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De Rosa M, Vignali L, D’Urso A, Ktori M, Bottini R, Crepaldi D. Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:528-552. [PMID: 38911459 PMCID: PMC11192515 DOI: 10.1162/nol_a_00145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 03/20/2024] [Indexed: 06/25/2024]
Abstract
Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research. The present study capitalizes on the coupling of fast periodic visual stimulation and MEG recordings to address this issue and investigate the role of different kinds of visual and linguistic units in the visual word identification system. We compared strings of pseudo-characters; strings of consonants (e.g., sfcl); readable, but unattested strings (e.g., amsi); frequent, but non-meaningful chunks (e.g., idge); suffixes (e.g., ment); and words (e.g., vibe); and looked for discrimination responses with a particular focus on the ventral, occipito-temporal regions. The results revealed sensitivity to alphabetic, readable, familiar, and lexical stimuli. Interestingly, there was no discrimination between suffixes and equally frequent, but meaningless endings, thus highlighting a lack of sensitivity to semantics. Taken together, the data suggest that the visual word identification system, at least in its early processing stages, is particularly tuned to form-based regularities, most likely reflecting its reliance on general-purpose, statistical learning mechanisms that are a core feature of the visual system as implemented in the ventral stream.
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Affiliation(s)
- Mara De Rosa
- Cognitive Neuroscience Department, International School for Advanced Studies, Trieste, Italy
| | - Lorenzo Vignali
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Mattarello, Trento, Italy
| | - Anna D’Urso
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Mattarello, Trento, Italy
| | - Maria Ktori
- Cognitive Neuroscience Department, International School for Advanced Studies, Trieste, Italy
| | - Roberto Bottini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Mattarello, Trento, Italy
| | - Davide Crepaldi
- Cognitive Neuroscience Department, International School for Advanced Studies, Trieste, Italy
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Liaghat A, Konsman JP. Methodological advice for the young at heart investigator: Triangulation to build better foundations. Brain Behav Immun 2024; 115:737-746. [PMID: 37972881 DOI: 10.1016/j.bbi.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/02/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
In medicine and science, one is typically taught the main theories in a discipline or field along with standard models before receiving more instructions on how to apply certain methods. The aim of this work is not to address one method, but rather methodology, the study and evaluation of methods, by taking a philosophy of science detour. In this, a critique of biomedicine will be used as a starting point to address some positions regarding reductionism, specifying notions such as systems and mechanisms, as well as regarding the mind-body problem discussing psychosomatic medicine and psychoneuroimmunology. Some recommendations to make science more pluralistic, robust and translationally-relevant will then be made as a way to foster constructive debates on reductionism and the mind-body problem and, in turn, favor more interdisciplinary research.
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Affiliation(s)
- Amirreza Liaghat
- IMMUNOlogy from CONcepts and ExPeriments to Translation, CNRS UMR 5164, University of Bordeaux, 33076 Bordeaux, France
| | - Jan Pieter Konsman
- IMMUNOlogy from CONcepts and ExPeriments to Translation, CNRS UMR 5164, University of Bordeaux, 33076 Bordeaux, France.
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Going deep into schizophrenia with artificial intelligence. Schizophr Res 2022; 245:122-140. [PMID: 34103242 DOI: 10.1016/j.schres.2021.05.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 12/30/2022]
Abstract
Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by the nervous system, has recently provided an accessible way of modeling and analyzing complex, high-dimensional, nonlinear systems. The unprecedented accuracy of deep learning algorithms in classification and prediction tasks has revolutionized a wide range of scientific fields and is rapidly permeating SZ research. Deep learning has the potential of becoming a valuable aid for clinicians in the prediction, diagnosis, and treatment of SZ, especially in combination with principles from Bayesian statistics. Furthermore, deep learning could become a powerful tool for uncovering the mechanisms underlying SZ thanks to a growing number of techniques designed for improving model interpretability and causal reasoning. The purpose of this article is to introduce SZ researchers to the field of deep learning and review its latest applications in SZ research. In general, existing studies have yielded impressive results in classification and outcome prediction tasks. However, methodological concerns related to the assessment of model performance in several studies, the widespread use of small training datasets, and the little clinical value of some models suggest that some of these results should be taken with caution.
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Blazek PJ, Lin MM. Explainable neural networks that simulate reasoning. NATURE COMPUTATIONAL SCIENCE 2021; 1:607-618. [PMID: 38217134 DOI: 10.1038/s43588-021-00132-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 08/16/2021] [Indexed: 01/15/2024]
Abstract
The success of deep neural networks suggests that cognition may emerge from indecipherable patterns of distributed neural activity. Yet these networks are pattern-matching black boxes that cannot simulate higher cognitive functions and lack numerous neurobiological features. Accordingly, they are currently insufficient computational models for understanding neural information processing. Here, we show how neural circuits can directly encode cognitive processes via simple neurobiological principles. To illustrate, we implemented this model in a non-gradient-based machine learning algorithm to train deep neural networks called essence neural networks (ENNs). Neural information processing in ENNs is intrinsically explainable, even on benchmark computer vision tasks. ENNs can also simulate higher cognitive functions such as deliberation, symbolic reasoning and out-of-distribution generalization. ENNs display network properties associated with the brain, such as modularity, distributed and localist firing, and adversarial robustness. ENNs establish a broad computational framework to decipher the neural basis of cognition and pursue artificial general intelligence.
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Affiliation(s)
- Paul J Blazek
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Milo M Lin
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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Molinari M, Masciullo M. Stroke and potential benefits of brain-computer interface. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:25-32. [PMID: 32164857 DOI: 10.1016/b978-0-444-63934-9.00003-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
To treat stroke and, in particular, to alleviate the personal and social burden of stroke survivors is a main challenge for neuroscience research. Advancements in the knowledge of neurobiologic mechanisms subserving stroke-related damage and recovery provide key data to guide clinicians to tailor interventions to specific patient's needs. How does the brain-computer interface (BCI) fit into this scenario? A technique created to allow completely paralyzed individuals to control the environment recently introduced a new line of development: to provide a means to possibly control formation and changes in the brain network organization. In a sort of revolution, similar to the change from geocentric to heliocentric planet organization envisioned by Copernicus, we are facing a critical change in BCI research, moving from a brain to computer direction to a computer to brain one. This direction change will profoundly open up new avenues for BCI research and clinical applications. In this chapter, we address this change and discuss present and future applications of this new line idea of BCI use in stroke.
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Affiliation(s)
- Marco Molinari
- Department of Neurorehabilitation, Fondazione Santa Lucia IRCCS, Rome, Italy.
| | - Marcella Masciullo
- Department of Neurorehabilitation, Fondazione Santa Lucia IRCCS, Rome, Italy
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Healy MJ, Caudell TP. Episodic memory: A hierarchy of spatiotemporal concepts. Neural Netw 2019; 120:40-57. [DOI: 10.1016/j.neunet.2019.09.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 08/13/2019] [Accepted: 09/07/2019] [Indexed: 11/28/2022]
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Barwich AS. The Value of Failure in Science: The Story of Grandmother Cells in Neuroscience. Front Neurosci 2019; 13:1121. [PMID: 31708726 PMCID: PMC6822296 DOI: 10.3389/fnins.2019.01121] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 10/04/2019] [Indexed: 11/13/2022] Open
Abstract
The annals of science are filled with successes. Only in footnotes do we hear about the failures, the cul-de-sacs, and the forgotten ideas. Failure is how research advances. Yet it hardly features in theoretical perspectives on science. That is a mistake. Failures, whether clear-cut or ambiguous, are heuristically fruitful in their own right. Thinking about failure questions our measures of success, including the conceptual foundations of current practice, that can only be transient in an experimental context. This article advances the heuristics of failure analysis, meaning the explicit treatment of certain ideas or models as failures. The value of failures qua being a failure is illustrated with the example of grandmother cells; the contested idea of a hypothetical neuron that encodes a highly specific but complex stimulus, such as the image of one's grandmother. Repeatedly evoked in popular science and maintained in textbooks, there is sufficient reason to critically review the theoretical and empirical background of this idea.
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Affiliation(s)
- Ann-Sophie Barwich
- Department of History and Philosophy of Science and Medicine, Cognitive Science Program, Indiana University Bloomington, Bloomington, IN, United States
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Healy MJ, Caudell TP. Local and distributed concept representation via colimits: An example. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Roy A. The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons. Front Psychol 2017; 8:186. [PMID: 28261127 PMCID: PMC5311062 DOI: 10.3389/fpsyg.2017.00186] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 01/30/2017] [Indexed: 11/17/2022] Open
Abstract
The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.
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Affiliation(s)
- Asim Roy
- Department of Information Systems, Arizona State University, TempeAZ, USA
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Roy A. An extension of the localist representation theory: grandmother cells are also widely used in the brain. Front Psychol 2013; 4:300. [PMID: 23745119 PMCID: PMC3662881 DOI: 10.3389/fpsyg.2013.00300] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Accepted: 05/09/2013] [Indexed: 11/21/2022] Open
Affiliation(s)
- Asim Roy
- Department of Information Systems, Arizona State University Tempe, AZ, USA
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Markowitsch HJ. Memory and self-neuroscientific landscapes. ISRN NEUROSCIENCE 2013; 2013:176027. [PMID: 24967303 PMCID: PMC4045540 DOI: 10.1155/2013/176027] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 04/22/2013] [Indexed: 02/07/2023]
Abstract
Relations between memory and the self are framed from a number of perspectives-developmental aspects, forms of memory, interrelations between memory and the brain, and interactions between the environment and memory. The self is seen as dividable into more rudimentary and more advanced aspects. Special emphasis is laid on memory systems and within them on episodic autobiographical memory which is seen as a pure human form of memory that is dependent on a proper ontogenetic development and shaped by the social environment, including culture. Self and episodic autobiographical memory are seen as interlocked in their development and later manifestation. Aside from content-based aspects of memory, time-based aspects are seen along two lines-the division between short-term and long-term memory and anterograde-future-oriented-and retrograde-past-oriented memory. The state dependency of episodic autobiographical is stressed and implications of it-for example, with respect to the occurrence of false memories and forensic aspects-are outlined. For the brain level, structural networks for encoding, consolidation, storage, and retrieval are discussed both by referring to patient data and to data obtained in normal participants with functional brain imaging methods. It is elaborated why descriptions from patients with functional or dissociative amnesia are particularly apt to demonstrate the facets in which memory, self, and personal temporality are interwoven.
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Affiliation(s)
- Hans J. Markowitsch
- Physiological Psychology, University of Bielefeld, Universitaetsstraße 25, 33615 Bielefeld, Germany
- Center of Excellence “Cognitive Interaction Technology” (CITEC), University of Bielefeld, 33615 Bielefeld, Germany
- Hanse Institute of Advanced Science, P. O. Box 1344, 27733 Delmenhorst, Germany
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