Littlewort; J Fuster
Journal:  J Neurosci       Date:  1993-08       Impact factor: 6.167

7.  Structural asymmetries in the infant language and sensori-motor networks.

Authors:  J Dubois; L Hertz-Pannier; A Cachia; J F Mangin; D Le Bihan; G Dehaene-Lambertz
Journal:  Cereb Cortex       Date:  2008-06-17       Impact factor: 5.357

8.  Functional links between motor and language systems.

Authors:  Friedemann Pulvermüller; Olaf Hauk; Vadim V Nikulin; Risto J Ilmoniemi
Journal:  Eur J Neurosci       Date:  2005-02       Impact factor: 3.386

9.  Motor cognition-motor semantics: action perception theory of cognition and communication.

Authors:  Friedemann Pulvermüller; Rachel L Moseley; Natalia Egorova; Zubaida Shebani; Véronique Boulenger
Journal:  Neuropsychologia       Date:  2013-12-12       Impact factor: 3.139

10.  A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

Authors:  A Hanuschkin; S Ganguli; R H R Hahnloser
Journal:  Front Neural Circuits       Date:  2013-06-19       Impact factor: 3.492

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  14 in total

1.  Modelling concrete and abstract concepts using brain-constrained deep neural networks.

Authors:  Malte R Henningsen-Schomers; Friedemann Pulvermüller
Journal:  Psychol Res       Date:  2021-11-11

2.  Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications.

Authors:  Pierre Bonzon
Journal:  Cogn Neurodyn       Date:  2017-04-01       Impact factor: 5.082

Review 3.  Biological constraints on neural network models of cognitive function.

Authors:  Friedemann Pulvermüller; Rosario Tomasello; Malte R Henningsen-Schomers; Thomas Wennekers
Journal:  Nat Rev Neurosci       Date:  2021-06-28       Impact factor: 34.870

4.  Neurophysiological evidence for whole form retrieval of complex derived words: a mismatch negativity study.

Authors:  Jeff Hanna; Friedemann Pulvermüller
Journal:  Front Hum Neurosci       Date:  2014-11-06       Impact factor: 3.169

Review 5.  Is the Sensorimotor Cortex Relevant for Speech Perception and Understanding? An Integrative Review.

Authors:  Malte R Schomers; Friedemann Pulvermüller
Journal:  Front Hum Neurosci       Date:  2016-09-21       Impact factor: 3.169

6.  A model of individualized canonical microcircuits supporting cognitive operations.

Authors:  Tim Kunze; Andre D H Peterson; Jens Haueisen; Thomas R Knösche
Journal:  PLoS One       Date:  2017-12-04       Impact factor: 3.240

7.  eMindLog: Self-Measurement of Anxiety and Depression Using Mobile Technology.

Authors:  Thomas M Penders; Karl L Wuensch; Philip T Ninan
Journal:  JMIR Res Protoc       Date:  2017-05-24

8.  Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models.

Authors:  Tim Kunze; Jens Haueisen; Thomas R Knösche
Journal:  Biol Cybern       Date:  2019-02-14       Impact factor: 2.086

9.  The Use of Hebbian Cell Assemblies for Nonlinear Computation.

Authors:  Christian Tetzlaff; Sakyasingha Dasgupta; Tomas Kulvicius; Florentin Wörgötter
Journal:  Sci Rep       Date:  2015-08-07       Impact factor: 4.379

10.  A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords.

Authors:  Max Garagnani; Guglielmo Lucchese; Rosario Tomasello; Thomas Wennekers; Friedemann Pulvermüller
Journal:  Front Comput Neurosci       Date:  2017-01-18       Impact factor: 2.380

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