• Publications
  • Influence
Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons
  • N. Brunel
  • Computer Science, Medicine
    Journal of Computational Neuroscience
  • 1 May 2000
The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous
Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex.
TLDR
This work finds that if the average synaptic potentiation (LTP) is too low, no stimulus specific activity manifests itself in the delay period, and investigates self-sustaining stable states (attractors) in networks of integrate-and-fire neurons.
Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.
TLDR
A network model endowed with a columnar architecture and based on the physiological properties of cortical neurons and synapses finds that recurrent synaptic excitation should be primarily mediated by NMDA receptors, and that overall recurrent synaptic interactions should be dominated by inhibition.
Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition
TLDR
Several mechanisms that enhance the signal-to-noise ratio in working memory states could be implemented in the prefrontal cortex by dopaminergic projections from the midbrain, using a recurrent network model of object working memory.
How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs
TLDR
This study examines the ability of neurons to track temporally varying inputs by investigating how the instantaneous firing rate of a neuron is modulated by a noisy input with a small sinusoidal component with frequency, and proposes a simplified one-variable model, the “exponential integrate-and-fire neuron,” as an approximation of a conductance-based model.
Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates
We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the
What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance.
TLDR
This work shows how to derive quantitatively the coherent oscillation frequency for a randomly connected network of leaky integrate-and-fire neurons with realistic synaptic parameters.
From subthreshold to firing-rate resonance.
TLDR
It is suggested that resonant neurons are able to communicate their frequency preference to postsynaptic targets when the level of noise is comparable to that prevailing in vivo, and the modulatory effect an additional weak oscillating current has on the instantaneous firing rate.
Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons
TLDR
The detailed calculations showing that if a synaptic decay time constant is included in the synaptic current model, the firing-rate modulation of the neuron due to an oscillatory input remains finite in the high-frequency limit with no phase lag are reported.
Electrical Coupling Mediates Tunable Low-Frequency Oscillations and Resonance in the Cerebellar Golgi Cell Network
TLDR
Results suggest a major role for Golgi cells in coordinating cerebellar sensorimotor integration during oscillatory interactions, and show that electrical transmission of the spike afterhyperpolarization is the essential component for oscillatory population synchronization.
...
1
2
3
4
5
...