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Desbordes T, Lakretz Y, Chanoine V, Oquab M, Badier JM, Trébuchon A, Carron R, Bénar CG, Dehaene S, King JR. Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models. J Neurosci 2023; 43:5350-5364. [PMID: 37217308 PMCID: PMC10359032 DOI: 10.1523/jneurosci.1163-22.2023] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/07/2023] [Accepted: 02/19/2023] [Indexed: 05/24/2023] Open
Abstract
A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.SIGNIFICANCE STATEMENT Starting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multiword sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep neural language models, artificial neural networks trained on text and performing very well on many natural language processing tasks. Then, using a unique combination of MEG and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.
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Affiliation(s)
- Théo Desbordes
- Meta AI Research, Paris 75002, France; and Cognitive Neuroimaging Unit NeuroSpin center, 91191, Gif-sur-Yvette, France
| | - Yair Lakretz
- Cognitive Neuroimaging Unit NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Valérie Chanoine
- Institute of Language, Communication and the Brain, Aix-en-Provence, 13100, France; and Aix-Marseille Université, Centre National de la Recherche Scientifique, LPL, Aix-en-Provence, 13100, France
| | | | - Jean-Michel Badier
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, CNRS, LPL, Aix-en-Provence 13100; and Inst Neurosci Syst, Marseille, 13005, France
| | - Agnès Trébuchon
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, CNRS, LPL, Aix-en-Provence 13100, France; and Inst Neurosci Syst, Marseille, 13005, France; and Assistance Publique Hopitaux de Marseille, Timone hospital, Epileptology and Cerebral Rythmology, Marseille, 13385, France
| | - Romain Carron
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, CNRS, LPL, Aix-en-Provence 13100, France; and Inst Neurosci Syst, Marseille, 13005, France; and Assistance Publique Hopitaux de Marseille, Timone hospital, Functional and Stereotactic Neurosurgery, Marseille, 13385, France
| | - Christian-G Bénar
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, CNRS, LPL, Aix-en-Provence 13100, France; and Inst Neurosci Syst, Marseille, 13005, France
| | - Stanislas Dehaene
- Université Paris Saclay, Institut National de la Santé et de la Recherche Médicale, Commissariat à l'Energie Atomique, Cognitive Neuroimaging Unit, NeuroSpin center, Saclay, 91191, France; and Collège de France, PSL University, Paris, 75231, France
| | - Jean-Rémi King
- Meta AI Research, Paris 75002, France; and Cognitive Neuroimaging Unit NeuroSpin center, 91191, Gif-sur-Yvette, France
- LSP, École normale supérieure, PSL (Paris Sciences & Lettres) University, CNRS, 75005 Paris, France
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Robotka H, Thomas L, Yu K, Wood W, Elie JE, Gahr M, Theunissen FE. Sparse ensemble neural code for a complete vocal repertoire. Cell Rep 2023; 42:112034. [PMID: 36696266 PMCID: PMC10363576 DOI: 10.1016/j.celrep.2023.112034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/08/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
The categorization of animal vocalizations into distinct behaviorally relevant groups for communication is an essential operation that must be performed by the auditory system. This auditory object recognition is a difficult task that requires selectivity to the group identifying acoustic features and invariance to renditions within each group. We find that small ensembles of auditory neurons in the forebrain of a social songbird can code the bird's entire vocal repertoire (∼10 call types). Ensemble neural discrimination is not, however, correlated with single unit selectivity, but instead with how well the joint single unit tunings to characteristic spectro-temporal modulations span the acoustic subspace optimized for the discrimination of call types. Thus, akin to face recognition in the visual system, call type recognition in the auditory system is based on a sparse code representing a small number of high-level features and not on highly selective grandmother neurons.
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Affiliation(s)
- H Robotka
- Max Planck Institute for Ornithology, Seewiesen, Germany
| | - L Thomas
- University of California, Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, USA
| | - K Yu
- University of California, Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, USA
| | - W Wood
- University of California, Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, USA
| | - J E Elie
- University of California, Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, USA
| | - M Gahr
- Max Planck Institute for Ornithology, Seewiesen, Germany
| | - F E Theunissen
- Max Planck Institute for Ornithology, Seewiesen, Germany; University of California, Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, USA; Department of Psychology and Integrative Biology, University of California, Berkeley, Berkeley, CA, USA.
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Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
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Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
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