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Chauvière L. Update on temporal lobe‐dependent information processing, in health and disease. Eur J Neurosci 2019; 51:2159-2204. [DOI: 10.1111/ejn.14594] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/06/2019] [Accepted: 09/27/2019] [Indexed: 01/29/2023]
Affiliation(s)
- Laëtitia Chauvière
- INSERM U1266 Institut de Psychiatrie et de Neurosciences de Paris (IPNP) Paris France
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2
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Attili SM, Silva MFM, Nguyen TV, Ascoli GA. Cell numbers, distribution, shape, and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas. Brain Struct Funct 2019; 224:2883-2897. [PMID: 31444616 PMCID: PMC6778719 DOI: 10.1007/s00429-019-01940-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 08/13/2019] [Indexed: 01/07/2023]
Abstract
Quantifying the distribution of cells in every brain region is fundamental to attaining a comprehensive census of distinct neuronal and glial types. Until recently, estimating neuron numbers involved time-consuming procedures that were practically limited to stereological sampling. Progress in open-source image recognition software, growth in computing power, and unprecedented neuroinformatics developments now offer the potentially paradigm-shifting alternative of comprehensive cell-by-cell analysis in an entire brain region. The Allen Brain Atlas provides free digital access to complete series of raw Nissl-stained histological section images along with regional delineations. Automated cell segmentation of these data enables reliable and reproducible high-throughput quantification of regional variations in cell count, density, size, and shape at whole-system scale. While this strategy is directly applicable to any regions of the mouse brain, we first deploy it here on the closed-loop circuit of the hippocampal formation: the medial and lateral entorhinal cortices; dentate gyrus (DG); areas Cornu Ammonis 3 (CA3), CA2, and CA1; and dorsal and ventral subiculum. Using two independent image processing pipelines and the adult mouse reference atlas, we report the first cellular-level soma segmentation in every sub-region and non-principal layer of the left hippocampal formation through the full rostral-caudal extent. It is important to note that our techniques excluded the layers with the largest number of cells, DG granular and CA pyramidal, due to dense packing. The numerical estimates for the remaining layers are corroborated by traditional stereological sampling on a data subset and well match sparse published reports.
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Affiliation(s)
- Sarojini M Attili
- Center for Neural Informatics Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Marcos F M Silva
- Center for Neural Informatics Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Thuy-Vi Nguyen
- Center for Neural Informatics Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
- Duke University, Durham, NC, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
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3
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Sherrill KR, Chrastil ER, Aselcioglu I, Hasselmo ME, Stern CE. Structural Differences in Hippocampal and Entorhinal Gray Matter Volume Support Individual Differences in First Person Navigational Ability. Neuroscience 2018; 380:123-131. [DOI: 10.1016/j.neuroscience.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 03/13/2018] [Accepted: 04/06/2018] [Indexed: 12/12/2022]
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4
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Ferrante M, Tahvildari B, Duque A, Hadzipasic M, Salkoff D, Zagha EW, Hasselmo ME, McCormick DA. Distinct Functional Groups Emerge from the Intrinsic Properties of Molecularly Identified Entorhinal Interneurons and Principal Cells. Cereb Cortex 2018; 27:3186-3207. [PMID: 27269961 DOI: 10.1093/cercor/bhw143] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Inhibitory interneurons are an important source of synaptic inputs that may contribute to network mechanisms for coding of spatial location by entorhinal cortex (EC). The intrinsic properties of inhibitory interneurons in the EC of the mouse are mostly undescribed. Intrinsic properties were recorded from known cell types, such as, stellate and pyramidal cells and 6 classes of molecularly identified interneurons (regulator of calcineurin 2, somatostatin, serotonin receptor 3a, neuropeptide Y neurogliaform (NGF), neuropeptide Y non-NGF, and vasoactive intestinal protein) in acute brain slices. We report a broad physiological diversity between and within cell classes. We also found differences in the ability to produce postinhibitory rebound spikes and in the frequency and amplitude of incoming EPSPs. To understand the source of this intrinsic variability we applied hierarchical cluster analysis to functionally classify neurons. These analyses revealed physiologically derived cell types in EC that mostly corresponded to the lines identified by biomarkers with a few unexpected and important differences. Finally, we reduced the complex multidimensional space of intrinsic properties to the most salient five that predicted the cellular biomolecular identity with 81.4% accuracy. These results provide a framework for the classification of functional subtypes of cortical neurons by their intrinsic membrane properties.
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Affiliation(s)
- Michele Ferrante
- Center for Memory and Brain.,Center for Systems Neuroscience.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Babak Tahvildari
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Alvaro Duque
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Muhamed Hadzipasic
- Interdepartmental Program in Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - David Salkoff
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Edward William Zagha
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Michael E Hasselmo
- Center for Memory and Brain.,Center for Systems Neuroscience.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - David A McCormick
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
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5
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Goh JOS, Hung HY, Su YS. A conceptual consideration of the free energy principle in cognitive maps: How cognitive maps help reduce surprise. PSYCHOLOGY OF LEARNING AND MOTIVATION 2018. [DOI: 10.1016/bs.plm.2018.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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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: 234] [Impact Index Per Article: 29.3] [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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7
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Michelmann S, Bowman H, Hanslmayr S. The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans. PLoS Biol 2016; 14:e1002528. [PMID: 27494601 PMCID: PMC4975452 DOI: 10.1371/journal.pbio.1002528] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 06/23/2016] [Indexed: 01/12/2023] Open
Abstract
Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans.
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Affiliation(s)
| | - Howard Bowman
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
- School of Computing, University of Kent, Canterbury, United Kingdom
| | - Simon Hanslmayr
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Friston K, Buzsáki G. The Functional Anatomy of Time: What and When in the Brain. Trends Cogn Sci 2016; 20:500-511. [PMID: 27261057 DOI: 10.1016/j.tics.2016.05.001] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 04/24/2016] [Accepted: 05/02/2016] [Indexed: 11/17/2022]
Abstract
This Opinion article considers the implications for functional anatomy of how we represent temporal structure in our exchanges with the world. It offers a theoretical treatment that tries to make sense of the architectural principles seen in mammalian brains. Specifically, it considers a factorisation between representations of temporal succession and representations of content or, heuristically, a segregation into when and what. This segregation may explain the central role of the hippocampus in neuronal hierarchies while providing a tentative explanation for recent observations of how ordinal sequences are encoded. The implications for neuroanatomy and physiology may have something important to say about how self-organised cell assembly sequences enable the brain to exhibit purposeful behaviour that transcends the here and now.
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Affiliation(s)
- Karl Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.
| | - Gyorgy Buzsáki
- NYU Neuroscience Institute, School of Medicine, New York University, New York, NY 10016, USA; Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
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