scholarly journals Cyclic transitions between higher order motifs underlie sustained activity in asynchronous sparse recurrent networks

2019 ◽  
Author(s):  
Kyle Bojanek ◽  
Yuqing Zhu ◽  
Jason MacLean
Keyword(s):  
Neural Network ◽  
Higher Order ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Functional Networks ◽  
Dynamical Regime ◽  
Spiking Neural Network ◽  
Recurrent Networks ◽  
Sustained Activity ◽  
Spiking Activity

AbstractMany studies have demonstrated the prominence of higher-order patterns in excitatory synaptic connectivity as well as activity in neocortex. Surveyed as a whole, these results suggest that there may be an essential role for higher-order patterns in neocortical function. In order to stably propagate signal within and between regions of neocortex, the most basic - yet nontrivial - function which neocortical circuitry must satisfy is the ability to maintain stable spiking activity over time. Here we algorithmically construct spiking neural network models comprised of 5000 neurons using topological statistics from neocortex and a set of objective functions that identify networks which produce naturalistic low-rate, asynchronous, and critical activity. We find that the same network topology can exhibit either sustained activity under one set of initial membrane voltages or truncated activity under a different set. Yet these two outcomes are not readily differentiated by rate or criticality. By summarizing the statistical dependencies in the pairwise activity of neurons as directed weighted functional networks, we examined the transient manifestations of higher-order motifs in the functional networks across time. We find that stereotyped low variance cyclic transitions between three isomorphic triangle motifs, quantified as a Markov process, are required for sustained activity. If the network fails to engage the dynamical regime characterized by a recurring stable pattern of motif dominance, spiking activity ceased. Motif cycling generalized across manipulations of synaptic weights and across topologies, demonstrating the robustness of this dynamical regime for sustained spiking in critical asynchronous network activity. Our results point to the necessity of higher-order patterns amongst excitatory connections for sustaining activity in sparse recurrent networks. They also provide a possible explanation as to why such excitatory synaptic connectivity and activity patterns have been prominently reported in neocortex.Author summaryHere we address two questions. First, it remains unclear how activity propagates stably through a network since neurons are leaky and connectivity is sparse and weak. Second, higher order patterns abound in neocortex, hinting at potential functional relevance for their presence. Several lines of evidence suggest that higher-order network interactions may be instrumental for spike propagation. For example, excitatory synaptic connectivity shows a prevalence of local neuronal cliques and patterns, and propagating activity in vivo displays elevated clustering dominated by specific triplet motifs. In this study we demonstrate a mechanistic link between activity propagation and higher-order motifs at the level of individual neurons and across networks. We algorithmically build spiking neural network (SNN) models to mirror the topological and dynamical statistics of neocortex. Using a combination of graph theory, information theory, and probabilistic tools, we show that higher order coordination of synapses is necessary for sustaining activity. Coordination takes the form of cyclic transitions between specific triangle motifs. The results of our model are consistent with numerous experimental observations in neuroscience, and their generalizability to other weakly and sparsely connected networks is predicted.

scholarly journals Impact of higher order network structure on emergent cortical activity

2020 ◽  
Vol 4 (1) ◽  
pp. 292-314 ◽  
Author(s):  
Max Nolte ◽  
Eyal Gal ◽  
Henry Markram ◽  
Michael W. Reimann
Keyword(s):  
Network Structure ◽  
Morphological Diversity ◽  
Cortical Activity ◽  
Higher Order ◽  
Neuronal Firing ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Order Structure ◽  
First Order ◽  
Neuronal Types

Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher order network structure, and suggests that the higher order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

scholarly journals Stochastic Dynamics of a Finite-Size Spiking Neural Network

2007 ◽  
Vol 19 (12) ◽  
pp. 3262-3292 ◽  
Author(s):  
Hédi Soula ◽  
Carson C. Chow
Keyword(s):  
Neural Network ◽  
Equilibrium Distribution ◽  
Stochastic Dynamics ◽  
Mean Field Theory ◽  
Mean Field ◽  
Finite Size ◽  
Network Activity ◽  
Neural Dynamics ◽  
Spiking Neural Network ◽  
Firing Activity

We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution, mean rate, variance, and autocorrelation function of the network activity. The model is applicable to any network where the probability of firing of a neuron in the network depends on only the number of neurons that fired in a previous temporal epoch. Networks with statistically homogeneous connectivity and membrane and synaptic time constants that are not excessively long could satisfy these conditions. Our model completely accounts for the size of the network and correlations in the firing activity. It also allows us to examine how the network dynamics can deviate from mean field theory. We show that the model and solutions are applicable to spiking neural networks in biophysically plausible parameter regimes.

scholarly journals Spiking neural network model of motor cortex with joint excitatory and inhibitory clusters reflects task uncertainty, reaction times, and variability dynamics

2020 ◽  
Author(s):  
Vahid Rostami ◽  
Thomas Rost ◽  
Alexa Riehle ◽  
Sacha J. van Albada ◽  
Martin P. Nawrot
Keyword(s):  
Neural Network ◽  
Motor Cortex ◽  
Firing Rate ◽  
Network Topology ◽  
Network Architecture ◽  
Reaction Times ◽  
Network Activity ◽  
Behavioral Variability ◽  
Spiking Neural Network ◽  
Neural Network Models

AbstractBoth neural activity and behavior of highly trained animals are strikingly variable across repetition of behavioral trials. The neural variability consistently decreases during behavioral tasks, in both sensory and motor cortices. The behavioral variability, on the other hand, changes depending on the difficulty of the task and animal performance.Here we study a mechanism for such variability in spiking neural network models with cluster topologies that enable multistability and attractor dynamics, features subserving functional roles such as decision-making, (working) memory and learning. Multistable attractors have been studied in spiking neural networks through clusters of strongly interconnected excitatory neurons. However, we show that this network topology results in the loss of excitation/inhibition balance and does not confer robustness against modulation of network activity. Moreover, it leads to widely separated firing rate states of single neurons, inconsistent with experimental observations.To overcome these problems we propose that a combination of excitatory and inhibitory clustering restores local excitation/inhibition balance. This network architecture is inspired by recent anatomical and physiological studies which point to increased local inhibitory connectivity and possible inhibitory clustering through connection strengths.We find that inhibitory clustering supports realistic spiking activity in terms of a biologically realistic firing rate, spiking irregularity, and trial-to-trial spike count variability. Furthermore, with the appropriate stimulation of network clusters, this network topology enabled us to qualitatively and quantitatively reproduce in vivo firing rate, variability dynamics and behavioral reaction times for different task conditions as observed in recordings from the motor cortex of behaving monkeys.

scholarly journals Impact of higher-order network structure on emergent cortical activity

2019 ◽  
Author(s):  
Max Nolte ◽  
Eyal Gal ◽  
Henry Markram ◽  
Michael W. Reimann
Keyword(s):  
Network Structure ◽  
Morphological Diversity ◽  
Cortical Activity ◽  
Higher Order ◽  
Neuronal Firing ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Order Structure ◽  
First Order ◽  
Neuronal Types

ABSTRACTSynaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher-order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher-order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher-order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher-order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher-order network structure, and suggests that the higher-order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters

scholarly journals Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

scholarly journals Alzheimer’s Disease Progressively Reduces Visual Functional Network Connectivity

2021 ◽  
pp. 1-14
Author(s):  
Jie Huang ◽  
Paul Beach ◽  
Andrea Bozoki ◽  
David C. Zhu
Keyword(s):  
Visual Cortex ◽  
Lower Order ◽  
Resting State ◽  
Visual Processing ◽  
Network Connectivity ◽  
Higher Order ◽  
Functional Network ◽  
Functional Networks ◽  
The Face ◽  
Processing Network

Background: Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression. Objective: This study investigated the effect of AD severity on visual functional network. Methods: Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group. Results: For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas. Conclusion: These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

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