Synaptic Self-Organization of Spatio-Temporal Pattern Selectivity

Mapping Intimacies ◽

10.1101/2021.11.12.468380 ◽

2021 ◽

Author(s):

Mohammad Dehghani Habibabadi ◽

Klaus Richard Pawelzik

Keyword(s):

Synaptic Plasticity ◽

Specific Pattern ◽

Temporal Coding ◽

Neuronal Coding ◽

Temporal Mode ◽

Spatio Temporal ◽

Set Up ◽

Spike Patterns ◽

The Brain ◽

Input Spike

Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic efficacies which tend to balance excitatory and inhibitory synaptic inputs can make neurons sensitive to particular input spike patterns. Simulations demonstrate that a combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is sufficient for self-organizing sensitivity for spatio-temporal spike patterns that repeat in the input. In networks inclusion of hetero-synaptic plasticity leads to specialization and faithful representation of pattern sequences by a group of target neurons. Pattern detection is found to be robust against a range of distortions and noise. Furthermore, the resulting balance of excitatory and inhibitory inputs protects the memory for a specific pattern from being overwritten during ongoing learning when the pattern is not present. These results not only provide an explanation for experimental observations of balanced excitation and inhibition in cortex but also promote the plausibility of precise temporal coding in the brain.

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μSPIM: A Software Platform for Selective Plane Illumination Microscopy

10.1101/2020.06.15.151993 ◽

2020 ◽

Author(s):

Daniel Saska ◽

Paul Pichler ◽

Chen Qian ◽

Chrysia Pegasiou ◽

Christopher L. Buckley ◽

...

Keyword(s):

Optical Design ◽

Light Sheet ◽

Volumetric Imaging ◽

Larval Zebrafish ◽

Selective Plane Illumination Microscopy ◽

Spatio Temporal ◽

Set Up ◽

Control And Synchronization ◽

The Brain ◽

Camera Shutter

AbstractSelective Plane Illumination Microscopy (SPIM) is a fluorescence imaging technique that allows volumetric imaging at high spatio-temporal resolution to monitor neural activity in live organisms such as larval zebrafish. A major challenge in the construction of a custom SPIM microscope is the control and synchronization of the various hardware components. Here we present a control toolset, μSPIM, built around the open-source MicroManager platform that has already been widely adopted for the control of microscopy hardware. Installation of μSPIM is relatively straightforward, involving a single C++ executable and a Java-based extension to Micro-Manager. Imaging protocols are defined through the μSPIM extension to Micro-Manager. The extension then synchronizes the camera shutter with the galvanometer mirrors to create a light-sheet that is scanned in the z-dimension, in synchrony with the imaging objective, to produce volumetric recordings. A key advantage of μSPIM is that a series of calibration procedures optimizes acquisition for a given set-up making it relatively independent of the optical design of the microscope, or the hardware used to build it. Two laser illumination arms can be used while also allowing for the introduction of illumination masks. μSPIM allows imaging of calcium activity throughout the brain of larval zebrafish at rates of 100 planes per second with single cell resolution as well as slower imaging to reconstruct cell populations, for example, in the cleared brains of mice.

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Numerical cognition based on precise counting with a single spiking neuron

10.1101/662932 ◽

2019 ◽

Author(s):

Hannes Rapp ◽

Martin Paul Nawrot ◽

Merav Stern

Keyword(s):

Neural Network ◽

Synaptic Plasticity ◽

Action Potentials ◽

Numerical Cognition ◽

Spiking Neuron ◽

Behavioral Task ◽

Dynamic Background ◽

Visual Concepts ◽

Spatio Temporal ◽

Spike Patterns

AbstractInsects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatio-temporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free flying bees that requires numerical cognition the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.

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Endocannabinoid Signaling and Synaptic Plasticity in the Brain

Critical Reviews in Neurobiology ◽

10.1615/critrevneurobiol.v18.i1-2.120 ◽

2006 ◽

Vol 18 (1-2) ◽

pp. 113-124 ◽

Cited By ~ 17

Author(s):

Ping Jun Zhu

Keyword(s):

Synaptic Plasticity ◽

The Brain

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Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

Journal of Marine Science and Engineering ◽

10.3390/jmse9050467 ◽

2021 ◽

Vol 9 (5) ◽

pp. 467

Author(s):

Mostafa Farrag ◽

Gerald Corzo Perez ◽

Dimitri Solomatine

Keyword(s):

Hydrological Modeling ◽

Model Building ◽

Model Performance ◽

Model Description ◽

Generation Model ◽

Model Structure ◽

Runoff Generation ◽

Spatial Sensitivity ◽

Spatio Temporal ◽

Set Up

Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source.

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Graphene-Based Artificial Synapses with Tunable Plasticity

ACM Journal on Emerging Technologies in Computing Systems ◽

10.1145/3447778 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-21

Author(s):

He Wang ◽

Nicoleta Cucu Laurenciu ◽

Yande Jiang ◽

Sorin Cotofana

Keyword(s):

Synaptic Plasticity ◽

Weight Change ◽

Time Scale ◽

Large Scale ◽

Synaptic Weight ◽

Low Voltage ◽

Logic Gates ◽

Full Potential ◽

Back Gate ◽

Input Spike

Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired computation’s full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity . Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm 2 ), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.

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Spatio-temporal expectile regression models

Statistical Modelling ◽

10.1177/1471082x19829945 ◽

2019 ◽

Vol 20 (4) ◽

pp. 386-409

Author(s):

Elmar Spiegel ◽

Thomas Kneib ◽

Fabian Otto-Sobotka

Keyword(s):

Tensor Product ◽

Least Squares Regression ◽

Expectile Regression ◽

Specific Distribution ◽

Weighted Means ◽

Temporal Models ◽

Error Terms ◽

Spatio Temporal ◽

Set Up ◽

Main Effects

Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

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Synaptic plasticity-dependent competition rule influences memory formation

Nature Communications ◽

10.1038/s41467-021-24269-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yire Jeong ◽

Hye-Yeon Cho ◽

Mujun Kim ◽

Jung-Pyo Oh ◽

Min Soo Kang ◽

...

Keyword(s):

Synaptic Plasticity ◽

Fear Conditioning ◽

Long Term Potentiation ◽

Memory Formation ◽

Specific Pattern ◽

Memory Encoding ◽

Competition Rule ◽

Term Potentiation ◽

Input Activity

AbstractMemory is supported by a specific collection of neurons distributed in broad brain areas, an engram. Despite recent advances in identifying an engram, how the engram is created during memory formation remains elusive. To explore the relation between a specific pattern of input activity and memory allocation, here we target a sparse subset of neurons in the auditory cortex and thalamus. The synaptic inputs from these neurons to the lateral amygdala (LA) are not potentiated by fear conditioning. Using an optogenetic priming stimulus, we manipulate these synapses to be potentiated by the learning. In this condition, fear memory is preferentially encoded in the manipulated cell ensembles. This change, however, is abolished with optical long-term depression (LTD) delivered shortly after training. Conversely, delivering optical long-term potentiation (LTP) alone shortly after fear conditioning is sufficient to induce the preferential memory encoding. These results suggest a synaptic plasticity-dependent competition rule underlying memory formation.

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There are no A1 and A2 astrocytes in brain

10.21203/rs.3.rs-737027/v1 ◽

2021 ◽

Author(s):

Shuang-qi Gao

Keyword(s):

Endothelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Cell Sorting ◽

Reactive Astrocytes ◽

Marker Genes ◽

Set Up ◽

The Brain

Abstract Objectives The subsets of astrocytes in the brain have not been fully elucidated. Using bulk RNA sequencing, reactive astrocytes were divided into A1 versus A2. However, using single-cell RNAseq (ScRNAseq), astrocytes were divided into over two subsets. Our aim was to set up the correspondence between the fluorescent-activated cell sorting (FACS)-bulk RNAseq and ScRNAseq data. Results We found that most of reactive astrocytes (RAs) marker genes were expressed in endothelial cells but not in astrocytes, suggesting those marker genes are not suitable for astrocytic activation. The absence of A1 and A2 astrocytes in the brain.

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Regeneration, Plasticity, and Induced Molecular Programs in Adult Zebrafish Brain

BioMed Research International ◽

10.1155/2015/769763 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 17

Author(s):

Mehmet Ilyas Cosacak ◽

Christos Papadimitriou ◽

Caghan Kizil

Keyword(s):

Neural Plasticity ◽

Molecular Mechanisms ◽

Clinical Settings ◽

Regenerative Capacity ◽

Zebrafish Brain ◽

Aquatic Vertebrates ◽

Neural Stem Progenitor Cells ◽

Set Up ◽

Regenerative Therapies ◽

The Brain

Regenerative capacity of the brain is a variable trait within animals. Aquatic vertebrates such as zebrafish have widespread ability to renew their brains upon damage, while mammals have—if not none—very limited overall regenerative competence. Underlying cause of such a disparity is not fully evident; however, one of the reasons could be activation of peculiar molecular programs, which might have specific roles after injury or damage, by the organisms that regenerate. If this hypothesis is correct, then there must be genes and pathways that (a) are expressed only after injury or damage in tissues, (b) are biologically and functionally relevant to restoration of neural tissue, and (c) are not detected in regenerating organisms. Presence of such programs might circumvent the initial detrimental effects of the damage and subsequently set up the stage for tissue redevelopment to take place by modulating the plasticity of the neural stem/progenitor cells. Additionally, if transferable, those “molecular mechanisms of regeneration” could open up new avenues for regenerative therapies of humans in clinical settings. This review focuses on the recent studies addressing injury/damage-induced molecular programs in zebrafish brain, underscoring the possibility of the presence of genes that could be used as biomarkers of neural plasticity and regeneration.

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