Chaotic balanced state in a model of cortical circuits
Neural Computation
Neural Computation
Neurons Tune to the Earliest Spikes Through STDP
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
2008 Special Issue: The state of MIIND
Neural Networks
Simplicity and efficiency of integrate-and-fire neuron models
Neural Computation
Vectorized algorithms for spiking neural network simulation
Neural Computation
Spiking neurons that keep the rhythm
Journal of Computational Neuroscience
Compositionality of arm movements can be realized by propagating synchrony
Journal of Computational Neuroscience
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Journal of Computational Neuroscience
A model for complex sequence learning and reproduction in neural populations
Journal of Computational Neuroscience
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the network is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group.