scholarly journals Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses

2021 ◽  
Author(s):  
Dmitrii Zendrikov ◽  
Alexander Paraskevov
Keyword(s):  
Synaptic Plasticity ◽  
Large Scale ◽  
Respiratory Rhythm ◽  
Small World ◽  
Population Activity ◽  
Pacemaker Neurons ◽  
Population Spikes ◽  
Nucleation Sites ◽  
Connection Probability ◽  
Dynamic Synapses

We show that networks of excitatory neurons with stochastic spontaneous spiking activity and short-term synaptic plasticity can exhibit spontaneous repetitive synchronization in so-called population spikes. The major reason for this is that synaptic plasticity nonlinearly modulates the interaction between neurons. For large-scale two-dimensional networks, where the connection probability decreases exponentially with increasing distance between the neurons resulting in a small-world network connectome, a population spike occurs in the form of circular traveling waves diverging from seemingly non-stationary nucleation sites. The latter is in drastic contrast to the case of networks with a fixed fraction of steady pacemaker neurons, where the set of a few spontaneously formed nucleation sites is stationary. Despite the spatial non-stationarity of their nucleation, population spikes may occur surprisingly regularly. From a theoretical viewpoint, these findings show that the regime of nearly-periodic population spikes, which mimics respiratory rhythm, can occur strictly without stochastic resonance. In addition, the observed spatiotemporal effects serve as an example of transient chimera patterns.

Identification of Two Types of Inspiratory Pacemaker Neurons in the Isolated Respiratory Neural Network of Mice

2001 ◽  
Vol 86 (1) ◽  
pp. 104-112 ◽  
Author(s):  
Muriel Thoby-Brisson ◽  
Jan-Marino Ramirez
Keyword(s):  
Neural Network ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Pacemaker Activity ◽  
Patch Clamp Technique ◽  
Population Activity ◽  
Pacemaker Neurons ◽  
Whole Cell Patch Clamp ◽  
Respiratory Network ◽  
Bursting Activity

In the respiratory network of mice, we characterized with the whole cell patch-clamp technique pacemaker properties in neurons discharging in phase with inspiration. The respiratory network was isolated in a transverse brain stem slice containing the pre-Bötzinger complex (PBC), the presumed site for respiratory rhythm generation. After blockade of respiratory network activity with 6-cyano-7-nitroquinoxalene-2,3-dione (CNQX), 18 of 52 inspiratory neurons exhibited endogenous pacemaker activity, which was voltage dependent, could be reset by brief current injections and could be entrained by repetitive stimuli. In the pacemaker group ( n = 18), eight neurons generated brief bursts (0.43 ± 0.03 s) at a relatively high frequency (1.05 ± 0.12 Hz) in CNQX. These bursts resembled the bursts that these neurons generated in the intact network during the interval between two inspiratory bursts. Cadmium (200 μM) altered but did not eliminate this bursting activity, while 0.5 μM tetrodotoxin suppressed bursting activity. Another set of pacemaker neurons (10 of 18) generated in CNQX longer bursts (1.57 ± 0.07 s) at a lower frequency (0.35 ± 0.01 Hz). These bursts resembled the inspiratory bursts generated in the intact network in phase with the population activity. This bursting activity was blocked by 50–100 μM cadmium or 0.5 μM tetrodotoxin. We conclude that the respiratory neural network contains pacemaker neurons with two types of bursting properties. The two types of pacemaker activities might have different functions within the respiratory network.

scholarly journals Graphene-Based Artificial Synapses with Tunable Plasticity

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.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

scholarly journals DISC1 regulates N-Methyl-D-Aspartate receptor dynamics: Abnormalities induced by a Disc1 mutation modelling a translocation linked to major mental illness

2018 ◽  
Author(s):  
Elise L.V. Malavasi ◽  
Kyriakos D. Economides ◽  
Ellen Grünewald ◽  
Paraskevi Makedonopoulou ◽  
Philippe Gautier ◽  
...  
Keyword(s):  
Mental Illness ◽  
Synaptic Plasticity ◽  
Large Scale ◽  
Mutant Mouse ◽  
Psychiatric Patients ◽  
Cognitive Disorders ◽  
Copy Number Variant ◽  
Common Disease ◽  
Disease Mechanisms ◽  
Human Neurons

ABSTRACTThe neuromodulatory gene DISC1 is disrupted by a t(1;11) translocation that is highly penetrant for schizophrenia and affective disorders, but how this translocation affects DISC1 function is incompletely understood. N-Methyl-D-Aspartate receptors (NMDAR) play a central role in synaptic plasticity and cognition, and are implicated in the pathophysiology of schizophrenia through genetic and functional studies. We show that the NMDAR subunit GluN2B complexes with DISC1-associated trafficking factor TRAK1, while DISC1 interacts with the GluN1 subunit and regulates dendritic NMDAR motility in cultured mouse neurons. Moreover, in the first mutant mouse that models DISC1 disruption by the translocation, the pool of NMDAR transport vesicles and surface/synaptic NMDAR expression are increased. Since NMDAR cell surface/synaptic expression is tightly regulated to ensure correct function, these changes in the mutant mouse are likely to affect NMDAR signalling and synaptic plasticity. Consistent with these observations, RNASeq analysis of translocation carrier-derived human neurons indicates abnormalities of excitatory synapses and vesicle dynamics. RNASeq analysis of the human neurons also identifies many differentially expressed genes previously highlighted as putative schizophrenia and/or depression risk factors through large-scale genome-wide association and copy number variant studies, indicating that the translocation triggers common disease pathways that are shared with unrelated psychiatric patients. Altogether our findings suggest that translocation-induced disease mechanisms are likely to be relevant to mental illness in general, and that such disease mechanisms include altered NMDAR dynamics and excitatory synapse function. This could contribute to the cognitive disorders displayed by translocation carriers.

scholarly journals Orientation and color tuning of the human visual gamma rhythm

2021 ◽  
Author(s):  
Ye Li ◽  
William Bosking ◽  
Michael S Beauchamp ◽  
Sameer A Sheth ◽  
Daniel Yoshor ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Brain Activity ◽  
Population Level ◽  
Gamma Oscillations ◽  
Cell Orientation ◽  
Population Activity ◽  
Color Tuning ◽  
Human Visual Cortex ◽  
Gamma Rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.

scholarly journals Towards a More General Understanding of the Algorithmic Utility of Recurrent Connections

2021 ◽  
Author(s):  
Brett W. Larsen ◽  
Shaul Druckmann
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Neural Circuits ◽  
Computation Time ◽  
Computational Time ◽  
Feedforward Networks ◽  
Population Activity ◽  
Artificial Neural ◽  
The Difference ◽  
Recurrent Connections

AbstractLateral and recurrent connections are ubiquitous in biological neural circuits. The strong computational abilities of feedforward networks have been extensively studied; on the other hand, while certain roles for lateral and recurrent connections in specific computations have been described, a more complete understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Previous key studies by Minsky and later by Roelfsema argued that the sequential, parallel computations for which recurrent networks are well suited can be highly effective approaches to complex computational problems. Such “tag propagation” algorithms perform repeated, local propagation of information and were introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and demonstrate hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to multiple, interacting propagating tags and demonstrate that these are efficient computational substrates for more general computations by introducing and solving an abstracted biologically inspired decision-making task. More generally, our work clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.Author SummaryLateral and recurrent connections are ubiquitous in biological neural circuits; intriguingly, this stands in contrast to the majority of current-day artificial neural network research which primarily uses feedforward architectures except in the context of temporal sequences. This raises the possibility that part of the difference in computational capabilities between real neural circuits and artificial neural networks is accounted for by the role of recurrent connections, and as a result a more detailed understanding of the computational role played by such connections is of great importance. Making effective comparisons between architectures is a subtle challenge, however, and in this paper we leverage the computational capabilities of large-scale machine learning to robustly explore how differences in architectures affect a network’s ability to learn a task. We first focus on the task of determining whether two pixels are connected in an image which has an elegant and efficient recurrent solution: propagate a connected label or tag along paths. Inspired by this solution, we show that it can be generalized in many ways, including propagating multiple tags at once and changing the computation performed on the result of the propagation. To illustrate these generalizations, we introduce an abstracted decision-making task related to foraging in which an animal must determine whether it can avoid predators in a random environment. Our results shed light on the set of computational tasks that can be solved efficiently by recurrent computation and how these solutions may appear in neural activity.

scholarly journals Representational drift in the mouse visual cortex

2020 ◽  
Author(s):  
Daniel Deitch ◽  
Alon Rubin ◽  
Yaniv Ziv
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Population Level ◽  
Population Activity ◽  
Cortical Areas ◽  
Electrophysiological Recordings ◽  
The Face ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Over Time

AbstractNeuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such ‘representational drift’ occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells’ activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

Runtime Service Discovery for Grid Applications

2009 ◽  
pp. 212-234 ◽  
Author(s):  
James Dooley ◽  
Andrea Zisman ◽  
George Spanoudakis
Keyword(s):  
Social Networks ◽  
Service Discovery ◽  
Large Scale ◽  
Virtual Organisations ◽  
Small World ◽  
Resource Discovery ◽  
Local Network ◽  
Network Node ◽  
Grid Resource ◽  
New Generation

A Virtual Organisation in large-scale distributed systems is a set of individuals and/or institutions with some common purposes or interests that need to share their resources to further their objectives, which is similar to a human community in social networks that consists of people have common interests or goals. Due to the similarity between social networks and Grids, the concepts in social science (e.g. small world phenomenon) can be adopted for the design of new generation Grid systems. This chapter presents a Small World Architecture for Effective Virtual Organisations (SWEVO) for Grid resource discovery in Virtual Organisations, which enables Virtual Organisations working in a more collaborative manner to support decision makers. In SWEVO, Virtual Organisations are connected by a small number of interorganisational links. Not every local network node needs to be connected to remote Virtual Organisations, but every network node can efficiently find connections to specific Virtual Organisations.

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