Neural activity of heterogeneous inhibitory spiking networks with delay

被引:6
|
作者
Luccioli, Stefano [1 ]
Angulo-Garcia, David [2 ]
Torcini, Alessandro [1 ,3 ]
机构
[1] CNR, Ist Sistemi Complessi, Via Madonna Piano 10, I-50019 Sesto Fiorentino, Italy
[2] Univ Cartagena, Grp Modelado Computac Dinam & Complejidad Sistem, Inst Matemat Aplicadas, Carrera 6 36-100, Cartagena De Indias, Colombia
[3] Univ Cergy Pontoise, Lab Phys Theor & Modelisat, CNRS, UMR 8089, F-95302 Cergy Pontoise, France
关键词
WINNER-TAKE-ALL; NEURONS; SYNCHRONIZATION; DYNAMICS; POPULATIONS; COMPETITION; PLASTICITY; DIVERSITY; CIRCUITS; RHYTHMS;
D O I
10.1103/PhysRevE.99.052412
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a winner-takes-all mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of subthreshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behavior alternating periods of silence with periods where they fire freely in absence of any inhibition.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks
    Lecerf, Gwendal
    Tomas, Jean
    Saighi, Sylvain
    [J]. 2013 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2013, : 1616 - 1619
  • [2] Federated Learning with Spiking Neural Networks in Heterogeneous Systems
    Tumpa, Sadia Anjum
    Singh, Sonali
    Khan, Md Fahim Faysal
    Kandemir, Mahmut Tylan
    Narayanan, Vijaykrishnan
    Das, Chita R.
    [J]. 2023 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI, ISVLSI, 2023, : 49 - 54
  • [3] Macroscopic dynamics of neural networks with heterogeneous spiking thresholds
    Gast, Richard
    Solla, Sara A.
    Kennedy, Ann
    [J]. PHYSICAL REVIEW E, 2023, 107 (02)
  • [4] Heterogeneous Responses to Changes in Inhibitory Synaptic Strength in Networks of Spiking Neurons
    Li, H. Y.
    Cheng, G. M.
    Ching, Emily S. C.
    [J]. FRONTIERS IN CELLULAR NEUROSCIENCE, 2022, 16
  • [5] Oscillatory activity in the neural networks of spiking elements
    Borisyuk, R
    [J]. BIOSYSTEMS, 2002, 67 (1-3) : 3 - 16
  • [6] Axonal Delay Controller for Spiking Neural Networks Based on FPGA
    Zapata, Mireya
    Madrenas, Jordi
    Zapata, Miroslava
    Alvarez, Jorge
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, SOFTWARE AND SYSTEMS ENGINEERING, 2020, 965 : 284 - 292
  • [7] A simple programmable axonal delay scheme for spiking neural networks
    Dowrick, Thomas
    Hall, Steve
    McDaid, Liam
    [J]. NEUROCOMPUTING, 2013, 108 : 79 - 83
  • [8] Delay learning based on temporal coding in Spiking Neural Networks
    Sun, Pengfei
    Wu, Jibin
    Zhang, Malu
    Devos, Paul
    Botteldooren, Dick
    [J]. NEURAL NETWORKS, 2024, 180
  • [9] Heterogeneous Axonal Delay Improves the Spiking Activity Propagation on a Toroidal Network
    Marcello Salustri
    Ruggero Micheletto
    [J]. Cognitive Computation, 2023, 15 : 1231 - 1242
  • [10] Heterogeneous Axonal Delay Improves the Spiking Activity Propagation on a Toroidal Network
    Salustri, Marcello
    Micheletto, Ruggero
    [J]. COGNITIVE COMPUTATION, 2023, 15 (04) : 1231 - 1242