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Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues

NEURON 2022年第11期110卷 1843-+页

作者： Waaga, Torgeir Agmon, Haggai Normand, Valentin A. Nagelhus, Anne Gardner, Richard J. Moser, May-Britt Moser, Edvard, I Burak, Yoram Norwegian Univ Sci & Technol Kavli Inst Syst Neurosci Trondheim Norway Norwegian Univ Sci & Technol Ctr Neural Computat Trondheim Norway Hebrew Univ Jerusalem Edmond & Lily Safra Ctr Brain Sci Jerusalem Israel Hebrew Univ Jerusalem Racah Inst Phys Jerusalem Israel

The representation of an animal's position in the medial entorhinal cortex (MEC) is distributed across several modules of grid cells, each characterized by a distinct spatial scale. The population activity within each module is tightly coordinated and preserved across environments and behavioral states. Little is known, however, about the coordination of activity patterns across modules. We analyzed the joint activity patterns of hundreds of grid cells simultaneously recorded in animals that were foraging either in the light, when sensory cues could stabilize the representation, or in darkness, when such stabilization was disrupted. We found that the states of different modules are tightly coordinated, even in darkness, when the internal representation of position within the MEC deviates substantially from the true position of the animal. These findings suggest that internal brain mechanisms dynamically coordinate the representation of position in different modules, ensuring that they jointly encode a coherent and smooth trajectory.

关键词： neural coding neural decoding grid cells entorhinal cortex spatial memory spatial coding population coding neural circuits

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Hand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way

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CELL 2020年第2期181卷 396-+页

作者： Willett, Francis R. Deo, Darrel R. Avansino, Donald T. Rezaii, Paymon Hochberg, Leigh R. Henderson, Jaimie M. Shenoy, Krishna V. Stanford Univ Dept Neurosurg Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA Providence VA Med Ctr Rehabil R&D Serv VA RR&D Ctr Neurorestorat & Neurotechnol Providence RI USA Brown Univ Sch Engn Providence RI 02912 USA Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Harvard Med Sch Dept Neurol Boston MA 02115 USA Massachusetts Gen Hosp Dept Neurol Ctr Neurotechnol & Neurorecovery Boston MA 02114 USA Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Dept Neurobiol Stanford CA 94305 USA Stanford Univ Howard Hughes Med Inst Stanford CA 94305 USA Stanford Univ Wu Tsai Neurosci Inst BioX Program Stanford CA 94305 USA

Decades after the motor homunculus was first proposed, it is still unknown how different body parts are intermixed and interrelated in human motor cortical areas at single-neuron resolution. Using multi-unit recordings, we studied how face, head, arm, and leg movements are represented in the hand knob area of premotor cortex (precentral gyrus) in people with tetraplegia. Contrary to traditional expectations, we found strong representation of all movements and a partially "compositional'' neural code that linked together all four limbs. The code consisted of (1) a limb-coding component representing the limb to be moved and (2) a movement-coding component where analogous movements from each limb (e.g., hand grasp and toe curl) were represented similarly. Compositional coding might facilitate skill transfer across limbs, and it provides a useful framework for thinking about how the motor system constructs movement. Finally, we leveraged these results to create a whole-body intracortical brain-computer interface that spreads targets across all limbs.

关键词： motor cortex hand knob neural coding skill transfer microelectrode array brain-computer interface

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The neural circuitry supporting successful spatial navigation despite variable movement speeds

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NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS 2020年 108卷 821-833页

作者： Sheeran, William M. Ahmed, Omar J. Univ Michigan Dept Psychol 580 Union Dr Ann Arbor MI 48109 USA Univ Michigan Dept Biomed Engn Ann Arbor MI 48109 USA Univ Michigan Neurosci Grad Program Ann Arbor MI 48109 USA Univ Michigan Kresge Hearing Res Inst 1301 E Ann St Ann Arbor MI 48109 USA Univ Michigan Michigan Ctr Integrat Res Crit Care Ann Arbor MI 48109 USA Univ Michigan Dept Mol Cellular & Dev Biol Ann Arbor MI 48109 USA Univ Colorado Sch Med Med Scientist Training Program Aurora CO 80045 USA

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

关键词： Running speed Spatial navigation Learning & memory Brain rhythms neural coding Temporal code Rate code Hippocampus Entorhinal cortex Secondary motor cortex Medial septum Mesencephalic locomotor region

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Noise-Robust Deep Spiking neural Networks with Temporal Information 21

Noise-Robust Deep Spiking Neural Networks with Temporal Info...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Seongsik Park Dongjin Lee Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Seoul South Korea and Institute of New Media and Communications Seoul National University Seoul South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea and Institute of New Media and Communications Seoul National University Seoul South Korea and ASRI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul South Korea

ISBN: (纸本)9781665432740

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter.

关键词： neural coding

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STDP Forms Associations between Memory Traces in Networks of Spiking Neurons

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CEREBRAL CORTEX 2020年第3期30卷 952-968页

作者： Pokorny, Christoph Ison, Matias J. Rao, Arjun Legenstein, Robert Papadimitriou, Christos Maass, Wolfgang Graz Univ Technol Inst Theoret Comp Sci A-8010 Graz Austria Univ Nottingham Sch Psychol Nottingham NG7 2RD England Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

关键词： associations memory traces neural coding spiking neurons STDP

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Patch-Clamp Electrophysiological Analysis of Murine Melanopsin Neurons

Patch-Clamp Electrophysiological Analysis of Murine Melanops...

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作者： Michael Tri Do

Intrinsically photosensitive retinal ganglion cells (ipRGCs) regulate diverse aspects of mammalian physiology, including the synchronization of circadian rhythms with local time. These neurons sense light with a receptor called melanopsin and provide practically all retinal innervation of the suprachiasmatic nucleus, the master clock. Their activity is especially important over long timescales. How do ipRGCs respond to light, what signals do they send downstream, and to what extent are these signals tailored to circadian photoregulation? This chapter provides practical guidance for studying ipRGC signals in the ex vivo mouse retina using patch-clamp electrophysiology and optical stimulation. Somatic and axonal recording are covered, as are methods that include loose, cell attached, whole cell, and perforated patch. Also discussed are how particular features of the ipRGC light response affect the design and interpretation of experiments. These approaches may be useful in the broader effort to understand how a neuron’s functional properties align with its role in the organism. less

关键词： Electrophysiology Dissection Transcription Profiling Electroporation Dialysis Image Processing Fluorescence Microscopy Mus musculus Homo sapiens Circadian Melanopsin neural coding Photoreceptors Phototransduction Intrinsically photosensitive retinal ganglion cell Phase shift Retinohypothalamic tract Persistent activity

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Design and Implementation of a Spiking neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition

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INTERNATIONAL JOURNAL OF neural SYSTEMS 2021年第3期31卷 2050073页

作者： Rashvand, Parvaneh Ahmadzadeh, Mohammad Reza Shayegh, Farzaneh Isfahan Univ Technol Dept Elect & Comp Engn Digital Signal Proc Res Lab Esfahan *** Iran

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.

关键词： Spiking neural networks training algorithm neural coding image recognition IF neuron model

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An Integrative Account of neural Network Interaction: Neuro-Messenger Theory

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World Journal of Neuroscience 2021年第2期11卷 124-136页

作者： Tien-Wen Lee The Neurocognitive Institute (NCI) Clinical Research Foundation New Jersey US Department of Psychiatry Dajia Lee’s General Hospital Lee’s Medical Corporation Taichung Chinese Taipei

neural interaction is realized by information exchange. It seemed that the information amount does not keep constant and may be reduced during the travel between neural nodes. In addition, recent research of neural coding has suggested that neural information could be represented by parsimonious spiking pattern, named sparse coding. Based on the above observation, neuro-messenger theory (NMT) is proposed to explicate the communicative process between the source and the target neural nodes. Neuro-messenger is a sparse code which does not have to carry every detail of the dynamics in source node. Other formats of neural coding (e.g., temporal and rate coding) could be the precursors of neuro-messengers, and the repeated spatiotemporal patterns buried in the ongoing brain activities may be the circulated neuro-messengers from diverse origins. Referred to chaos/complexity theory, information can be recovered at target node where neuro-messenger serves as a facilitator to locate the trajectory at proper attractor, and hence the associated psychological entity. In contrast to conventional concepts of encoding and decoding, the processes of encoding in source node, issuing neuro-messengers, and recovering information at target node are summarized as “three-facet coding scheme”. The design of neuro-messenger enables the brain to utilize energy in an efficient and economical way. NMT may have substantial implication in several major psychiatric disorders. Some psychiatric conditions could be mediated by abnormal neuro-messengers that coerce the regional neuro-dynamics to delve into maladaptive attractors and hence the characteristic symptoms.

关键词： Auditory Hallucination Delusion neural coding Neuro-Messenger Obsessive-Compulsive Disorder Sparse coding

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Measures of neural Similarity

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Computational Brain and Behavior 2020年第4期3卷 369-383页

作者： Bobadilla-Suarez, S. Ahlheim, C. Mehrotra, A. Panos, A. Love, B.C. Department of Experimental Psychology University College London 26 Bedford Way London WC1H 0AP United Kingdom Department of Geography University College London Gower Street London WC1E 6BT United Kingdom Department of Statistical Science University College London Gower Street London WC1E 6BT United Kingdom The Alan Turing Institute 96 Euston Road London NW1 2DB United Kingdom

One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex elicited from viewing a robin similar to a sparrow? One common assumption in fMRI analysis is that neural similarity is described by Pearson correlation. However, there are a host of other possibilities, including Minkowski and Mahalanobis measures, with each differing in its mathematical, theoretical, and neural computational assumptions. Moreover, the operable measures may vary across brain regions and tasks. Here, we evaluated which of several competing similarity measures best captured neural similarity. Our technique uses a decoding approach to assess the information present in a brain region, and the similarity measures that best correspond to the classifier’s confusion matrix are preferred. Across two published fMRI datasets, we found the preferred neural similarity measures were common across brain regions but differed across tasks. Moreover, Pearson correlation was consistently surpassed by alternatives. © 2019, The Author(s).

关键词： fMRI Machine learning neural coding neural similarity

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Variability and Randomness of the Instantaneous Firing Rate

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 620410页

作者： Tomar, Rimjhim Kostal, Lubomir Czech Acad Sci Inst Physiol Dept Computat Neurosci Prague Czech Republic Charles Univ Prague Med Fac 2 Prague Czech Republic

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

关键词： variability randomness firing rate entropy rate coding neural coding temporal coding instantaneous firing rate

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