1
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Zhang Z, Tang F, Li Y, Feng X. A spatial transformation-based CAN model for information integration within grid cell modules. Cogn Neurodyn 2024; 18:1861-1876. [PMID: 39104694 PMCID: PMC11297887 DOI: 10.1007/s11571-023-10047-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/13/2023] [Accepted: 11/26/2023] [Indexed: 08/07/2024] Open
Abstract
The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.
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Affiliation(s)
- Zhihui Zhang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Fengzhen Tang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Yiping Li
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Xisheng Feng
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
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2
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Zhang Z, Tang F, Li Y, Feng X. Modeling the grid cell activity based on cognitive space transformation. Cogn Neurodyn 2024; 18:1227-1243. [PMID: 38826659 PMCID: PMC11143158 DOI: 10.1007/s11571-023-09972-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/09/2023] [Accepted: 04/11/2023] [Indexed: 06/04/2024] Open
Abstract
The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.
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Affiliation(s)
- Zhihui Zhang
- University of Science and Technology of China, Hefei, 230026 China
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Fengzhen Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Yiping Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
| | - Xisheng Feng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016 China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
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3
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Sutton NM, Gutiérrez-Guzmán BE, Dannenberg H, Ascoli GA. A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology. Int J Mol Sci 2024; 25:6059. [PMID: 38892248 PMCID: PMC11173171 DOI: 10.3390/ijms25116059] [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: 04/29/2024] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 06/21/2024] Open
Abstract
Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications.
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Affiliation(s)
- Nate M. Sutton
- Bioengineering Department, George Mason University, Fairfax, VA 22030, USA; (N.M.S.); (B.E.G.-G.); (H.D.)
| | - Blanca E. Gutiérrez-Guzmán
- Bioengineering Department, George Mason University, Fairfax, VA 22030, USA; (N.M.S.); (B.E.G.-G.); (H.D.)
| | - Holger Dannenberg
- Bioengineering Department, George Mason University, Fairfax, VA 22030, USA; (N.M.S.); (B.E.G.-G.); (H.D.)
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA 22030, USA
| | - Giorgio A. Ascoli
- Bioengineering Department, George Mason University, Fairfax, VA 22030, USA; (N.M.S.); (B.E.G.-G.); (H.D.)
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA 22030, USA
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4
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Sutton N, Gutiérrez-Guzmán B, Dannenberg H, Ascoli GA. A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.591748. [PMID: 38746202 PMCID: PMC11092518 DOI: 10.1101/2024.04.29.591748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal was to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software is released as open source to enable broad community reuse and encourage novel applications.
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Affiliation(s)
- Nate Sutton
- Bioengineering Department, at George Mason University
| | | | - Holger Dannenberg
- Bioengineering Department, at George Mason University
- Interdisciplinary Program in Neuroscience at George Mason University
| | - Giorgio A. Ascoli
- Bioengineering Department, at George Mason University
- Interdisciplinary Program in Neuroscience at George Mason University
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5
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Hoffman C, Cheng J, Morales R, Ji D, Dabaghian Y. Altered patterning of neural activity in a tauopathy mouse model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.23.586417. [PMID: 38585991 PMCID: PMC10996513 DOI: 10.1101/2024.03.23.586417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales-spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal's location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions.
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Affiliation(s)
- C Hoffman
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
| | - J Cheng
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - R Morales
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
| | - D Ji
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - Y Dabaghian
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
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6
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Reinshagen A. Grid cells: the missing link in understanding Parkinson's disease? Front Neurosci 2024; 18:1276714. [PMID: 38389787 PMCID: PMC10881698 DOI: 10.3389/fnins.2024.1276714] [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: 08/12/2023] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
The mechanisms underlying Parkinson's disease (PD) are complex and not fully understood, and the box-and-arrow model among other current models present significant challenges. This paper explores the potential role of the allocentric brain and especially its grid cells in several PD motor symptoms, including bradykinesia, kinesia paradoxa, freezing of gait, the bottleneck phenomenon, and their dependency on cueing. It is argued that central hubs, like the locus coeruleus and the pedunculopontine nucleus, often narrowly interpreted in the context of PD, play an equally important role in governing the allocentric brain as the basal ganglia. Consequently, the motor and secondary motor (e.g., spatially related) symptoms of PD linked with dopamine depletion may be more closely tied to erroneous computation by grid cells than to the basal ganglia alone. Because grid cells and their associated central hubs introduce both spatial and temporal information to the brain influencing velocity perception they may cause bradykinesia or hyperkinesia as well. In summary, PD motor symptoms may primarily be an allocentric disturbance resulting from virtual faulty computation by grid cells revealed by dopamine depletion in PD.
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Vedururu Srinivas A, Canavier CC. Phase Resetting Curves Determine Stability of Synchrony in One and Two Clusters of Pulse Coupled Oscillators with Delays. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575222. [PMID: 38260324 PMCID: PMC10802586 DOI: 10.1101/2024.01.11.575222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Previously, our group used phase response curves under a pulsatile coupling assumption to determine the stability of synchrony within a cluster of neural oscillators and between two clusters of oscillators. The interactions of the within and between cluster terms were considered, demonstrating how an alternating firing pattern between clusters could stabilize within cluster synchrony-even in clusters unable to synchronize themselves in isolation. In addition, criteria were derived for synchrony between two pulse coupled oscillators with synaptic delays. In this study, we update our previous work on one and two clusters of coupled oscillators to include delays and demonstrate the validity of the results using a map of the firing intervals based on the phase resetting curve. We use self-connected neurons to represent clusters and derive conditions under which an oscillator can phase-lock itself with a delayed input. Although this analysis only strictly applies to identical neurons receiving identical synapses from the same number of neurons, the principles are general and can be used to understand how to promote or impede synchrony in physiological networks of neurons. Heterogeneity can be interpreted as a form of frozen noise, and approximate synchrony can be sustained despite heterogeneity. The pulse-coupled oscillator model can not only be used to describe biological neuronal networks but also cardiac pacemakers, lasers, fireflies, artificial neural networks, social self-organization, and wireless sensor networks.
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8
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Schøyen V, Pettersen MB, Holzhausen K, Fyhn M, Malthe-Sørenssen A, Lepperød ME. Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents. iScience 2023; 26:108102. [PMID: 37867941 PMCID: PMC10589895 DOI: 10.1016/j.isci.2023.108102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 08/25/2023] [Accepted: 09/27/2023] [Indexed: 10/24/2023] Open
Abstract
It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.
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Affiliation(s)
- Vemund Schøyen
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
| | | | | | - Marianne Fyhn
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
| | - Anders Malthe-Sørenssen
- Department of Physics, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
| | - Mikkel Elle Lepperød
- Department of Physics, University of Oslo, Oslo 0313, Norway
- Department of Biosciences, University of Oslo, Oslo 0313, Norway
- Simula Research Laboratory, Norway
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9
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Xu Z, Mo F, Yang G, Fan P, Lu B, Liang W, Kong F, Jing L, Xu W, Liu J, Wang M, Wu Y, Cai X. Impaired Spatial Firing Representations of Neurons in the Medial Entorhinal Cortex of the Epileptic Rat Using Microelectrode Arrays. RESEARCH (WASHINGTON, D.C.) 2023; 6:0229. [PMID: 37719050 PMCID: PMC10503993 DOI: 10.34133/research.0229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/28/2023] [Indexed: 09/19/2023]
Abstract
Epilepsy severely impairs the cognitive behavior of patients. It remains unclear whether epilepsy-induced cognitive impairment is associated with neuronal activities in the medial entorhinal cortex (MEC), a region known for its involvement in spatial cognition. To explore this neural mechanism, we recorded the spikes and local field potentials from MEC neurons in lithium-pilocarpine-induced epileptic rats using self-designed microelectrode arrays. Through the open field test, we identified spatial cells exhibiting spatially selective firing properties and assessed their spatial representations in relation to the progression of epilepsy. Meanwhile, we analyzed theta oscillations and theta modulation in both excitatory and inhibitory neurons. Furthermore, we used a novel object recognition test to evaluate changes in spatial cognitive ability of epileptic rats. After the epilepsy modeling, the spatial tuning of various types of spatial cells had suffered a rapid and pronounced damage during the latent period (1 to 5 d). Subsequently, the firing characteristics and theta oscillations were impaired. In the chronic period (>10 d), the performance in the novel object experiment deteriorated. In conclusion, our study demonstrates the detrimental effect on spatial representations and electrophysiological properties of MEC neurons in the epileptic latency, suggesting the potential use of these changes as a "functional biomarker" for predicting cognitive impairment caused by epilepsy.
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Affiliation(s)
- Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fan Mo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gucheng Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Penghui Fan
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Botao Lu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fanli Kong
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luyi Jing
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Juntao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yirong Wu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute,
Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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10
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Traub RD, Whittington MA, Cunningham MO. Simulation of oscillatory dynamics induced by an approximation of grid cell output. Rev Neurosci 2023; 34:517-532. [PMID: 36326795 PMCID: PMC10329426 DOI: 10.1515/revneuro-2022-0107] [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: 08/17/2022] [Accepted: 10/06/2022] [Indexed: 07/20/2023]
Abstract
Grid cells, in entorhinal cortex (EC) and related structures, signal animal location relative to hexagonal tilings of 2D space. A number of modeling papers have addressed the question of how grid firing behaviors emerge using (for example) ideas borrowed from dynamical systems (attractors) or from coupled oscillator theory. Here we use a different approach: instead of asking how grid behavior emerges, we take as a given the experimentally observed intracellular potentials of superficial medial EC neurons during grid firing. Employing a detailed neural circuit model modified from a lateral EC model, we then ask how the circuit responds when group of medial EC principal neurons exhibit such potentials, simultaneously with a simulated theta frequency input from the septal nuclei. The model predicts the emergence of robust theta-modulated gamma/beta oscillations, suggestive of oscillations observed in an in vitro medial EC experimental model (Cunningham, M.O., Pervouchine, D.D., Racca, C., Kopell, N.J., Davies, C.H., Jones, R.S.G., Traub, R.D., and Whittington, M.A. (2006). Neuronal metabolism governs cortical network response state. Proc. Natl. Acad. Sci. U S A 103: 5597-5601). Such oscillations result because feedback interneurons tightly synchronize with each other - despite the varying phases of the grid cells - and generate a robust inhibition-based rhythm. The lack of spatial specificity of the model interneurons is consistent with the lack of spatial periodicity in parvalbumin interneurons observed by Buetfering, C., Allen, K., and Monyer, H. (2014). Parvalbumin interneurons provide grid cell-driven recurrent inhibition in the medial entorhinal cortex. Nat. Neurosci. 17: 710-718. If in vivo EC gamma rhythms arise during exploration as our model predicts, there could be implications for interpreting disrupted spatial behavior and gamma oscillations in animal models of Alzheimer's disease and schizophrenia. Noting that experimental intracellular grid cell potentials closely resemble cortical Up states and Down states, during which fast oscillations also occur during Up states, we propose that the co-occurrence of slow principal cell depolarizations and fast network oscillations is a general property of the telencephalon, in both waking and sleep states.
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Affiliation(s)
- Roger D. Traub
- AI Foundations, IBM T.J. Watson Research Center, Yorktown Heights, NY10598, USA
- Department of Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA19104, USA
| | | | - Mark O. Cunningham
- Discipline of Physiology, School of Medicine, Trinity College Dublin, University of Dublin, 152-160 Pearse St., Dublin 2, Ireland
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11
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Topczewska A, Giacalone E, Pratt WS, Migliore M, Dolphin AC, Shah MM. T-type Ca 2+ and persistent Na + currents synergistically elevate ventral, not dorsal, entorhinal cortical stellate cell excitability. Cell Rep 2023; 42:112699. [PMID: 37368752 PMCID: PMC10687207 DOI: 10.1016/j.celrep.2023.112699] [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: 03/24/2022] [Revised: 03/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Dorsal and ventral medial entorhinal cortex (mEC) regions have distinct neural network firing patterns to differentially support functions such as spatial memory. Accordingly, mEC layer II dorsal stellate neurons are less excitable than ventral neurons. This is partly because the densities of inhibitory conductances are higher in dorsal than ventral neurons. Here, we report that T-type Ca2+ currents increase 3-fold along the dorsal-ventral axis in mEC layer II stellate neurons, with twice as much CaV3.2 mRNA in ventral mEC compared with dorsal mEC. Long depolarizing stimuli trigger T-type Ca2+ currents, which interact with persistent Na+ currents to elevate the membrane voltage and spike firing in ventral, not dorsal, neurons. T-type Ca2+ currents themselves prolong excitatory postsynaptic potentials (EPSPs) to enhance their summation and spike coupling in ventral neurons only. These findings indicate that T-type Ca2+ currents critically influence the dorsal-ventral mEC stellate neuron excitability gradient and, thereby, mEC dorsal-ventral circuit activity.
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Affiliation(s)
| | | | - Wendy S Pratt
- Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6BT, UK
| | - Michele Migliore
- Institute of Biophysics, National Research Council, 90146 Palermo, Italy
| | - Annette C Dolphin
- Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6BT, UK
| | - Mala M Shah
- Pharmacology, School of Pharmacy, University College London, London WC1N 4AX, UK.
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12
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Andrade-Talavera Y, Fisahn A, Rodríguez-Moreno A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry 2023; 28:2177-2188. [PMID: 36991134 PMCID: PMC10611582 DOI: 10.1038/s41380-023-02027-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/31/2023]
Abstract
In the mammalian brain information processing and storage rely on the complex coding and decoding events performed by neuronal networks. These actions are based on the computational ability of neurons and their functional engagement in neuronal assemblies where precise timing of action potential firing is crucial. Neuronal circuits manage a myriad of spatially and temporally overlapping inputs to compute specific outputs that are proposed to underly memory traces formation, sensory perception, and cognitive behaviors. Spike-timing-dependent plasticity (STDP) and electrical brain rhythms are suggested to underlie such functions while the physiological evidence of assembly structures and mechanisms driving both processes continues to be scarce. Here, we review foundational and current evidence on timing precision and cooperative neuronal electrical activity driving STDP and brain rhythms, their interactions, and the emerging role of glial cells in such processes. We also provide an overview of their cognitive correlates and discuss current limitations and controversies, future perspectives on experimental approaches, and their application in humans.
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Affiliation(s)
- Yuniesky Andrade-Talavera
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
| | - André Fisahn
- Department of Biosciences and Nutrition and Department of Women's and Children's Health, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Antonio Rodríguez-Moreno
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
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Yuan W, Zhi W, Ma L, Hu X, Wang Q, Zou Y, Wang L. Neural Oscillation Disorder in the Hippocampal CA1 Region of Different Alzheimer's Disease Mice. Curr Alzheimer Res 2023; 20:350-359. [PMID: 37559542 PMCID: PMC10661967 DOI: 10.2174/1567205020666230808122643] [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: 02/11/2023] [Revised: 05/16/2023] [Accepted: 06/30/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a well-known neurodegenerative disease that gradually induces neural network dysfunction and progressive memory deficits. Neural network activity is represented by rhythmic oscillations that influence local field potentials (LFPs). However, changes in hippocampal neural rhythmic oscillations in the early stage of AD remain largely unexplored. OBJECTIVE This study investigated neural rhythmic oscillations in 3-month-old APP/PS1 and 5x- FAD mice to assess early neural connectivity in AD. METHODS LFPs were recorded from the hippocampal CA1 region with implanted microelectrode arrays while the mice were in the awake resting stage. Welch fast Fourier transforms, continuous wavelet transforms, and phase-amplitude coupling analyses were used to compute the power density of different frequency bands and phase-amplitude modulation indices in the LFPs. RESULTS Our results showed impaired theta, low gamma, and high gamma frequency band power in APP/PS1 and 5xFAD mice during the awake resting stage. AD mice also showed decreased delta, alpha, and beta frequency band power. Impaired theta-low gamma and theta-high gamma phaseamplitude coupling were observed in 5xFAD mice. CONCLUSION This study revealed neural network activity differences in oscillation power and cross-frequency coupling in the early stage of AD, providing a new perspective for developing biomarkers for early AD diagnosis.
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Affiliation(s)
- Weiming Yuan
- Graduate Collaborative Training Base of Academy of Military Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
| | - Weijia Zhi
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
| | - Lizhen Ma
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
| | - Xiangjun Hu
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
| | - Qian Wang
- Department of Medical Imaging, Chinese PAP Beijing Corps Hospital, Beijing 100600, China
| | - Yong Zou
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
| | - Lifeng Wang
- Graduate Collaborative Training Base of Academy of Military Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China
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Via G, Baravalle R, Fernandez FR, White JA, Canavier CC. Interneuronal network model of theta-nested fast oscillations predicts differential effects of heterogeneity, gap junctions and short term depression for hyperpolarizing versus shunting inhibition. PLoS Comput Biol 2022; 18:e1010094. [PMID: 36455063 PMCID: PMC9747050 DOI: 10.1371/journal.pcbi.1010094] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 12/13/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Theta and gamma oscillations in the hippocampus have been hypothesized to play a role in the encoding and retrieval of memories. Recently, it was shown that an intrinsic fast gamma mechanism in medial entorhinal cortex can be recruited by optogenetic stimulation at theta frequencies, which can persist with fast excitatory synaptic transmission blocked, suggesting a contribution of interneuronal network gamma (ING). We calibrated the passive and active properties of a 100-neuron model network to capture the range of passive properties and frequency/current relationships of experimentally recorded PV+ neurons in the medial entorhinal cortex (mEC). The strength and probabilities of chemical and electrical synapses were also calibrated using paired recordings, as were the kinetics and short-term depression (STD) of the chemical synapses. Gap junctions that contribute a noticeable fraction of the input resistance were required for synchrony with hyperpolarizing inhibition; these networks exhibited theta-nested high frequency oscillations similar to the putative ING observed experimentally in the optogenetically-driven PV-ChR2 mice. With STD included in the model, the network desynchronized at frequencies above ~200 Hz, so for sufficiently strong drive, fast oscillations were only observed before the peak of the theta. Because hyperpolarizing synapses provide a synchronizing drive that contributes to robustness in the presence of heterogeneity, synchronization decreases as the hyperpolarizing inhibition becomes weaker. In contrast, networks with shunting inhibition required non-physiological levels of gap junctions to synchronize using conduction delays within the measured range.
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Affiliation(s)
- Guillem Via
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
| | - Roman Baravalle
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
| | - Fernando R. Fernandez
- Department of Biomedical Engineering, Center for Systems Neuroscience, Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - John A. White
- Department of Biomedical Engineering, Center for Systems Neuroscience, Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Carmen C. Canavier
- Louisiana State University Health Sciences Center, Department of Cell Biology and Anatomy, New Orleans, Louisiana, United States of America
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Li M, Cheng S, Fan J, Shang Z, Wan H, Yang L, Yang L. Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons. BMC ZOOL 2022; 7:54. [PMID: 37170160 PMCID: PMC10127027 DOI: 10.1186/s40850-022-00143-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The hippocampus plays an important role to support path planning and adjustment in goal-directed spatial navigation. While we still only have limited knowledge about how do the hippocampal neural activities, especially the functional connectivity patterns, change during the spatial path adjustment. In this study, we measured the behavioural indicators and local field potentials of the pigeon (Columba livia, male and female) during a goal-directed navigational task with the detour paradigm, exploring the changing patterns of the hippocampal functional network connectivity of the bird during the spatial path learning and adjustment.
Results
Our study demonstrates that the pigeons progressively learned to solve the path adjustment task after the preferred path is blocked suddenly. Behavioural results show that both the total duration and the path lengths pigeons completed the task during the phase of adjustment are significantly longer than those during the acquisition and recovery phases. Furthermore, neural results show that hippocampal functional connectivity selectively changed during path adjustment. Specifically, we identified depressed connectivity in lower bands (delta and theta) and elevated connectivity in higher bands (slow-gamma and fast-gamma).
Conclusions
These results feature both the behavioural response and neural representation of the avian spatial cognitive learning process, suggesting that the functional disarrangement and reorganization of the connectivity in the avian hippocampus during different phases may contribute to our further understanding of the potential mechanism of path learning and adjustment.
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Anesthetic modulations dissociate neuroelectric characteristics between sensory-evoked and spontaneous activities across bilateral rat somatosensory cortical laminae. Sci Rep 2022; 12:11661. [PMID: 35804171 PMCID: PMC9270342 DOI: 10.1038/s41598-022-13759-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
Spontaneous neural activity has been widely adopted to construct functional connectivity (FC) amongst distant brain regions. Although informative, the functional role and signaling mechanism of the resting state FC are not intuitive as those in stimulus/task-evoked activity. In order to bridge the gap, we investigated anesthetic modulation of both resting-state and sensory-evoked activities. We used two well-studied GABAergic anesthetics of varying dose (isoflurane: 0.5–2.0% and α-chloralose: 30 and 60 mg/kg∙h) and recorded changes in electrophysiology using a pair of laminar electrode arrays that encompass the entire depth of the bilateral somatosensory cortices (S1fl) in rats. Specifically, the study focused to describe how varying anesthesia conditions affect the resting state activities and resultant FC between bilateral hemispheres in comparison to those obtained by evoked responses. As results, isoflurane decreased the amplitude of evoked responses in a dose-dependent manner mostly due to the habituation of repetitive responses. However, α-chloralose rather intensified the amplitude without exhibiting habituation. No such diverging trend was observed for the spontaneous activity, in which both anesthetics increased the signal power. For α-chloralose, overall FC was similar to that obtained with the lowest dose of isoflurane at 0.5% while higher doses of isoflurane displayed increased FC. Interestingly, only α-chloralose elicited relatively much greater increases in the ipsi-stimulus evoked response (i.e., in S1fl ipsilateral to the stimulated forelimb) than those associated with the contra-stimulus response, suggesting enhanced neuronal excitability. Taken together, the findings demonstrate modulation of the FC profiles by anesthesia is highly non-linear, possibly with a distinct underlying mechanism that affects either resting state or evoked activities differently. Further, the current study warrants thorough investigation of the basal neuronal states prior to the interpretation of resting state FC and evoked activities for accurate understanding of neural signal processing and circuitry.
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17
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Yang X, Li X, Yuan Y, Sun T, Yang J, Deng B, Yu H, Gao A, Guan J. 40 Hz Blue LED Relieves the Gamma Oscillations Changes Caused by Traumatic Brain Injury in Rat. Front Neurol 2022; 13:882991. [PMID: 35800078 PMCID: PMC9253286 DOI: 10.3389/fneur.2022.882991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundPhotobiomodulation (PBM) using low-level light-emitting diodes (LEDs) can be rapidly applied to various neurological disorders safely and non-invasively.Materials and MethodsForty-eight rats were involved in this study. The traumatic brain injury (TBI) model of rat was set up by a controlled cortical impact (CCI) injury. An 8-channel cortex electrode EEG was fixed to two hemispheres, and gamma oscillations were extracted according to each electrode. A 40 hz blue LED stimulation was set at four points of the frontal and parietal regions for 60 s each, six times per day for 1 week. Modified Neurologic Severity Scores (mNSS) were used to evaluate the level of neurological function.ResultsIn the right-side TBI model, the gamma oscillation decreased in electrodes Fp2, T4, C4, and O2; but significantly increased after 1 week of 40 hz Blue LED intervention. In the left-side TBI model, the gamma oscillation decreased in electrodes Fp1, T3, C3, and O1; and similarly increased after 1 week of 40 hz Blue LED intervention. Both left and right side TBI rats performed significantly better in mNSS after 40 hz Blue LED intervention.ConclusionTBI causes the decrease of gamma oscillations on the injured side of the brain of rats. The 40 hz Blue LED therapy could relieve the gamma oscillation changes caused by TBI and improve the prognosis of TBI.
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Affiliation(s)
- Xiaoyu Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuepei Li
- Medical Simulation Center, Chengdu First People's Hospital, Chengdu, China
| | - Yikai Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Sun
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jingguo Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Deng
- College of Electronic Engineering (College of Meteorological Observation), Chengdu University of Information Technology, Chengdu, China
| | - Hang Yu
- College of Electronic Engineering (College of Meteorological Observation), Chengdu University of Information Technology, Chengdu, China
| | - Anliang Gao
- Department of Neurosurgery, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Junwen Guan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Junwen Guan
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Tukker JJ, Beed P, Brecht M, Kempter R, Moser EI, Schmitz D. Microcircuits for spatial coding in the medial entorhinal cortex. Physiol Rev 2022; 102:653-688. [PMID: 34254836 PMCID: PMC8759973 DOI: 10.1152/physrev.00042.2020] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The hippocampal formation is critically involved in learning and memory and contains a large proportion of neurons encoding aspects of the organism's spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields as well as a host of other functionally defined cell types including head direction cells, speed cells, border cells, and object-vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.
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Affiliation(s)
- John J Tukker
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
| | - Prateep Beed
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humbold-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Brecht
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edvard I Moser
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Trondheim, Norway
| | - Dietmar Schmitz
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humbold-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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19
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Zutshi I, Valero M, Fernández-Ruiz A, Buzsáki G. Extrinsic control and intrinsic computation in the hippocampal CA1 circuit. Neuron 2022; 110:658-673.e5. [PMID: 34890566 PMCID: PMC8857017 DOI: 10.1016/j.neuron.2021.11.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/01/2021] [Accepted: 11/12/2021] [Indexed: 10/19/2022]
Abstract
In understanding circuit operations, a key problem is the extent to which neuronal spiking reflects local computation or responses to upstream inputs. We addressed this issue in the hippocampus by performing combined optogenetic and pharmacogenetic local and upstream inactivation. Silencing the medial entorhinal cortex (mEC) largely abolished extracellular theta and gamma currents in CA1 while only moderately affecting firing rates. In contrast, CA3 and local CA1 silencing strongly decreased firing of CA1 neurons without affecting theta currents. Each perturbation reconfigured the CA1 spatial map. However, the ability of the CA1 circuit to support place field activity persisted, maintaining the same fraction of spatially tuned place fields and reliable assembly expression as in the intact mouse. Thus, the CA1 network can induce and maintain coordinated cell assemblies with minimal reliance on its inputs, but these inputs can effectively reconfigure and assist in maintaining stability of the CA1 map.
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Affiliation(s)
- Ipshita Zutshi
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA
| | - Manuel Valero
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA
| | - Antonio Fernández-Ruiz
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA
| | - György Buzsáki
- New York University Neuroscience Institute, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA.
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20
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Vandrey B, Armstrong J, Brown CM, Garden DLF, Nolan MF. Fan cells in lateral entorhinal cortex directly influence medial entorhinal cortex through synaptic connections in layer 1. eLife 2022; 11:83008. [PMID: 36562467 PMCID: PMC9822265 DOI: 10.7554/elife.83008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Standard models for spatial and episodic memory suggest that the lateral entorhinal cortex (LEC) and medial entorhinal cortex (MEC) send parallel independent inputs to the hippocampus, each carrying different types of information. Here, we evaluate the possibility that information is integrated between divisions of the entorhinal cortex prior to reaching the hippocampus. We demonstrate that, in mice, fan cells in layer 2 (L2) of LEC that receive neocortical inputs, and that project to the hippocampal dentate gyrus, also send axon collaterals to layer 1 (L1) of the MEC. Activation of inputs from fan cells evokes monosynaptic glutamatergic excitation of stellate and pyramidal cells in L2 of the MEC, typically followed by inhibition that contains fast and slow components mediated by GABAA and GABAB receptors, respectively. Inputs from fan cells also directly activate interneurons in L1 and L2 of MEC, with synaptic connections from L1 interneurons accounting for slow feedforward inhibition of L2 principal cell populations. The relative strength of excitation and inhibition following fan cell activation differs substantially between neurons and is largely independent of anatomical location. Our results demonstrate that the LEC, in addition to directly influencing the hippocampus, can activate or inhibit major hippocampal inputs arising from the MEC. Thus, local circuits in the superficial MEC may combine spatial information with sensory and higher order signals from the LEC, providing a substrate for integration of 'what' and 'where' components of episodic memories.
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Affiliation(s)
- Brianna Vandrey
- Centre for Discovery Brain Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Jack Armstrong
- Centre for Discovery Brain Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Christina M Brown
- Centre for Discovery Brain Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Derek LF Garden
- Centre for Discovery Brain Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of EdinburghEdinburghUnited Kingdom,Simons Initiative for the Developing Brain, University of EdinburghEdinburghUnited Kingdom,Centre for Statistics, University of EdinburghEdinburghUnited Kingdom
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21
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Kinetics and Connectivity Properties of Parvalbumin- and Somatostatin-Positive Inhibition in Layer 2/3 Medial Entorhinal Cortex. eNeuro 2022; 9:ENEURO.0441-21.2022. [PMID: 35105656 PMCID: PMC8856710 DOI: 10.1523/eneuro.0441-21.2022] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/14/2022] [Accepted: 01/22/2022] [Indexed: 01/19/2023] Open
Abstract
Parvalbumin-positive (Pvalb+) and somatostatin-positive (Sst+) cells are the two largest subgroups of inhibitory interneurons. Studies in visual cortex indicate that synaptic connections between Pvalb+ cells are common while connections between Sst+ interneurons have not been observed. The inhibitory connectivity and kinetics of these two interneuron subpopulations, however, have not been characterized in medial entorhinal cortex (mEC). Using fluorescence-guided paired recordings in mouse brain slices from interneurons and excitatory cells in layer 2/3 mEC, we found that, unlike neocortical measures, Sst+ cells inhibit each other, albeit with a lower probability than Pvalb+ cells (18% vs 36% for unidirectional connections). Gap junction connections were also more frequent between Pvalb+ cells than between Sst+ cells. Pvalb+ cells inhibited each other with larger conductances, smaller decay time constants, and shorter delays. Similarly, synaptic connections between Pvalb+ and excitatory cells were more likely and expressed faster decay times and shorter delays than those between Sst+ and excitatory cells. Inhibitory cells exhibited smaller synaptic decay time constants between interneurons than on their excitatory targets. Inhibition between interneurons also depressed faster, and to a greater extent. Finally, inhibition onto layer 2 pyramidal and stellate cells originating from Pvalb+ interneurons were very similar, with no significant differences in connection likelihood, inhibitory amplitude, and decay time. A model of short-term depression fitted to the data indicates that recovery time constants for refilling the available pool are in the range of 50-150 ms and that the fraction of the available pool released on each spike is in the range 0.2-0.5.
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22
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Han C, Wang T, Yang Y, Wu Y, Li Y, Dai W, Zhang Y, Wang B, Yang G, Cao Z, Kang J, Wang G, Li L, Yu H, Yeh CI, Xing D. Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex. PLoS Biol 2021; 19:e3001466. [PMID: 34932558 PMCID: PMC8691622 DOI: 10.1371/journal.pbio.3001466] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 11/03/2021] [Indexed: 12/26/2022] Open
Abstract
Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.
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Affiliation(s)
- Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yange Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Guanzhong Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ziqi Cao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jian Kang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gang Wang
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Liang Li
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Hongbo Yu
- Vision Research Laboratory, Center for Brain Science Research and School of Life Sciences, Fudan University, Shanghai, China
| | - Chun-I Yeh
- Department of Psychology, National Taiwan University, Taipei, Taiwan, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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23
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The Control of Rat Hippocampal Gamma Oscillation Strength by BK Channel Activity. Neuroscience 2021; 475:220-228. [PMID: 34509547 DOI: 10.1016/j.neuroscience.2021.09.002] [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: 03/04/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/20/2022]
Abstract
Neuronal network oscillations in the gamma frequency band (30-80 Hz, γ oscillations) are associated with the higher brain functions such as perception, attention, learning and memory. BK channels mediate rapid repolarization and fast afterhyperpolarization in neurons and control neuronal excitability, and potentially control hippocampal γ oscillations. In this study, we examined the effects of modulating BK channels on hippocampal γ oscillations in the absence or presence of Ca2+ influx through voltage-gated Ca2+ channels (VGCC) or Ca2+-permeable AMPA receptors (CP-AMPAR). We found that blocking BK channels enhanced γ power, without affecting oscillation frequency or regularity, suggesting that BK channel activity suppresses γ oscillations. Blocking either VGCC or CP-AMPAR itself enhanced γ power, and completely occluded the effect of BK channel blockers on γ oscillations, whereas blocking BK channels first could not prevent a further γ power increase upon blockade of either CP-AMPAR or VGCC. We propose that Ca2+ influx through VGCC or CP-AMPAR during γ oscillations, cause membrane BK channel activation and regulate hippocampal γ oscillation strength by negative feedback.
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24
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Gerlei KZ, Brown CM, Sürmeli G, Nolan MF. Deep entorhinal cortex: from circuit organization to spatial cognition and memory. Trends Neurosci 2021; 44:876-887. [PMID: 34593254 DOI: 10.1016/j.tins.2021.08.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 10/20/2022]
Abstract
The deep layers of the entorhinal cortex are important for spatial cognition, as well as memory storage, consolidation and retrieval. A long-standing hypothesis is that deep-layer neurons relay spatial and memory-related signals between the hippocampus and telencephalon. We review the implications of recent circuit-level analyses that suggest more complex roles. The organization of deep entorhinal layers is consistent with multi-stage processing by specialized cell populations; in this framework, hippocampal, neocortical, and subcortical inputs are integrated to generate representations for use by targets in the telencephalon and for feedback to the superficial entorhinal cortex and hippocampus. Addressing individual sublayers of the deep entorhinal cortex in future experiments and models will be important for establishing systems-level mechanisms for spatial cognition and episodic memory.
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Affiliation(s)
- Klára Z Gerlei
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Christina M Brown
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Gülşen Sürmeli
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK; Simons Initiative for the Developing Brain, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK; Simons Initiative for the Developing Brain, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK.
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Ter Wal M, Tiesinga PHE. Comprehensive characterization of oscillatory signatures in a model circuit with PV- and SOM-expressing interneurons. BIOLOGICAL CYBERNETICS 2021; 115:487-517. [PMID: 34628539 PMCID: PMC8551150 DOI: 10.1007/s00422-021-00894-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/06/2021] [Indexed: 05/06/2023]
Abstract
Neural circuits contain a wide variety of interneuron types, which differ in their biophysical properties and connectivity patterns. The two most common interneuron types, parvalbumin-expressing and somatostatin-expressing cells, have been shown to be differentially involved in many cognitive functions. These cell types also show different relationships with the power and phase of oscillations in local field potentials. The mechanisms that underlie the emergence of different oscillatory rhythms in neural circuits with more than one interneuron subtype, and the roles specific interneurons play in those mechanisms, are not fully understood. Here, we present a comprehensive analysis of all possible circuit motifs and input regimes that can be achieved in circuits comprised of excitatory cells, PV-like fast-spiking interneurons and SOM-like low-threshold spiking interneurons. We identify 18 unique motifs and simulate their dynamics over a range of input strengths. Using several characteristics, such as oscillation frequency, firing rates, phase of firing and burst fraction, we cluster the resulting circuit dynamics across motifs in order to identify patterns of activity and compare these patterns to behaviors that were generated in circuits with one interneuron type. In addition to the well-known PING and ING gamma oscillations and an asynchronous state, our analysis identified three oscillatory behaviors that were generated by the three-cell-type motifs only: theta-nested gamma oscillations, stable beta oscillations and theta-locked bursting behavior, which have also been observed in experiments. Our characterization provides a map to interpret experimental activity patterns and suggests pharmacological manipulations or optogenetics approaches to validate these conclusions.
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Affiliation(s)
- Marije Ter Wal
- Department of Neuroinformatics, Donders Institute, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands.
- School of Psychology, University of Birmingham, Edgbaston, B15 2TT, UK.
| | - Paul H E Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands
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Rueckemann JW, Sosa M, Giocomo LM, Buffalo EA. The grid code for ordered experience. Nat Rev Neurosci 2021; 22:637-649. [PMID: 34453151 PMCID: PMC9371942 DOI: 10.1038/s41583-021-00499-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
Entorhinal cortical grid cells fire in a periodic pattern that tiles space, which is suggestive of a spatial coordinate system. However, irregularities in the grid pattern as well as responses of grid cells in contexts other than spatial navigation have presented a challenge to existing models of entorhinal function. In this Perspective, we propose that hippocampal input provides a key informative drive to the grid network in both spatial and non-spatial circumstances, particularly around salient events. We build on previous models in which neural activity propagates through the entorhinal-hippocampal network in time. This temporal contiguity in network activity points to temporal order as a necessary characteristic of representations generated by the hippocampal formation. We advocate that interactions in the entorhinal-hippocampal loop build a topological representation that is rooted in the temporal order of experience. In this way, the structure of grid cell firing supports a learned topology rather than a rigid coordinate frame that is bound to measurements of the physical world.
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Affiliation(s)
- Jon W Rueckemann
- Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Marielena Sosa
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA.
- Washington National Primate Research Center, Seattle, WA, USA.
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27
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Lepage KQ, Fleming CN, Witcher M, Vijayan S. Multitaper estimates of phase-amplitude coupling. J Neural Eng 2021; 18:10.1088/1741-2552/ac1deb. [PMID: 34399415 PMCID: PMC10511062 DOI: 10.1088/1741-2552/ac1deb] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/16/2021] [Indexed: 11/12/2022]
Abstract
Phase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing PAC with commonly used statistical measures have been well-documented. The limitations of these measures include: (1) response to non-oscillatory, high-frequency, broad-band activity, (2) response to high-frequency components of the low-frequency oscillation, (3) adhoc selection of analysis frequency-intervals, and (4) reliance upon data shuffling to assess statistical significance.Objective.To address issues (1)-(4) by introducing a nonparametric multitaper estimator of PAC.Approach.In this work, a multitaper PAC estimator is proposed that addresses these issues. Specifically, issue (1) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (2) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (3) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (4).Main results.Multitaper estimates of PAC are introduced. Their efficacy is demonstrated in simulation and on human intracranial recordings obtained from epileptic patients.Significance.This work facilitates a more informative statistical assessment of PAC, a phenomena exhibited by many neural systems, and provides a basis upon which further nonparametric multitaper-related methods can be developed.
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Affiliation(s)
- Kyle Q Lepage
- School of Neuroscience, Virginia Tech, Blacksburg, VA, United States of America
| | - Cavan N Fleming
- School of Neuroscience, Virginia Tech, Blacksburg, VA, United States of America
| | - Mark Witcher
- School of Medicine, Virginia Tech, Blacksburg, VA, United States of America
| | - Sujith Vijayan
- School of Neuroscience, Virginia Tech, Blacksburg, VA, United States of America
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28
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Sutton NM, Ascoli GA. Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories. COGN SYST RES 2021; 70:80-92. [PMID: 34504394 DOI: 10.1016/j.cogsys.2021.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.
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Affiliation(s)
- Nate M Sutton
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
| | - Giorgio A Ascoli
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA).,Interdepartmental Neuroscience Program, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
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29
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Prince LY, Tran MM, Grey D, Saad L, Chasiotis H, Kwag J, Kohl MM, Richards BA. Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information. Commun Biol 2021; 4:935. [PMID: 34354206 PMCID: PMC8342442 DOI: 10.1038/s42003-021-02437-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 07/09/2021] [Indexed: 11/30/2022] Open
Abstract
Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes. In order to address whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, Prince et al. used optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. They demonstrated that FS and RS interneurons had differential sensitivity to changes in synchrony and rate, which advances our understanding of neural processing in the neocortex.
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Affiliation(s)
- Luke Y Prince
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada.,School of Computer Science, McGill University, Montreal, QC, Canada.,Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Matthew M Tran
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Dorian Grey
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Lydia Saad
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Helen Chasiotis
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Jeehyun Kwag
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Michael M Kohl
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Blake A Richards
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada. .,School of Computer Science, McGill University, Montreal, QC, Canada. .,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada. .,Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
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30
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Low IIC, Williams AH, Campbell MG, Linderman SW, Giocomo LM. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 2021; 109:2967-2980.e11. [PMID: 34363753 DOI: 10.1016/j.neuron.2021.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/26/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022]
Abstract
Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations.
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Affiliation(s)
- Isabel I C Low
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Alex H Williams
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Malcolm G Campbell
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Scott W Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
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31
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Peng Y, Barreda Tomas FJ, Pfeiffer P, Drangmeister M, Schreiber S, Vida I, Geiger JRP. Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning. SCIENCE ADVANCES 2021; 7:7/25/eabg4693. [PMID: 34134979 PMCID: PMC8208710 DOI: 10.1126/sciadv.abg4693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/04/2021] [Indexed: 05/04/2023]
Abstract
In cortical microcircuits, it is generally assumed that fast-spiking parvalbumin interneurons mediate dense and nonselective inhibition. Some reports indicate sparse and structured inhibitory connectivity, but the computational relevance and the underlying spatial organization remain unresolved. In the rat superficial presubiculum, we find that inhibition by fast-spiking interneurons is organized in the form of a dominant super-reciprocal microcircuit motif where multiple pyramidal cells recurrently inhibit each other via a single interneuron. Multineuron recordings and subsequent 3D reconstructions and analysis further show that this nonrandom connectivity arises from an asymmetric, polarized morphology of fast-spiking interneuron axons, which individually cover different directions in the same volume. Network simulations assuming topographically organized input demonstrate that such polarized inhibition can improve head direction tuning of pyramidal cells in comparison to a "blanket of inhibition." We propose that structured inhibition based on asymmetrical axons is an overarching spatial connectivity principle for tailored computation across brain regions.
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Affiliation(s)
- Yangfan Peng
- Institute for Neurophysiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Federico J Barreda Tomas
- Institute for Integrative Neuroanatomy, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - Paul Pfeiffer
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Moritz Drangmeister
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Susanne Schreiber
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Imre Vida
- Institute for Integrative Neuroanatomy, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany.
| | - Jörg R P Geiger
- Institute for Neurophysiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany.
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32
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Lepperød ME, Christensen AC, Lensjø KK, Buccino AP, Yu J, Fyhn M, Hafting T. Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells. SCIENCE ADVANCES 2021; 7:7/19/eabd5684. [PMID: 33952512 DOI: 10.1126/sciadv.abd5684] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
Grid cells in the medial entorhinal cortex (MEC) exhibit remarkable spatial activity patterns with spikes coordinated by theta oscillations driven by the medial septal area (MSA). Spikes from grid cells progress relative to the theta phase in a phenomenon called phase precession, which is suggested as essential to create the spatial periodicity of grid cells. Here, we show that optogenetic activation of parvalbumin-positive (PV+) cells in the MSA enabled selective pacing of local field potential (LFP) oscillations in MEC. During optogenetic stimulation, the grid cells were locked to the imposed pacing frequency but kept their spatial patterns. Phase precession was abolished, and speed information was no longer reflected in the LFP oscillations but was still carried by rate coding of individual MEC neurons. Together, these results support that theta oscillations are not critical to the spatial pattern of grid cells and do not carry a crucial velocity signal.
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Affiliation(s)
- Mikkel Elle Lepperød
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Ane Charlotte Christensen
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Kristian Kinden Lensjø
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Alessio Paolo Buccino
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Jai Yu
- Department of Psychology, Institute for Mind and Biology, Grossman Institute for Neuroscience, and Quantitative Biology and Human Behavior, University of Chicago, 5848 S. University Avenue, Chicago, IL 60637, USA
| | - Marianne Fyhn
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Torkel Hafting
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
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Ablation of p75 NTR signaling strengthens gamma-theta rhythm interaction and counteracts Aβ-induced degradation of neuronal dynamics in mouse hippocampus in vitro. Transl Psychiatry 2021; 11:212. [PMID: 33837176 PMCID: PMC8035168 DOI: 10.1038/s41398-021-01332-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/09/2021] [Accepted: 03/26/2021] [Indexed: 11/09/2022] Open
Abstract
Gamma and theta brain rhythms play important roles in cognition and their interaction can affect gamma oscillation features. Hippocampal theta oscillations depend on cholinergic and GABAergic input from the medial septum-diagonal band of Broca. These projecting neurons undergo degeneration during aging and maintain high levels of neurotrophin receptor p75 (p75NTR). p75NTR mediates both apoptosis and survival and its expression is increased in Alzheimer's disease (AD) patients. Here, we investigate the importance of p75NTR for the cholinergic input to the hippocampus. Performing extracellular recordings in brain slices from p75NTR knockout mice (p75-/-) in presence of the muscarinic agonist carbachol, we find that gamma oscillation power and rhythmicity are increased compared to wild-type (WT) mice. Furthermore, gamma activity is more phase-locked to the underlying theta rhythm, which renders a stronger coupling of both rhythms. On the cellular level, we find that fast-spiking interneurons (FSNs) fire more synchronized to a preferred gamma phase in p75-/- mice. The excitatory input onto FSN is more rhythmic displaying a higher similarity with the concomitant gamma rhythm. Notably, the ablation of p75NTR counteracts the Aβ-induced degradation of gamma oscillations and its nesting within the underlying theta rhythm. Our results show that the lack of p75NTR signaling could promote stronger cholinergic modulation of the hippocampal gamma rhythm, suggesting an involvement of p75NTR in the downregulation of cognition-relevant hippocampal network dynamics in pathologies. Moreover, functional data provided here suggest p75NTR as a suitable target in the search for efficacious treatments to counteract the loss of cognitive function observed in amyloid-driven pathologies such as AD.
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Krishna A, Mittal D, Virupaksha SG, Nair AR, Narayanan R, Thakur CS. Biomimetic FPGA-based spatial navigation model with grid cells and place cells. Neural Netw 2021; 139:45-63. [PMID: 33677378 DOI: 10.1016/j.neunet.2021.01.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 01/15/2021] [Accepted: 01/25/2021] [Indexed: 12/22/2022]
Abstract
The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons responding to distinct features including location, speed, borders and head direction; an ensuing convergence finally enables navigation and path integration. Here, we report the algorithmic and hardware implementation of biomimetic neural structures encompassing a feed-forward trimodular, multi-layer architecture representing grid-cell, place-cell and decoding modules for navigation. The grid-cell module comprised of neurons that fired in a grid-like pattern, and was built of distinct layers that constituted the dorsoventral span of the medial entorhinal cortex. Each layer was built as an independent continuous attractor network with distinct grid-field spatial scales. The place-cell module comprised of neurons that fired at one or few spatial locations, organized into different clusters based on convergent modular inputs from different grid-cell layers, replicating the gradient in place-field size along the hippocampal dorso-ventral axis. The decoding module, a two-layer neural network that constitutes the convergence of the divergent representations in preceding modules, received inputs from the place-cell module and provided specific coordinates of the navigating object. After vital design optimizations involving all modules, we implemented the tri-modular structure on Zynq Ultrascale+ field-programmable gate array silicon chip, and demonstrated its capacity in precisely estimating the navigational trajectory with minimal overall resource consumption involving a mere 2.92% Look Up Table utilization. Our implementation of a biomimetic, digital spatial navigation system is stable, reliable, reconfigurable, real-time with execution time of about 32 s for 100k input samples (in contrast to 40 minutes on Intel Core i7-7700 CPU with 8 cores clocking at 3.60 GHz) and thus can be deployed for autonomous-robotic navigation without requiring additional sensors.
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Affiliation(s)
- Adithya Krishna
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Divyansh Mittal
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Siri Garudanagiri Virupaksha
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Abhishek Ramdas Nair
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Chetan Singh Thakur
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
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Parvalbumin Interneurons Are Differentially Connected to Principal Cells in Inhibitory Feedback Microcircuits along the Dorsoventral Axis of the Medial Entorhinal Cortex. eNeuro 2021; 8:ENEURO.0354-20.2020. [PMID: 33531369 PMCID: PMC8114875 DOI: 10.1523/eneuro.0354-20.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/01/2020] [Accepted: 12/07/2020] [Indexed: 12/13/2022] Open
Abstract
The medial entorhinal cortex (mEC) shows a high degree of spatial tuning, predominantly grid cell activity, which is reliant on robust, dynamic inhibition provided by local interneurons (INs). In fact, feedback inhibitory microcircuits involving fast-spiking parvalbumin (PV) basket cells (BCs) are believed to contribute dominantly to the emergence of grid cell firing in principal cells (PrCs). However, the strength of PV BC-mediated inhibition onto PrCs is not uniform in this region, but high in the dorsal and weak in the ventral mEC. This is in good correlation with divergent grid field sizes, but the underlying morphologic and physiological mechanisms remain unknown. In this study, we examined PV BCs in layer (L)2/3 of the mEC characterizing their intrinsic physiology, morphology and synaptic connectivity in the juvenile rat. We show that while intrinsic physiology and morphology are broadly similar over the dorsoventral axis, PV BCs form more connections onto local PrCs in the dorsal mEC, independent of target cell type. In turn, the major PrC subtypes, pyramidal cell (PC) and stellate cell (SC), form connections onto PV BCs with lower, but equal probability. These data thus identify inhibitory connectivity as source of the gradient of inhibition, plausibly explaining divergent grid field formation along this dorsoventral axis of the mEC.
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Christensen AC, Lensjø KK, Lepperød ME, Dragly SA, Sutterud H, Blackstad JS, Fyhn M, Hafting T. Perineuronal nets stabilize the grid cell network. Nat Commun 2021; 12:253. [PMID: 33431847 PMCID: PMC7801665 DOI: 10.1038/s41467-020-20241-w] [Citation(s) in RCA: 269] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 11/16/2020] [Indexed: 11/29/2022] Open
Abstract
Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.
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Affiliation(s)
- Ane Charlotte Christensen
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Kristian Kinden Lensjø
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Mikkel Elle Lepperød
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Svenn-Arne Dragly
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Halvard Sutterud
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Jan Sigurd Blackstad
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Marianne Fyhn
- Department of Biosciences, University of Oslo, Oslo, Norway
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
| | - Torkel Hafting
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
- Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.
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37
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Wang W. The broadband power shifts in entorhinal EEG are related to the firing of grid cells. Heliyon 2021; 7:e06087. [PMID: 33553754 PMCID: PMC7846926 DOI: 10.1016/j.heliyon.2021.e06087] [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: 09/02/2020] [Revised: 12/03/2020] [Accepted: 01/20/2021] [Indexed: 11/04/2022] Open
Abstract
The relationship between the firing of the grid cell and mesoscopic neural oscillations is one of the key issues to understand the neural mechanism of grid cells. Previous studies have focused more on the correspondence between neuronal firing and phases of oscillations, such as phase precession. There are also some conclusions about the relationship between the activity of grid cells and the intensity of neural oscillations, such as the disappearance of grid pattern caused by the blocking of theta rhythm, but the correlation between the firing rates of grid cells and the narrowband power of neural oscillations or the broadband LFP power is still scarce. Through analyzing the records of spike times of grid cells and local entorhinal EEG obtained by Hafting et al., in the spatial navigation experiment, we find that grid cells are, to a large proportion, a kind of broadband-shift neurons, and the positive correlation between grid cell activity and power of low theta and gamma bands was observed. These results have well verified, promoted, and connected many scattered research conclusions, such as the broadband shift phenomenon of hippocampal neurons, the influence of low theta activity on the firing pattern of grid cells, and the positive correlation between single-cell activity and gamma-band activity. This work is of great significance for the study of the neural mechanism of grid cells at the micro and mesoscopic levels, and may also inspire the use of indicators such as broadband power as markers for grid cell activity.
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Affiliation(s)
- Wenjing Wang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
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38
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Somatostatin expressing GABAergic interneurons in the medial entorhinal cortex preferentially inhibit layer III-V pyramidal cells. Commun Biol 2020; 3:754. [PMID: 33303963 PMCID: PMC7728756 DOI: 10.1038/s42003-020-01496-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 11/13/2020] [Indexed: 12/31/2022] Open
Abstract
GABA released from heterogeneous types of interneurons acts in a complex spatio-temporal manner on postsynaptic targets in the networks. In addition to GABA, a large fraction of GABAergic cells also express neuromodulator peptides. Somatostatin (SOM) containing interneurons, in particular, have been recognized as key players in several brain circuits, however, the action of SOM and its downstream network effects remain largely unknown. Here, we used optogenetics, electrophysiologic, anatomical and behavioral experiments to reveal that the dendrite-targeting, SOM+ GABAergic interneurons demonstrate a unique layer-specific action in the medial entorhinal cortex (MEC) both in terms of GABAergic and SOM-related properties. We show that GABAergic and somatostatinergic neurotransmission originating from SOM+ local interneurons preferentially inhibit layerIII-V pyramidal cells, known to be involved in memory formation. We propose that this dendritic GABA–SOM dual inhibitory network motif within the MEC serves to selectively modulate working-memory formation without affecting the retrieval of already learned spatial navigation tasks. Miklós Kecskés et al. show that somatostatin-expressing interneurons in the medial entorhinal cortex regulate deep-layer pyramidal neurons and impact short-term memory in mice.
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39
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D'Albis T, Kempter R. Recurrent amplification of grid-cell activity. Hippocampus 2020; 30:1268-1297. [PMID: 33022854 DOI: 10.1002/hipo.23254] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 06/18/2020] [Accepted: 07/25/2020] [Indexed: 11/07/2022]
Abstract
High-level cognitive abilities such as navigation and spatial memory are thought to rely on the activity of grid cells in the medial entorhinal cortex (MEC), which encode the animal's position in space with periodic triangular patterns. Yet the neural mechanisms that underlie grid-cell activity are still unknown. Recent in vitro and in vivo experiments indicate that grid cells are embedded in highly structured recurrent networks. But how could recurrent connectivity become structured during development? And what is the functional role of these connections? With mathematical modeling and simulations, we show that recurrent circuits in the MEC could emerge under the supervision of weakly grid-tuned feedforward inputs. We demonstrate that a learned excitatory connectivity could amplify grid patterns when the feedforward sensory inputs are available and sustain attractor states when the sensory cues are lost. Finally, we propose a Fourier-based measure to quantify the spatial periodicity of grid patterns: the grid-tuning index.
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Affiliation(s)
- Tiziano D'Albis
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,Einstein Center for Neurosciences, Berlin, Germany
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40
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Perez DM. α 1-Adrenergic Receptors in Neurotransmission, Synaptic Plasticity, and Cognition. Front Pharmacol 2020; 11:581098. [PMID: 33117176 PMCID: PMC7553051 DOI: 10.3389/fphar.2020.581098] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/11/2020] [Indexed: 12/14/2022] Open
Abstract
α1-adrenergic receptors are G-Protein Coupled Receptors that are involved in neurotransmission and regulate the sympathetic nervous system through binding and activating the neurotransmitter, norepinephrine, and the neurohormone, epinephrine. There are three α1-adrenergic receptor subtypes (α1A, α1B, α1D) that are known to play various roles in neurotransmission and cognition. They are related to two other adrenergic receptor families that also bind norepinephrine and epinephrine, the β- and α2-, each with three subtypes (β1, β2, β3, α2A, α2B, α2C). Previous studies assessing the roles of α1-adrenergic receptors in neurotransmission and cognition have been inconsistent. This was due to the use of poorly-selective ligands and many of these studies were published before the characterization of the cloned receptor subtypes and the subsequent development of animal models. With the availability of more-selective ligands and the development of animal models, a clearer picture of their role in cognition and neurotransmission can be assessed. In this review, we highlight the significant role that the α1-adrenergic receptor plays in regulating synaptic efficacy, both short and long-term synaptic plasticity, and its regulation of different types of memory. We will also present evidence that the α1-adrenergic receptors, and particularly the α1A-adrenergic receptor subtype, are a potentially good target to treat a wide variety of neurological conditions with diminished cognition.
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Affiliation(s)
- Dianne M Perez
- The Lerner Research Institute, The Cleveland Clinic Foundation, Cleveland, OH, United States
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41
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Gerlei K, Passlack J, Hawes I, Vandrey B, Stevens H, Papastathopoulos I, Nolan MF. Grid cells are modulated by local head direction. Nat Commun 2020; 11:4228. [PMID: 32839445 PMCID: PMC7445272 DOI: 10.1038/s41467-020-17500-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 07/02/2020] [Indexed: 01/11/2023] Open
Abstract
Grid and head direction codes represent cognitive spaces for navigation and memory. Pure grid cells generate grid codes that have been assumed to be independent of head direction, whereas conjunctive cells generate grid representations that are tuned to a single head direction. Here, we demonstrate that pure grid cells also encode head direction, but through distinct mechanisms. We show that individual firing fields of pure grid cells are tuned to multiple head directions, with the preferred sets of directions differing between fields. This local directional modulation is not predicted by previous continuous attractor or oscillatory interference models of grid firing but is accounted for by models in which pure grid cells integrate inputs from co-aligned conjunctive cells with firing rates that differ between their fields. We suggest that local directional signals from grid cells may contribute to downstream computations by decorrelating different points of view from the same location.
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Affiliation(s)
- Klara Gerlei
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Jessica Passlack
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ian Hawes
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Brianna Vandrey
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Holly Stevens
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ioannis Papastathopoulos
- School of Mathematics, Maxwell Institute and Centre for Statistics, University of Edinburgh, Edinburgh, EH9 3FD, UK
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, EH8 9XD, UK.
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42
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Katyare N, Sikdar SK. Theta resonance and synaptic modulation scale activity patterns in the medial entorhinal cortex stellate cells. Ann N Y Acad Sci 2020; 1478:92-112. [PMID: 32794193 DOI: 10.1111/nyas.14434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 05/31/2020] [Accepted: 06/19/2020] [Indexed: 11/28/2022]
Abstract
Stellate cells (SCs) of the medial entorhinal cortex (MEC) are rich in hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which are known to effectively shape their activity patterns. The explanatory mechanisms, however, have remained elusive. One important but previously unassessed possibility is that HCN channels control the gain of synaptic inputs to these cells. Here, we test this possibility in rat brain slices, while subjecting SCs to a stochastic synaptic bombardment using the dynamic clamp. We show that in the presence of synaptic noise, HCN channels mainly exert their influence by increasing the relative signal gain in the theta frequency through the theta modulation of stochastic synaptic inputs. This subthreshold synaptic modulation then translates into a spiking resonance, which steepens with excitation in the presence of HCN channels. We present here a systematic assessment of synaptic theta modulation and trace its implications to the suprathreshold control of firing rate motifs. Such analysis was yet lacking in the SC literature. Furthermore, we assess the impact of noise statistics on this gain modulation and indicate possible mechanisms for the emergence of membrane theta oscillations and synaptic ramps, as observed in vivo. We support the data with a computational model that further unveils a competing role of inhibition, suggesting important implications for MEC computations.
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Affiliation(s)
- Nupur Katyare
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Sujit Kumar Sikdar
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, Karnataka, India
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43
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Segneri M, Bi H, Olmi S, Torcini A. Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models. Front Comput Neurosci 2020; 14:47. [PMID: 32547379 PMCID: PMC7270590 DOI: 10.3389/fncom.2020.00047] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 04/30/2020] [Indexed: 11/21/2022] Open
Abstract
Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments in vitro it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling (CFC) among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: namely, the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms always display phase amplitude coupling. However, two different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behavior; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing intensity and which are clearly modulated by the theta phase. Furthermore, analogously to the experiments, the gamma power and the frequency of the gamma-power peak increase with the forcing amplitude. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in our model, only by incrementing, at the same time, also the stimulation power. An effect achieved by increasing the amplitude either of the noise or of the forcing term proportionally to the theta frequency. On the basis of our analysis both the PING and the ING mechanism give rise to theta-nested gamma oscillations with almost identical features.
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Affiliation(s)
- Marco Segneri
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France
| | - Hongjie Bi
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France.,Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, Valbonne, France.,CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
| | - Alessandro Torcini
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France.,CNR-Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
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44
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Ceni A, Olmi S, Torcini A, Angulo-Garcia D. Cross frequency coupling in next generation inhibitory neural mass models. CHAOS (WOODBURY, N.Y.) 2020; 30:053121. [PMID: 32491891 DOI: 10.1063/1.5125216] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 04/22/2020] [Indexed: 06/11/2023]
Abstract
Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study, we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling, and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master-slave configuration with different synaptic time scales can display various collective dynamical regimes: damped oscillations toward a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled, the two inhibitory populations can exhibit different types of θ-γ cross-frequency couplings (CFCs): phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in the presence of an external θ forcing, reminiscent of the type of modulation induced in hippocampal and cortex circuits via optogenetic drive.
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Affiliation(s)
- Andrea Ceni
- Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, 2004 Route des Lucioles, 06902 Valbonne, France
| | - Alessandro Torcini
- Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, 95302 Cergy-Pontoise cedex, France
| | - David Angulo-Garcia
- Grupo de Modelado Computacional-Dinámica y Complejidad de Sistemas, Instituto de Matemáticas Aplicadas, Universidad de Cartagena, Carrera 6 #36-100, 130001 Cartagena de Indias, Colombia
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45
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Spike Afterpotentials Shape the In Vivo Burst Activity of Principal Cells in Medial Entorhinal Cortex. J Neurosci 2020; 40:4512-4524. [PMID: 32332120 DOI: 10.1523/jneurosci.2569-19.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 04/03/2020] [Accepted: 04/11/2020] [Indexed: 11/21/2022] Open
Abstract
Principal neurons in rodent medial entorhinal cortex (MEC) generate high-frequency bursts during natural behavior. While in vitro studies point to potential mechanisms that could support such burst sequences, it remains unclear whether these mechanisms are effective under in vivo conditions. In this study, we focused on the membrane-potential dynamics immediately following action potentials (APs), as measured in whole-cell recordings from male mice running in virtual corridors (Domnisoru et al., 2013). These afterpotentials consisted either of a hyperpolarization, an extended ramp-like shoulder, or a depolarization reminiscent of depolarizing afterpotentials (DAPs) recorded in vitro in MEC principal neurons. Next, we correlated the afterpotentials with the cells' propensity to fire bursts. All DAP cells with known location resided in Layer II, generated bursts, and their interspike intervals (ISIs) were typically between 5 and 15 ms. The ISI distributions of Layer-II cells without DAPs peaked sharply at around 4 ms and varied only minimally across that group. This dichotomy in burst behavior is explained by cell-group-specific DAP dynamics. The same two groups of bursting neurons also emerged when we clustered extracellular spike-train autocorrelations measured in real 2D arenas (Latuske et al., 2015). Apart from slight variations in grid spacing, no difference in the spatial coding properties of the grid cells across all three groups was discernible. Layer III neurons were only sparsely bursting (SB) and had no DAPs. As various mechanisms for modulating ion-channels underlying DAPs exist, our results suggest that temporal features of MEC activity can be altered while maintaining the cells' overall spatial tuning characteristics.SIGNIFICANCE STATEMENT Depolarizing afterpotentials (DAPs) are frequently observed in principal neurons from slice preparations of rodent medial entorhinal cortex (MEC), but their functional role in vivo is unknown. Analyzing whole-cell data from mice running on virtual tracks, we show that DAPs do occur during behavior. Cells with prominent DAPs are found in Layer II; their interspike intervals (ISIs) reflect DAP time-scales. In contrast, neither the rarely bursting cells in Layer III, nor the high-frequency bursters in Layer II, have a DAP. Extracellular recordings from mice exploring real 2D arenas demonstrate that grid cells within these three groups have similar spatial coding properties. We conclude that DAPs shape the temporal response characteristics of principal neurons in MEC with little effect on spatial properties.
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46
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Dawitz J, Kroon T, Hjorth JJJ, Mansvelder HD, Meredith RM. Distinct Synchronous Network Activity During the Second Postnatal Week of Medial Entorhinal Cortex Development. Front Cell Neurosci 2020; 14:91. [PMID: 32372917 PMCID: PMC7186407 DOI: 10.3389/fncel.2020.00091] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/26/2020] [Indexed: 01/01/2023] Open
Abstract
The medial entorhinal cortex (MEC) contains specialized cell types whose firing is tuned to aspects of an animal’s position and orientation in the environment, reflecting a neuronal representation of space. The spatially tuned firing properties of these cells quickly emerge during the third postnatal week of development in rodents. Spontaneous synchronized network activity (SSNA) has been shown to play a crucial role in the development of neuronal circuits prior to week 3. SSNA in MEC is well described in rodents during the first postnatal week, but there are little data about its development immediately prior to eye opening and spatial exploration. Furthermore, existing data lack single-cell resolution and are not integrated across layers. In this study, we addressed the question of whether the characteristics and underlying mechanisms of SSNA during the second postnatal week resemble that of the first week or whether distinct features emerge during this period. Using a combined calcium imaging and electrophysiology approach in vitro, we confirm that in mouse MEC during the second postnatal week, SSNA persists and in fact peaks, and is dependent on ionotropic glutamatergic signaling. However, SSNA differs from that observed during the first postnatal week in two ways: First, EC does not drive network activity in the hippocampus but only in neighboring neocortex (NeoC). Second, GABA does not drive network activity but influences it in a manner that is dependent both on age and receptor type. Therefore, we conclude that while there is a partial mechanistic overlap in SSNA between the first and second postnatal weeks, unique mechanistic features do emerge during the second week, suggestive of different or additional functions of MEC within the hippocampal-entorhinal circuitry with increasing maturation.
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Affiliation(s)
- Julia Dawitz
- Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Tim Kroon
- Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - J J Johannes Hjorth
- Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Huib D Mansvelder
- Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rhiannon M Meredith
- Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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47
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Zeng T, Tang F, Ji D, Si B. NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation. Neural Netw 2020; 126:21-35. [PMID: 32179391 DOI: 10.1016/j.neunet.2020.02.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 02/10/2020] [Accepted: 02/28/2020] [Indexed: 01/09/2023]
Abstract
Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains. In the model, the pose of the robot is encoded separately by two sub-networks, namely head direction network for angle representation and grid cell network for position representation, using similar neural codes of head direction cells and grid cells observed in mammalian brains. The neural codes in each of the sub-networks are updated in a Bayesian manner by a population of integrator cells for vestibular cue integration, as well as a population of calibration cells for visual cue calibration. The conflict between vestibular cue and visual cue is resolved by the competitive dynamics between the two populations. The model, implemented on a monocular visual simultaneous localization and mapping (SLAM) system, termed NeuroBayesSLAM, successfully builds semi-metric topological maps and self-localizes in outdoor and indoor environments of difference characteristics, achieving comparable performance as previous neurobiologically inspired navigation systems but with much less computation complexity. The proposed multisensory integration method constitutes a concise yet robust and biologically plausible method for robot navigation in large environments. The model provides a viable Bayesian mechanism for multisensory integration that may pertain to other neural subsystems beyond spatial cognition.
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Affiliation(s)
- Taiping Zeng
- 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; State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
| | - Fengzhen Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
| | - Daxiong Ji
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China.
| | - Bailu Si
- School of Systems Science, Beijing Normal University, 100875, China.
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48
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Hernández-Pérez JJ, Cooper KW, Newman EL. Medial entorhinal cortex activates in a traveling wave in the rat. eLife 2020; 9:52289. [PMID: 32057292 PMCID: PMC7046467 DOI: 10.7554/elife.52289] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 02/04/2020] [Indexed: 12/12/2022] Open
Abstract
Traveling waves are hypothesized to support the long-range coordination of anatomically distributed circuits. Whether separate strongly interacting circuits exhibit traveling waves remains unknown. The hippocampus exhibits traveling ‘theta’ waves and interacts strongly with the medial entorhinal cortex (MEC). To determine whether the MEC also activates in a traveling wave, we performed extracellular recordings of local field potentials (LFP) and multi-unit activity along the MEC. These recordings revealed progressive phase shifts in activity, indicating that the MEC also activates in a traveling wave. Variation in theta waveform along the region, generated by gradients in local physiology, contributed to the observed phase shifts. Removing waveform-related phase shifts left significant residual phase shifts. The residual phase shifts covaried with theta frequency in a manner consistent with those generated by weakly coupled oscillators. These results show that the coordination of anatomically distributed circuits could be enabled by traveling waves but reveal heterogeneity in the mechanisms generating those waves.
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Affiliation(s)
- J Jesús Hernández-Pérez
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, United States
| | - Keiland W Cooper
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, United States
| | - Ehren L Newman
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, United States
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49
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Pastoll H, Garden DL, Papastathopoulos I, Sürmeli G, Nolan MF. Inter- and intra-animal variation in the integrative properties of stellate cells in the medial entorhinal cortex. eLife 2020; 9:52258. [PMID: 32039761 PMCID: PMC7067584 DOI: 10.7554/elife.52258] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 02/04/2020] [Indexed: 01/28/2023] Open
Abstract
Distinctions between cell types underpin organizational principles for nervous system function. Functional variation also exists between neurons of the same type. This is exemplified by correspondence between grid cell spatial scales and the synaptic integrative properties of stellate cells (SCs) in the medial entorhinal cortex. However, we know little about how functional variability is structured either within or between individuals. Using ex-vivo patch-clamp recordings from up to 55 SCs per mouse, we found that integrative properties vary between mice and, in contrast to the modularity of grid cell spatial scales, have a continuous dorsoventral organization. Our results constrain mechanisms for modular grid firing and provide evidence for inter-animal phenotypic variability among neurons of the same type. We suggest that neuron type properties are tuned to circuit-level set points that vary within and between animals.
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Affiliation(s)
- Hugh Pastoll
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Derek L Garden
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Ioannis Papastathopoulos
- The Alan Turing Institute, London, United States.,School of Mathematics, Maxwell Institute and Centre for Statistics, University of Edinburgh, Edinburgh, United Kingdom
| | - Gülşen Sürmeli
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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50
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Munn RGK, Mallory CS, Hardcastle K, Chetkovich DM, Giocomo LM. Entorhinal velocity signals reflect environmental geometry. Nat Neurosci 2020; 23:239-251. [PMID: 31932764 PMCID: PMC7007349 DOI: 10.1038/s41593-019-0562-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 11/21/2019] [Indexed: 01/05/2023]
Abstract
The entorhinal cortex contains neurons that represent self-location, including grid cells that fire in periodic locations and velocity signals that encode running speed and head direction. Although the size and shape of the environment influence grid patterns, whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here we report that speed cells rescale after changes to the size and shape of the environment. Moreover, head direction cells reorganize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows dissociation of the coordination between cell types, with grid and speed cells, but not head direction cells, responding in concert to environmental change. These results point to malleability in the coding features of multiple entorhinal cell types and have implications for which cell types contribute to the velocity signal used by computational models of grid cells.
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Affiliation(s)
- Robert G K Munn
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Caitlin S Mallory
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kiah Hardcastle
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Dane M Chetkovich
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.
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