Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neuronal tuning: to sharpen or broaden
Neural Computation
Narrow versus wide turning curves: what's best for a population code?
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
A sparse generative model of v1 simple cells with intrinsic plasticity
Neural Computation
Sleeping our way to weight normalization and stable learning
Neural Computation
A self-organizing map with homeostatic synaptic scaling
Neurocomputing
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Neurons in the nervous system display a wide variety of plasticity processes. Among them are covariance-based rules and homeostatic plasticity. By themselves, the first ones tend to generate instabilities because of the unbounded potentiation of synapses. The second ones tend to stabilize the system by setting a target for the postsynaptic firing rate. In this work, we analyze the combined effect of these two mechanisms in a simple model of hypercolumn of the visual cortex. We find that the presence of homeostatic plasticity together with nonplastic uniform inhibition stabilizes the effect of Hebbian plasticity. The system can reach nontrivial solutions, where the recurrent intracortical connections are strongly modulated. The modulation is strong enough to generate contrast invariance. Moreover, this state can be reached even beginning from a weakly modulated initial condition.