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Piza DB, Corrigan BW, Gulli RA, Do Carmo S, Cuello AC, Muller L, Martinez-Trujillo J. Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in marmoset hippocampus. Nat Commun 2024; 15:4053. [PMID: 38744848 PMCID: PMC11093997 DOI: 10.1038/s41467-024-48374-2] [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: 08/22/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
The role of the hippocampus in spatial navigation has been primarily studied in nocturnal mammals, such as rats, that lack many adaptations for daylight vision. Here we demonstrate that during 3D navigation, the common marmoset, a new world primate adapted to daylight, predominantly uses rapid head-gaze shifts for visual exploration while remaining stationary. During active locomotion marmosets stabilize the head, in contrast to rats that use low-velocity head movements to scan the environment as they locomote. Pyramidal neurons in the marmoset hippocampus CA3/CA1 regions predominantly show mixed selectivity for 3D spatial view, head direction, and place. Exclusive place selectivity is scarce. Inhibitory interneurons are predominantly mixed selective for angular head velocity and translation speed. Finally, we found theta phase resetting of local field potential oscillations triggered by head-gaze shifts. Our findings indicate that marmosets adapted to their daylight ecological niche by modifying exploration/navigation strategies and their corresponding hippocampal specializations.
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Affiliation(s)
- Diego B Piza
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
| | - Benjamin W Corrigan
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Biology, Faculty of Science, York University, Toronto, ON, Canada
| | | | - Sonia Do Carmo
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - A Claudio Cuello
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Lyle Muller
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Applied Mathematics, Western University, London, ON, Canada
| | - Julio Martinez-Trujillo
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
- Robarts Research Institute, Western University, London, ON, Canada.
- Department of Physiology and Pharmacology, Western University, London, ON, Canada.
- Department of Psychiatry, Western University, London, ON, Canada.
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada.
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2
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Rolls ET. Two what, two where, visual cortical streams in humans. Neurosci Biobehav Rev 2024; 160:105650. [PMID: 38574782 DOI: 10.1016/j.neubiorev.2024.105650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/25/2024] [Accepted: 03/31/2024] [Indexed: 04/06/2024]
Abstract
ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China.
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3
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Matteucci G, Piasini E, Zoccolan D. Unsupervised learning of mid-level visual representations. Curr Opin Neurobiol 2024; 84:102834. [PMID: 38154417 DOI: 10.1016/j.conb.2023.102834] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/30/2023]
Abstract
Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn to exploit the statistical structure of their inputs in the absence of explicit training targets or rewards. Sophisticated experimental approaches have enabled the investigation of the influence of sensory experience on neural self-organization and its synaptic bases. Meanwhile, novel algorithms for unsupervised and self-supervised learning have become increasingly popular both as inspiration for theories of the brain, particularly for the function of intermediate visual cortical areas, and as building blocks of real-world learning machines. Here we review some of these recent developments, placing them in historical context and highlighting some research lines that promise exciting breakthroughs in the near future.
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Affiliation(s)
- Giulio Matteucci
- Department of Basic Neurosciences, University of Geneva, Geneva, 1206, Switzerland. https://twitter.com/giulio_matt
| | - Eugenio Piasini
- International School for Advanced Studies (SISSA), Trieste, 34136, Italy
| | - Davide Zoccolan
- International School for Advanced Studies (SISSA), Trieste, 34136, Italy.
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4
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Doerig A, Sommers RP, Seeliger K, Richards B, Ismael J, Lindsay GW, Kording KP, Konkle T, van Gerven MAJ, Kriegeskorte N, Kietzmann TC. The neuroconnectionist research programme. Nat Rev Neurosci 2023:10.1038/s41583-023-00705-w. [PMID: 37253949 DOI: 10.1038/s41583-023-00705-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 06/01/2023]
Abstract
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.
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Affiliation(s)
- Adrien Doerig
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
| | - Rowan P Sommers
- Department of Neurobiology of Language, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Katja Seeliger
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Blake Richards
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
- School of Computer Science, McGill University, Montréal, QC, Canada
- Mila, Montréal, QC, Canada
- Montréal Neurological Institute, Montréal, QC, Canada
- Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada
| | | | | | - Konrad P Kording
- Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada
- Bioengineering, Neuroscience, University of Pennsylvania, Pennsylvania, PA, USA
| | | | | | | | - Tim C Kietzmann
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
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5
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Struckmeier O, Tiwari K, Kyrki V. Autoencoding slow representations for semi-supervised data-efficient regression. Mach Learn 2023. [DOI: 10.1007/s10994-022-06299-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
AbstractThe slowness principle is a concept inspired by the visual cortex of the brain. It postulates that the underlying generative factors of a quickly varying sensory signal change on a different, slower time scale. By applying this principle to state-of-the-art unsupervised representation learning methods one can learn a latent embedding to perform supervised downstream regression tasks more data efficient. In this paper, we compare different approaches to unsupervised slow representation learning such as $$L_p$$
L
p
norm based slowness regularization and the SlowVAE, and propose a new term based on Brownian motion used in our method, the S-VAE. We empirically evaluate these slowness regularization terms with respect to their downstream task performance and data efficiency in state estimation and behavioral cloning tasks. We find that slow representations show great performance improvements in settings where only sparse labeled training data is available. Furthermore, we present a theoretical and empirical comparison of the discussed slowness regularization terms. Finally, we discuss how the Fréchet Inception Distance (FID), commonly used to determine the generative capabilities of GANs, can predict the performance of trained models in supervised downstream tasks.
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6
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Tesileanu T, Piasini E, Balasubramanian V. Efficient processing of natural scenes in visual cortex. Front Cell Neurosci 2022; 16:1006703. [PMID: 36545653 PMCID: PMC9760692 DOI: 10.3389/fncel.2022.1006703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.
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Affiliation(s)
- Tiberiu Tesileanu
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, United States
| | - Eugenio Piasini
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
| | - Vijay Balasubramanian
- Department of Physics and Astronomy, David Rittenhouse Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Santa Fe Institute, Santa Fe, NM, United States
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7
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Rolls ET, Deco G, Huang CC, Feng J. Multiple cortical visual streams in humans. Cereb Cortex 2022; 33:3319-3349. [PMID: 35834308 DOI: 10.1093/cercor/bhac276] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
The effective connectivity between 55 visual cortical regions and 360 cortical regions was measured in 171 HCP participants using the HCP-MMP atlas, and complemented with functional connectivity and diffusion tractography. A Ventrolateral Visual "What" Stream for object and face recognition projects hierarchically to the inferior temporal visual cortex, which projects to the orbitofrontal cortex for reward value and emotion, and to the hippocampal memory system. A Ventromedial Visual "Where" Stream for scene representations connects to the parahippocampal gyrus and hippocampus. An Inferior STS (superior temporal sulcus) cortex Semantic Stream receives from the Ventrolateral Visual Stream, from visual inferior parietal PGi, and from the ventromedial-prefrontal reward system and connects to language systems. A Dorsal Visual Stream connects via V2 and V3A to MT+ Complex regions (including MT and MST), which connect to intraparietal regions (including LIP, VIP and MIP) involved in visual motion and actions in space. It performs coordinate transforms for idiothetic update of Ventromedial Stream scene representations. A Superior STS cortex Semantic Stream receives visual inputs from the Inferior STS Visual Stream, PGi, and STV, and auditory inputs from A5, is activated by face expression, motion and vocalization, and is important in social behaviour, and connects to language systems.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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8
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Zeng T, Si B, Li X. Entorhinal-hippocampal interactions lead to globally coherent representations of space. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 3:100035. [PMID: 36685760 PMCID: PMC9846457 DOI: 10.1016/j.crneur.2022.100035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 02/08/2022] [Accepted: 03/09/2022] [Indexed: 01/25/2023] Open
Abstract
The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory.
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Affiliation(s)
- Taiping Zeng
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China
| | - Bailu Si
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
- Peng Cheng Laboratory, Shenzhen, 518055, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
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9
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Zhao D, Zhang Z, Lu H, Cheng S, Si B, Feng X. Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:508-521. [PMID: 32275629 DOI: 10.1109/tcyb.2020.2977999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central questions in the navigation research. Studies in neuroscience have shown that place cells in the hippocampus of the rodent brains form dynamic cognitive representations of locations in the environment. We propose a neural-network model called sensory-motor integration network model (SeMINet) to learn cognitive map representations by integrating sensory and motor information while an agent is exploring a virtual environment. This biologically inspired model consists of a deep neural network representing visual features of the environment, a recurrent network of place units encoding spatial information by sensorimotor integration, and a secondary network to decode the locations of the agent from spatial representations. The recurrent connections between the place units sustain an activity bump in the network without the need of sensory inputs, and the asymmetry in the connections propagates the activity bump in the network, forming a dynamic memory state which matches the motion of the agent. A competitive learning process establishes the association between the sensory representations and the memory state of the place units, and is able to correct the cumulative path-integration errors. The simulation results demonstrate that the network forms neural codes that convey location information of the agent independent of its head direction. The decoding network reliably predicts the location even when the movement is subject to noise. The proposed SeMINet thus provides a brain-inspired neural-network model for cognitive map updated by both self-motion cues and visual cues.
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10
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Kancharla P, Channappayya SS. Completely Blind Quality Assessment of User Generated Video Content. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 31:263-274. [PMID: 34855597 DOI: 10.1109/tip.2021.3130541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this work, we address the challenging problem of completely blind video quality assessment (BVQA) of user generated content (UGC). The challenge is twofold since the quality prediction model is oblivious of human opinion scores, and there are no well-defined distortion models for UGC content. Our solution is inspired by a recent computational neuroscience model which hypothesizes that the human visual system (HVS) transforms a natural video input to follow a straighter temporal trajectory in the perceptual domain. A bandpass filter based computational model of the lateral geniculate nucleus (LGN) and V1 regions of the HVS was used to validate the perceptual straightening hypothesis. We hypothesize that distortions in natural videos lead to loss in straightness (or increased curvature) in their transformed representations in the HVS. We provide extensive empirical evidence to validate our hypothesis. We quantify the loss in straightness as a measure of temporal quality, and show that this measure delivers acceptable quality prediction performance on its own. Further, the temporal quality measure is combined with a state-of-the-art blind spatial (image) quality metric to design a blind video quality predictor that we call STraightness Evaluation Metric (STEM). STEM is shown to deliver state-of-the-art performance over the class of BVQA algorithms on five UGC VQA datasets including KoNViD-1K, LIVE-Qualcomm, LIVE-VQC, CVD and YouTube-UGC. Importantly, our solution is completely blind i.e., training-free, generalizes very well, is explainable, has few tunable parameters, and is simple and easy to implement.
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11
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Parra-Barrero E, Diba K, Cheng S. Neuronal sequences during theta rely on behavior-dependent spatial maps. eLife 2021; 10:e70296. [PMID: 34661526 PMCID: PMC8565928 DOI: 10.7554/elife.70296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/15/2021] [Indexed: 11/15/2022] Open
Abstract
Navigation through space involves learning and representing relationships between past, current, and future locations. In mammals, this might rely on the hippocampal theta phase code, where in each cycle of the theta oscillation, spatial representations provided by neuronal sequences start behind the animal's true location and then sweep forward. However, the exact relationship between theta phase, represented position and true location remains unclear and even paradoxical. Here, we formalize previous notions of 'spatial' or 'temporal' theta sweeps that have appeared in the literature. We analyze single-cell and population variables in unit recordings from rat CA1 place cells and compare them to model simulations based on each of these schemes. We show that neither spatial nor temporal sweeps quantitatively accounts for how all relevant variables change with running speed. To reconcile these schemes with our observations, we introduce 'behavior-dependent' sweeps, in which theta sweep length and place field properties, such as size and phase precession, vary across the environment depending on the running speed characteristic of each location. These behavior-dependent spatial maps provide a structured heterogeneity that is essential for understanding the hippocampal code.
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Affiliation(s)
- Eloy Parra-Barrero
- Institute for Neural Computation, Ruhr University BochumBochumGermany
- International Graduate School of Neuroscience, Ruhr University BochumBochumGermany
| | - Kamran Diba
- Department of Anesthesiology, University of Michigan, Michigan MedicineAnn ArborUnited States
| | - Sen Cheng
- Institute for Neural Computation, Ruhr University BochumBochumGermany
- International Graduate School of Neuroscience, Ruhr University BochumBochumGermany
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12
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Rolls ET. Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning. Front Comput Neurosci 2021; 15:686239. [PMID: 34366818 PMCID: PMC8335547 DOI: 10.3389/fncom.2021.686239] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/29/2021] [Indexed: 11/13/2022] Open
Abstract
First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry, United Kingdom
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13
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Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding. eNeuro 2021; 8:ENEURO.0557-20.2021. [PMID: 34162691 PMCID: PMC8266216 DOI: 10.1523/eneuro.0557-20.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 12/03/2022] Open
Abstract
Cells in the entorhinal cortex (EC) contain rich spatial information and project strongly to the hippocampus where a cognitive map is supposedly created. These cells range from cells with structured spatial selectivity, such as grid cells in the medial EC (MEC) that are selective to an array of spatial locations that form a hexagonal grid, to weakly spatial cells, such as non-grid cells in the MEC and lateral EC (LEC) that contain spatial information but have no structured spatial selectivity. However, in a small environment, place cells in the hippocampus are generally selective to a single location of the environment, while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations but lack spatial periodicity. Given the anatomic connection from the EC to the hippocampus, how the hippocampus retrieves information from upstream EC remains unclear. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding from EC inputs. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the hippocampus. Our results show that the hexagonal patterns of MEC grid cells with various orientations, grid spacings and phases are necessary for the model to learn different place cells that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of hippocampal cells in the network, this will lead to the emergence of hippocampal cells that have multiple firing locations. More surprisingly, the model can also learn hippocampal place cells even when weakly spatial cells, instead of grid cells, are used as the input to the hippocampus. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.
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14
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Rolls ET. Neurons including hippocampal spatial view cells, and navigation in primates including humans. Hippocampus 2021; 31:593-611. [PMID: 33760309 DOI: 10.1002/hipo.23324] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/01/2021] [Accepted: 03/13/2021] [Indexed: 01/11/2023]
Abstract
A new theory is proposed of mechanisms of navigation in primates including humans in which spatial view cells found in the primate hippocampus and parahippocampal gyrus are used to guide the individual from landmark to landmark. The navigation involves approach to each landmark in turn (taxis), using spatial view cells to identify the next landmark in the sequence, and does not require a topological map. Two other cell types found in primates, whole body motion cells, and head direction cells, can be utilized in the spatial view cell navigational mechanism, but are not essential. If the landmarks become obscured, then the spatial view representations can be updated by self-motion (idiothetic) path integration using spatial coordinate transform mechanisms in the primate dorsal visual system to transform from egocentric to allocentric spatial view coordinates. A continuous attractor network or time cells or working memory is used in this approach to navigation to encode and recall the spatial view sequences involved. I also propose how navigation can be performed using a further type of neuron found in primates, allocentric-bearing-to-a-landmark neurons, in which changes of direction are made when a landmark reaches a particular allocentric bearing. This is useful if a landmark cannot be approached. The theories are made explicit in models of navigation, which are then illustrated by computer simulations. These types of navigation are contrasted with triangulation, which requires a topological map. It is proposed that the first strategy utilizing spatial view cells is used frequently in humans, and is relatively simple because primates have spatial view neurons that respond allocentrically to locations in spatial scenes. An advantage of this approach to navigation is that hippocampal spatial view neurons are also useful for episodic memory, and for imagery.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry, UK
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15
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Abstract
Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus-response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In "response learning," associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In "place learning," associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a "successor representation" of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to "blocking" by prior state-reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal-striatal system as a general system for decision making via adaptive combination of stimulus-response learning and the use of a cognitive map.
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Mao J, Hu X, Zhang L, He X, Milford M. A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01190-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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A computational model for grid maps in neural populations. J Comput Neurosci 2020; 48:149-159. [PMID: 32125562 DOI: 10.1007/s10827-020-00742-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 02/06/2020] [Accepted: 02/11/2020] [Indexed: 10/24/2022]
Abstract
Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain. In this work we introduce a novel theoretical and algorithmic framework able to explain the optimality of hexagonal grid-like response patterns. We show that this pattern is a result of minimal variance encoding of neurons together with maximal robustness to neurons' noise and minimal number of encoding neurons. The novelty lies in the formulation of the encoding problem considering neurons as an overcomplete basis (a frame) where the position information is encoded. Through the modern Frame Theory language, specifically that of tight and equiangular frames, we provide new insights about the optimality of hexagonal grid receptive fields. The proposed model is based on the well-accepted and tested hypothesis of Hebbian learning, providing a simplified cortical-based framework that does not require the presence of velocity-driven oscillations (oscillatory model) or translational symmetries in the synaptic connections (attractor model). We moreover demonstrate that the proposed encoding mechanism naturally explains axis alignment of neighbor grid cells and maps shifts, rotations and scaling of the stimuli onto the shape of grid cells' receptive fields, giving a straightforward explanation of the experimental evidence of grid cells remapping under transformations of environmental cues.
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18
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Betti A, Gori M, Melacci S. Cognitive Action Laws: The Case of Visual Features. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:938-949. [PMID: 31071056 DOI: 10.1109/tnnls.2019.2911174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The cognitive action contains a potential and a kinetic term that nicely resemble the classic formulation of regularization in machine learning. A special choice of the functional index, which leads to the fourth-order differential equations-Cognitive Action Laws (CAL)-exhibits a structure that mirrors classic formulation of machine learning. In particular, unlike the action of mechanics, the stationarity condition corresponds with the global minimum. Moreover, it is proven that typical asymptotic learning conditions on the weights can coexist with the initialization provided that the system dynamics is driven under a policy referred to as information overloading control. Finally, the theory is experimented for the problem of feature extraction in computer vision.
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Fukawa A, Aizawa T, Yamakawa H, Eguchi Yairi I. Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation. Brain Sci 2020; 10:brainsci10010028. [PMID: 31948100 PMCID: PMC7016820 DOI: 10.3390/brainsci10010028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 12/31/2019] [Indexed: 12/31/2022] Open
Abstract
Path integration is one of the functions that support the self-localization ability of animals. Path integration outputs position information after an animal’s movement when initial-position and movement information is input. The core region responsible for this function has been identified as the medial entorhinal cortex (MEC), which is part of the hippocampal formation that constitutes the limbic system. However, a more specific core region has not yet been identified. This research aims to clarify the detailed structure at the cell-firing level in the core region responsible for path integration from fragmentarily accumulated experimental and theoretical findings by reviewing 77 papers. This research draws a novel diagram that describes the MEC, the hippocampus, and their surrounding regions by focusing on the MEC’s input/output (I/O) information. The diagram was created by summarizing the results of exhaustively scrutinizing the papers that are relative to the I/O relationship, the connection relationship, and cell position and firing pattern. From additional investigations, we show function information related to path integration, such as I/O information and the relationship between multiple functions. Furthermore, we constructed an algorithmic hypothesis on I/O information and path-integration calculation method from the diagram and the information of functions related to path integration. The algorithmic hypothesis is composed of regions related to path integration, the I/O relations between them, the calculation performed there, and the information representations (cell-firing pattern) in them. Results of examining the hypothesis confirmed that the core region responsible for path integration was either stellate cells in layer II or pyramidal cells in layer III of the MEC.
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Affiliation(s)
- Ayako Fukawa
- Graduate School of Science and Engineering, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan;
- Correspondence: ; Tel.: +81-3-3238-3300
| | - Takahiro Aizawa
- Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
| | - Hiroshi Yamakawa
- The Whole Brain Architecture Initiative, a Specified Nonprofit Organization, Nishikoiwa 2-19-21, Edogawa-ku, Tokyo 133-0057, Japan;
- Dwango Co., Ltd., KABUKIZA TOWER, 4-12-15 Ginza, Chuo-ku, Tokyo 104-0061, Japan
| | - Ikuko Eguchi Yairi
- Graduate School of Science and Engineering, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan;
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20
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Görler R, Wiskott L, Cheng S. Improving sensory representations using episodic memory. Hippocampus 2019; 30:638-656. [DOI: 10.1002/hipo.23186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Richard Görler
- Institute for Neural ComputationRuhr University Bochum Bochum Germany
- International Graduate School of NeuroscienceRuhr University Bochum Bochum Germany
| | - Laurenz Wiskott
- Institute for Neural ComputationRuhr University Bochum Bochum Germany
| | - Sen Cheng
- Institute for Neural ComputationRuhr University Bochum Bochum Germany
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21
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Improved graph-based SFA: information preservation complements the slowness principle. Mach Learn 2019. [DOI: 10.1007/s10994-019-05860-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
AbstractSlow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a multi-dimensional time series. SFA has been extended to supervised learning (classification and regression) by an algorithm called graph-based SFA (GSFA). GSFA relies on a particular graph structure to extract features that preserve label similarities. Processing of high dimensional input data (e.g., images) is feasible via hierarchical GSFA (HGSFA), resulting in a multi-layer neural network. Although HGSFA has useful properties, in this work we identify a shortcoming, namely, that HGSFA networks prematurely discard quickly varying but useful features before they reach higher layers, resulting in suboptimal global slowness and an under-exploited feature space. To counteract this shortcoming, which we call unnecessary information loss, we propose an extension called hierarchical information-preserving GSFA (HiGSFA), where some features fulfill a slowness objective and other features fulfill an information preservation objective. The efficacy of the extension is verified in three experiments: (1) an unsupervised setup where the input data is the visual stimuli of a simulated rat, (2) the localization of faces in image patches, and (3) the estimation of human age from facial photographs of the MORPH-II database. Both HiGSFA and HGSFA can learn multiple labels and offer a rich feature space, feed-forward training, and linear complexity in the number of samples and dimensions. However, the proposed algorithm, HiGSFA, outperforms HGSFA in terms of feature slowness, estimation accuracy, and input reconstruction, giving rise to a promising hierarchical supervised-learning approach. Moreover, for age estimation, HiGSFA achieves a mean absolute error of 3.41 years, which is a competitive performance for this challenging problem.
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22
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Rolls ET. Spatial coordinate transforms linking the allocentric hippocampal and egocentric parietal primate brain systems for memory, action in space, and navigation. Hippocampus 2019; 30:332-353. [PMID: 31697002 DOI: 10.1002/hipo.23171] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/05/2019] [Accepted: 10/09/2019] [Indexed: 01/03/2023]
Abstract
A theory and model of spatial coordinate transforms in the dorsal visual system through the parietal cortex that enable an interface via posterior cingulate and related retrosplenial cortex to allocentric spatial representations in the primate hippocampus is described. First, a new approach to coordinate transform learning in the brain is proposed, in which the traditional gain modulation is complemented by temporal trace rule competitive network learning. It is shown in a computational model that the new approach works much more precisely than gain modulation alone, by enabling neurons to represent the different combinations of signal and gain modulator more accurately. This understanding may have application to many brain areas where coordinate transforms are learned. Second, a set of coordinate transforms is proposed for the dorsal visual system/parietal areas that enables a representation to be formed in allocentric spatial view coordinates. The input stimulus is merely a stimulus at a given position in retinal space, and the gain modulation signals needed are eye position, head direction, and place, all of which are present in the primate brain. Neurons that encode the bearing to a landmark are involved in the coordinate transforms. Part of the importance here is that the coordinates of the allocentric view produced in this model are the same as those of spatial view cells that respond to allocentric view recorded in the primate hippocampus and parahippocampal cortex. The result is that information from the dorsal visual system can be used to update the spatial input to the hippocampus in the appropriate allocentric coordinate frame, including providing for idiothetic update to allow for self-motion. It is further shown how hippocampal spatial view cells could be useful for the transform from hippocampal allocentric coordinates to egocentric coordinates useful for actions in space and for navigation.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry, UK
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23
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Zhou X, Bai T, Gao Y, Han Y. Vision-Based Robot Navigation through Combining Unsupervised Learning and Hierarchical Reinforcement Learning. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1576. [PMID: 30939807 PMCID: PMC6479296 DOI: 10.3390/s19071576] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/21/2019] [Accepted: 03/25/2019] [Indexed: 11/16/2022]
Abstract
Extensive studies have shown that many animals' capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot's head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.
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Affiliation(s)
- Xiaomao Zhou
- College of Automation, Harbin Engineering University, Harbin 150001, China.
| | - Tao Bai
- College of Automation, Harbin Engineering University, Harbin 150001, China.
| | - Yanbin Gao
- College of Automation, Harbin Engineering University, Harbin 150001, China.
| | - Yuntao Han
- College of Automation, Harbin Engineering University, Harbin 150001, China.
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24
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Correlation structure of grid cells is preserved during sleep. Nat Neurosci 2019; 22:598-608. [DOI: 10.1038/s41593-019-0360-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/06/2019] [Indexed: 01/16/2023]
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25
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Prasad A, Wood SMW, Wood JN. Using automated controlled rearing to explore the origins of object permanence. Dev Sci 2019; 22:e12796. [PMID: 30589167 DOI: 10.1111/desc.12796] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 11/07/2018] [Accepted: 11/12/2018] [Indexed: 01/13/2023]
Abstract
What are the origins of object permanence? Despite widespread interest in this question, methodological barriers have prevented detailed analysis of how experience shapes the development of object permanence in newborn organisms. Here, we introduce an automated controlled-rearing method for studying the emergence of object permanence in strictly controlled virtual environments. We used newborn chicks as an animal model and recorded their behavior continuously (24/7) from the onset of vision. Across four experiments, we found that object permanence can develop rapidly, within the first few days of life. This ability developed even when chicks were reared in impoverished visual environments containing no object occlusion events. Object permanence failed to develop, however, when chicks were reared in environments containing temporally non-smooth objects (objects moving on discontinuous spatiotemporal paths). These results suggest that experience with temporally smooth objects facilitates the development of object permanence, confirming a key prediction of temporal learning models in computational neuroscience.
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Affiliation(s)
- Aditya Prasad
- Department of Psychology, University of Southern California, Los Angeles, California
| | - Samantha M W Wood
- Department of Psychology, University of Southern California, Los Angeles, California
| | - Justin N Wood
- Department of Psychology, University of Southern California, Los Angeles, California
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26
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Savelli F, Knierim JJ. Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions. J Exp Biol 2019; 222:jeb188912. [PMID: 30728236 PMCID: PMC7375830 DOI: 10.1242/jeb.188912] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Path integration is a straightforward concept with varied connotations that are important to different disciplines concerned with navigation, such as ethology, cognitive science, robotics and neuroscience. In studying the hippocampal formation, it is fruitful to think of path integration as a computation that transforms a sense of motion into a sense of location, continuously integrated with landmark perception. Here, we review experimental evidence that path integration is intimately involved in fundamental properties of place cells and other spatial cells that are thought to support a cognitive abstraction of space in this brain system. We discuss hypotheses about the anatomical and computational origin of path integration in the well-characterized circuits of the rodent limbic system. We highlight how computational frameworks for map-building in robotics and cognitive science alike suggest an essential role for path integration in the creation of a new map in unfamiliar territory, and how this very role can help us make sense of differences in neurophysiological data from novel versus familiar and small versus large environments. Similar computational principles could be at work when the hippocampus builds certain non-spatial representations, such as time intervals or trajectories defined in a sensory stimulus space.
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Affiliation(s)
- Francesco Savelli
- The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - James J Knierim
- The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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27
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A Network Model Reveals That the Experimentally Observed Switch of the Granule Cell Phenotype During Epilepsy Can Maintain the Pattern Separation Function of the Dentate Gyrus. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-319-99103-0_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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28
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Chauhan A, Soman K, Chakravarthy VS. Saccade Velocity Driven Oscillatory Network Model of Grid Cells. Front Comput Neurosci 2019; 12:107. [PMID: 30687054 PMCID: PMC6335253 DOI: 10.3389/fncom.2018.00107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 12/13/2018] [Indexed: 12/03/2022] Open
Abstract
Grid cells and place cells are believed to be cellular substrates for the spatial navigation functions of hippocampus as experimental animals physically navigated in 2D and 3D spaces. However, a recent saccade study on head fixated monkey has also reported grid-like representations on saccadic trajectory while the animal scanned the images on a computer screen. We present two computational models that explain the formation of grid patterns on saccadic trajectory formed on the novel Images. The first model named Saccade Velocity Driven Oscillatory Network -Direct PCA (SVDON—DPCA) explains how grid patterns can be generated on saccadic space using Principal Component Analysis (PCA) like learning rule. The model adopts a hierarchical architecture. We extend this to a network model viz. Saccade Velocity Driven Oscillatory Network—Network PCA (SVDON-NPCA) where the direct PCA stage is replaced by a neural network that can implement PCA using a neurally plausible algorithm. This gives the leverage to study the formation of grid cells at a network level. Saccade trajectory for both models is generated based on an attention model which attends to the salient location by computing the saliency maps of the images. Both models capture the spatial characteristics of grid cells such as grid scale variation on the dorso-ventral axis of Medial Entorhinal cortex. Adding one more layer of LAHN over the SVDON-NPCA model predicts the Place cells in saccadic space, which are yet to be discovered experimentally. To the best of our knowledge, this is the first attempt to model grid cells and place cells from saccade trajectory.
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Affiliation(s)
- Ankur Chauhan
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Karthik Soman
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
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29
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Noguchi W, Iizuka H, Yamamoto M. Navigation behavior based on self-organized spatial representation in hierarchical recurrent neural network. Adv Robot 2019. [DOI: 10.1080/01691864.2019.1566088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Wataru Noguchi
- Graduate School of Information Science and Technology, Hokkaido University, Hokkaido, Japan
| | - Hiroyuki Iizuka
- Graduate School of Information Science and Technology, Hokkaido University, Hokkaido, Japan
| | - Masahito Yamamoto
- Graduate School of Information Science and Technology, Hokkaido University, Hokkaido, Japan
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30
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Metka B, Franzius M, Bauer-Wersing U. Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis. PLoS One 2018; 13:e0203994. [PMID: 30240451 PMCID: PMC6150500 DOI: 10.1371/journal.pone.0203994] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation encodes the position of the camera as slowly varying features while being invariant to its orientation resembling place cells in a rodent's hippocampus. Using an omnidirectional mirror allows to manipulate the image statistics by adding simulated rotational movement for improved orientation invariance. We apply the model in indoor and outdoor experiments and, for the first time, compare its performance against two state of the art visual SLAM methods. Results of the experiments show that the proposed straightforward model enables a precise self-localization with accuracies in the range of 13-33cm demonstrating its competitiveness to the established SLAM methods in the tested scenarios.
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Affiliation(s)
- Benjamin Metka
- Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Hessen, Germany
| | - Mathias Franzius
- Honda Research Institute Europe, Offenbach am Main, Hessen, Germany
| | - Ute Bauer-Wersing
- Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Hessen, Germany
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31
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Stoianov IP, Pennartz CMA, Lansink CS, Pezzulo G. Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis. PLoS Comput Biol 2018; 14:e1006316. [PMID: 30222746 PMCID: PMC6160242 DOI: 10.1371/journal.pcbi.1006316] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 09/27/2018] [Accepted: 06/20/2018] [Indexed: 12/26/2022] Open
Abstract
While the neurobiology of simple and habitual choices is relatively well known, our current understanding of goal-directed choices and planning in the brain is still limited. Theoretical work suggests that goal-directed computations can be productively associated to model-based (reinforcement learning) computations, yet a detailed mapping between computational processes and neuronal circuits remains to be fully established. Here we report a computational analysis that aligns Bayesian nonparametrics and model-based reinforcement learning (MB-RL) to the functioning of the hippocampus (HC) and the ventral striatum (vStr)-a neuronal circuit that increasingly recognized to be an appropriate model system to understand goal-directed (spatial) decisions and planning mechanisms in the brain. We test the MB-RL agent in a contextual conditioning task that depends on intact hippocampus and ventral striatal (shell) function and show that it solves the task while showing key behavioral and neuronal signatures of the HC-vStr circuit. Our simulations also explore the benefits of biological forms of look-ahead prediction (forward sweeps) during both learning and control. This article thus contributes to fill the gap between our current understanding of computational algorithms and biological realizations of (model-based) reinforcement learning.
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Affiliation(s)
- Ivilin Peev Stoianov
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Cyriel M. A. Pennartz
- University of Amsterdam, Swammerdam Institute for Life Sciences–Center for Neuroscience Amsterdam, The Netherlands
| | - Carien S. Lansink
- University of Amsterdam, Swammerdam Institute for Life Sciences–Center for Neuroscience Amsterdam, The Netherlands
| | - Giovani Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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32
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Soman K, Muralidharan V, Chakravarthy VS. A unified hierarchical oscillatory network model of head direction cells, spatially periodic cells, and place cells. Eur J Neurosci 2018; 47:1266-1281. [PMID: 29575125 DOI: 10.1111/ejn.13918] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 02/09/2018] [Accepted: 03/12/2018] [Indexed: 01/11/2023]
Abstract
Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the Velocity Driven Oscillatory Network (VDON) and Locomotor Driven Oscillatory Network. Both models have basically three stages in common such as direction encoding stage, path integration (PI) stage, and a stage of unsupervised learning of PI values. In the first model, the following three stages are implemented: head direction layer, frequency modulation by a layer of oscillatory neurons, and an unsupervised stage that extracts the principal components from the oscillator outputs. In the second model, a refined version of the first model, the stages are extraction of velocity representation from the locomotor input, frequency modulation by a layer of oscillators, and two cascaded unsupervised stages consisting of the lateral anti-hebbian network. The principal component stage of VDON exhibits grid cell-like spatially periodic responses including hexagonal firing fields. Locomotor Driven Oscillatory Network shows the emergence of spatially periodic grid cells and periodically active border-like cells in its lower layer; place cell responses are found in its higher layer. This model shows the inheritance of phase precession from grid cell to place cell in both one- and two-dimensional spaces. It also shows a novel result on the influence of locomotion rhythms on the grid cell activity. The study thus presents a comprehensive, unifying hierarchical model for hippocampal spatial cells.
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Affiliation(s)
- Karthik Soman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Vignesh Muralidharan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Vaddadi Srinivasa Chakravarthy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
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33
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Weghenkel B, Wiskott L. Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Comput 2018; 30:1151-1179. [PMID: 29566353 DOI: 10.1162/neco_a_01070] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The computational principles of slowness and predictability have been proposed to describe aspects of information processing in the visual system. From the perspective of slowness being a limited special case of predictability we investigate the relationship between these two principles empirically. On a collection of real-world data sets we compare the features extracted by slow feature analysis (SFA) to the features of three recently proposed methods for predictable feature extraction: forecastable component analysis, predictable feature analysis, and graph-based predictable feature analysis. Our experiments show that the predictability of the learned features is highly correlated, and, thus, SFA appears to effectively implement a method for extracting predictable features according to different measures of predictability.
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Affiliation(s)
- Björn Weghenkel
- Institut für Neuroinformatik, Ruhr-Universität, 44801 Bochum, Germany
| | - Laurenz Wiskott
- Institut für Neuroinformatik, Ruhr-Universität, 44801 Bochum, Germany
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Fang J, Rüther N, Bellebaum C, Wiskott L, Cheng S. The Interaction between Semantic Representation and Episodic Memory. Neural Comput 2018; 30:293-332. [DOI: 10.1162/neco_a_01044] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.
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Affiliation(s)
- Jing Fang
- Mercator Research Group “Structure of Memory,” Institute for Neural Computation, and Faculty of Psychology, Ruhr University Bochum, Bochum 44801, Germany
| | - Naima Rüther
- Faculty of Psychology, Ruhr University Bochum, Bochum 44801, Germany
| | - Christian Bellebaum
- Institute of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Laurenz Wiskott
- Institute for Neural Computation, Ruhr University Bochum, Bochum 44801, Germany
| | - Sen Cheng
- Mercator Research Group “Structure of Memory” and Institute for Neural Computation, Ruhr University Bochum, Bochum 44801, Germany
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35
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A dual fast and slow feature interaction in biologically inspired visual recognition of human action. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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36
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Weber SN, Sprekeler H. Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity. eLife 2018; 7:34560. [PMID: 29465399 PMCID: PMC5927772 DOI: 10.7554/elife.34560] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 02/19/2018] [Indexed: 01/27/2023] Open
Abstract
Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns - in both their selectivity and their invariance - arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.
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Affiliation(s)
- Simon Nikolaus Weber
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer ScienceTechnische Universität BerlinBerlinGermany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer ScienceTechnische Universität BerlinBerlinGermany
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37
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Zafeiriou L, Panagakis Y, Pantic M, Zafeiriou S. Nonnegative Decompositions for Dynamic Visual Data Analysis. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2017; 26:5603-5617. [PMID: 28783634 DOI: 10.1109/tip.2017.2735186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The analysis of high-dimensional, possibly temporally misaligned, and time-varying visual data is a fundamental task in disciplines, such as image, vision, and behavior computing. In this paper, we focus on dynamic facial behavior analysis and in particular on the analysis of facial expressions. Distinct from the previous approaches, where sets of facial landmarks are used for face representation, raw pixel intensities are exploited for: 1) unsupervised analysis of the temporal phases of facial expressions and facial action units (AUs) and 2) temporal alignment of a certain facial behavior displayed by two different persons. To this end, the slow features nonnegative matrix factorization (SFNMF) is proposed in order to learn slow varying parts-based representations of time varying sequences capturing the underlying dynamics of temporal phenomena, such as facial expressions. Moreover, the SFNMF is extended in order to handle two temporally misaligned data sequences depicting the same visual phenomena. To do so, the dynamic time warping is incorporated into the SFNMF, allowing the temporal alignment of the data sets onto the subspace spanned by the estimated nonnegative shared latent features amongst the two visual sequences. Extensive experimental results in two video databases demonstrate the effectiveness of the proposed methods in: 1) unsupervised detection of the temporal phases of posed and spontaneous facial events and 2) temporal alignment of facial expressions, outperforming by a large margin the state-of-the-art methods that they are compared to.
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38
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Wood JN. Spontaneous Preference for Slowly Moving Objects in Visually Naïve Animals. Open Mind (Camb) 2017. [DOI: 10.1162/opmi_a_00012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
To perceive the world successfully, newborns need certain types of visual experiences. The development of object recognition, for example, requires visual experience with slowly moving objects. To date, however, it is unknown whether newborns actively seek out the best visual experiences for developing object recognition. To address this question, I used an automated controlled-rearing method to examine whether visually naïve animals (newborn chicks) seek out slowly moving objects. Despite receiving equal exposure to slowly and to quickly rotating objects, the majority of the chicks developed a preference for slowly rotating objects. This preference was robust, producing large effect sizes across objects, experiments, and successive test days. These results indicate that newborn brains rapidly develop mechanisms for orienting young animals toward optimal visual experiences, thus facilitating the development of object recognition. This study also demonstrates that automation can be a valuable tool for studying the origins and development of visual preferences.
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Affiliation(s)
- Justin N. Wood
- Department of Psychology, University of Southern California
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39
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40
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Neher T, Azizi AH, Cheng S. From grid cells to place cells with realistic field sizes. PLoS One 2017; 12:e0181618. [PMID: 28750005 PMCID: PMC5531553 DOI: 10.1371/journal.pone.0181618] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 07/05/2017] [Indexed: 01/10/2023] Open
Abstract
While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.
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Affiliation(s)
- Torsten Neher
- Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany
- International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Amir Hossein Azizi
- Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany
| | - Sen Cheng
- Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany
- International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany
- * E-mail:
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41
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Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. ARTIF INTELL 2017. [DOI: 10.1016/j.artint.2015.02.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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42
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Lőrincz A, Sárkány A. Semi-Supervised Learning of Cartesian Factors: A Top-Down Model of the Entorhinal Hippocampal Complex. Front Psychol 2017; 8:215. [PMID: 28270783 PMCID: PMC5318397 DOI: 10.3389/fpsyg.2017.00215] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 02/03/2017] [Indexed: 01/27/2023] Open
Abstract
The existence of place cells (PCs), grid cells (GCs), border cells (BCs), and head direction cells (HCs) as well as the dependencies between them have been enigmatic. We make an effort to explain their nature by introducing the concept of Cartesian Factors. These factors have specific properties: (i) they assume and complement each other, like direction and position and (ii) they have localized discrete representations with predictive attractors enabling implicit metric-like computations. In our model, HCs make the distributed and local representation of direction. Predictive attractor dynamics on that network forms the Cartesian Factor "direction." We embed these HCs and idiothetic visual information into a semi-supervised sparse autoencoding comparator structure that compresses its inputs and learns PCs, the distributed local and direction independent (allothetic) representation of the Cartesian Factor of global space. We use a supervised, information compressing predictive algorithm and form direction sensitive (oriented) GCs from the learned PCs by means of an attractor-like algorithm. Since the algorithm can continue the grid structure beyond the region of the PCs, i.e., beyond its learning domain, thus the GCs and the PCs together form our metric-like Cartesian Factors of space. We also stipulate that the same algorithm can produce BCs. Our algorithm applies (a) a bag representation that models the "what system" and (b) magnitude ordered place cell activities that model either the integrate-and-fire mechanism, or theta phase precession, or both. We relate the components of the algorithm to the entorhinal-hippocampal complex and to its working. The algorithm requires both spatial and lifetime sparsification that may gain support from the two-stage memory formation of this complex.
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43
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Savelli F, Luck JD, Knierim JJ. Framing of grid cells within and beyond navigation boundaries. eLife 2017; 6. [PMID: 28084992 PMCID: PMC5271608 DOI: 10.7554/elife.21354] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/11/2017] [Indexed: 12/21/2022] Open
Abstract
Grid cells represent an ideal candidate to investigate the allocentric determinants of the brain's cognitive map. Most studies of grid cells emphasized the roles of geometric boundaries within the navigational range of the animal. Behaviors such as novel route-taking between local environments indicate the presence of additional inputs from remote cues beyond the navigational borders. To investigate these influences, we recorded grid cells as rats explored an open-field platform in a room with salient, remote cues. The platform was rotated or translated relative to the room frame of reference. Although the local, geometric frame of reference often exerted the strongest control over the grids, the remote cues demonstrated a consistent, sometimes dominant, countervailing influence. Thus, grid cells are controlled by both local geometric boundaries and remote spatial cues, consistent with prior studies of hippocampal place cells and providing a rich representational repertoire to support complex navigational (and perhaps mnemonic) processes.
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Affiliation(s)
- Francesco Savelli
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United States
| | - J D Luck
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United States
| | - James J Knierim
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United States.,Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, United States
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44
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Brea J, Gerstner W. Does computational neuroscience need new synaptic learning paradigms? Curr Opin Behav Sci 2016. [DOI: 10.1016/j.cobeha.2016.05.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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45
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Marblestone AH, Wayne G, Kording KP. Toward an Integration of Deep Learning and Neuroscience. Front Comput Neurosci 2016; 10:94. [PMID: 27683554 PMCID: PMC5021692 DOI: 10.3389/fncom.2016.00094] [Citation(s) in RCA: 243] [Impact Index Per Article: 30.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 08/24/2016] [Indexed: 01/22/2023] Open
Abstract
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.
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Affiliation(s)
- Adam H. Marblestone
- Synthetic Neurobiology Group, Massachusetts Institute of Technology, Media LabCambridge, MA, USA
| | | | - Konrad P. Kording
- Rehabilitation Institute of Chicago, Northwestern UniversityChicago, IL, USA
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46
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Raudies F, Hinman JR, Hasselmo ME. Modelling effects on grid cells of sensory input during self-motion. J Physiol 2016; 594:6513-6526. [PMID: 27094096 DOI: 10.1113/jp270649] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 01/29/2016] [Indexed: 01/07/2023] Open
Abstract
The neural coding of spatial location for memory function may involve grid cells in the medial entorhinal cortex, but the mechanism of generating the spatial responses of grid cells remains unclear. This review describes some current theories and experimental data concerning the role of sensory input in generating the regular spatial firing patterns of grid cells, and changes in grid cell firing fields with movement of environmental barriers. As described here, the influence of visual features on spatial firing could involve either computations of self-motion based on optic flow, or computations of absolute position based on the angle and distance of static visual cues. Due to anatomical selectivity of retinotopic processing, the sensory features on the walls of an environment may have a stronger effect on ventral grid cells that have wider spaced firing fields, whereas the sensory features on the ground plane may influence the firing of dorsal grid cells with narrower spacing between firing fields. These sensory influences could contribute to the potential functional role of grid cells in guiding goal-directed navigation.
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Affiliation(s)
- Florian Raudies
- Center for Systems Neuroscience, Centre for Memory and Brain, Department of Psychological and Brain Sciences and Graduate Program for Neuroscience, Boston University, 2 Cummington Mall, Boston, MA, 02215, USA
| | - James R Hinman
- Center for Systems Neuroscience, Centre for Memory and Brain, Department of Psychological and Brain Sciences and Graduate Program for Neuroscience, Boston University, 2 Cummington Mall, Boston, MA, 02215, USA
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Centre for Memory and Brain, Department of Psychological and Brain Sciences and Graduate Program for Neuroscience, Boston University, 2 Cummington Mall, Boston, MA, 02215, USA
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47
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Kompella VR, Luciw M, Stollenga MF, Schmidhuber J. Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis. Neural Comput 2016; 28:1599-662. [PMID: 27348735 DOI: 10.1162/neco_a_00855] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Consider a self-motivated artificial agent who is exploring a complex environment. Part of the complexity is due to the raw high-dimensional sensory input streams, which the agent needs to make sense of. Such inputs can be compactly encoded through a variety of means; one of these is slow feature analysis (SFA). Slow features encode spatiotemporal regularities, which are information-rich explanatory factors (latent variables) underlying the high-dimensional input streams. In our previous work, we have shown how slow features can be learned incrementally, while the agent explores its world, and modularly, such that different sets of features are learned for different parts of the environment (since a single set of regularities does not explain everything). In what order should the agent explore the different parts of the environment? Following Schmidhuber's theory of artificial curiosity, the agent should always concentrate on the area where it can learn the easiest-to-learn set of features that it has not already learned. We formalize this learning problem and theoretically show that, using our model, called curiosity-driven modular incremental slow feature analysis, the agent on average will learn slow feature representations in order of increasing learning difficulty, under certain mild conditions. We provide experimental results to support the theoretical analysis.
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Affiliation(s)
| | - Matthew Luciw
- IDSIA, SUPSI, USI, Galleria 2, Manno-Lugano 6928, Switzerland
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48
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Integrated information theory: from consciousness to its physical substrate. Nat Rev Neurosci 2016; 17:450-61. [DOI: 10.1038/nrn.2016.44] [Citation(s) in RCA: 644] [Impact Index Per Article: 80.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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49
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Wood JN, Wood SMW. The development of newborn object recognition in fast and slow visual worlds. Proc Biol Sci 2016; 283:20160166. [PMID: 27097925 PMCID: PMC4855384 DOI: 10.1098/rspb.2016.0166] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Accepted: 03/29/2016] [Indexed: 11/12/2022] Open
Abstract
Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world.
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Affiliation(s)
- Justin N Wood
- Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
| | - Samantha M W Wood
- Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
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50
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Bellec G, Galtier M, Brette R, Yger P. Slow feature analysis with spiking neurons and its application to audio stimuli. J Comput Neurosci 2016; 40:317-29. [PMID: 27075919 DOI: 10.1007/s10827-016-0599-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Revised: 01/31/2016] [Accepted: 02/29/2016] [Indexed: 10/22/2022]
Abstract
Extracting invariant features in an unsupervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech. While various algorithms have been proposed to perform such a task, Slow Feature Analysis (SFA) uses time as a means of detecting those invariants, extracting the slowly time-varying components in the input signals. In this work, we address the question of how such an algorithm can be implemented by neurons, and apply it in the context of audio stimuli. We propose a projected gradient implementation of SFA that can be adapted to a Hebbian like learning rule dealing with biologically plausible neuron models. Furthermore, we show that a Spike-Timing Dependent Plasticity learning rule, shaped as a smoothed second derivative, implements SFA for spiking neurons. The theory is supported by numerical simulations, and to illustrate a simple use of SFA, we have applied it to auditory signals. We show that a single SFA neuron can learn to extract the tempo in sound recordings.
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Affiliation(s)
- Guillaume Bellec
- Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France. .,INSERM, U968, Paris, France. .,CNRS, UMR7210, Paris, France.
| | - Mathieu Galtier
- European Institute for Theoretical Neuroscience CNRS UNIC UPR-3293, Paris, France
| | - Romain Brette
- Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France.,INSERM, U968, Paris, France.,CNRS, UMR7210, Paris, France
| | - Pierre Yger
- Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France.,INSERM, U968, Paris, France.,CNRS, UMR7210, Paris, France.,Institut d'Etudes de la Cognition, ENS, Paris, France
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