Spectral noise-shaping in integrate-and-fire neural networks

2002 ◽  
Author(s):  
R.W. Adams
Keyword(s):  
Neural Networks ◽  
Noise Shaping ◽  
Integrate And Fire

scholarly journals Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons

2007 ◽  
Vol 19 (12) ◽  
pp. 3226-3238 ◽  
Author(s):  
Arnaud Tonnelier ◽  
Hana Belmabrouk ◽  
Dominique Martinez
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Synaptic Currents ◽  
Fire Model ◽  
Integrate And Fire ◽  
Fire Models ◽  
Event Driven

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

scholarly journals Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

2020 ◽  
Vol 14 ◽  
Author(s):  
Paulo R. Protachevicz ◽  
Kelly C. Iarosz ◽  
Iberê L. Caldas ◽  
Chris G. Antonopoulos ◽  
Antonio M. Batista ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Network ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Excitatory Synapses ◽  
Closed Loops ◽  
Integrate And Fire ◽  
Network Topologies ◽  
Chemical Synapses ◽  
Synchronous Behavior

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

scholarly journals Patterns of Synchrony in Neural Networks with Spike Adaptation

2001 ◽  
Vol 13 (5) ◽  
pp. 959-992 ◽  
Author(s):  
C. van Vreeswijk ◽  
D. Hansel
Keyword(s):  
Neural Networks ◽  
Burst Activity ◽  
Integrate And Fire ◽  
Network Parameters ◽  
Excitatory Neurons ◽  
Inhibitory Feedback ◽  
Asynchronous State ◽  
Two Populations

We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one excitatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous state to the synchronized bursting state. Finally, we show that the same mechanism also causes synchronized burst activity in networks of more realistic conductance-based model neurons.

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