Modeling the receptive field development

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Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

Explores short-term and long-term synaptic changes, Hebbian learning, and the role of neuromodulators.

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.

Covers Hebbian learning, reinforcement learning, types of learning, neuron models, learning rules, and weight homeostasis.

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Explores different forms of synaptic plasticity and computational models to understand its mechanisms and induction protocols.

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.