Learning sensory maps with real-world stimuli in real time using a biophysically realistic learning rule.

We present a real-time model of learning in the auditory cortex that is trained using real-world stimuli. The system consists of a peripheral and a central cortical network of spiking neurons. The synapses formed by peripheral neurons on the central ones are subject to synaptic plasticity. We implemented a biophysically realistic learning rule that depends on the precise temporal relation of pre- and postsynaptic action potentials. We demonstrate that this biologically realistic real-time neuronal system forms stable receptive fields that accurately reflect the spectral content of the input signals and that the size of these representations can be biased by global signals acting on the local learning mechanism. In addition, we show that this learning mechanism shows fast acquisition and is robust in the presence of large imbalances in the probability of occurrence of individual stimuli and noise.

Local and global gating of synaptic plasticity.

Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.

A central pattern generator to control a pyloric-based system.

A central pattern generator (CPG) is built to control a mechanical device (plant) inspired by the pyloric chamber of the lobster. Conductance-based models are used to construct the neurons of the CPG. The plant has an associated function that measures the amount of food flowing through it per unit of time. We search for the best set of solutions that give a high positive flow of food in the maximization function. The plant is symmetric and the model neurons are identical to avoid any bias in the space of solutions. We find that the solution is not unique and that three neurons are sufficient to produce positive flow. We propose an effective principle for CPGs (effective on-off connectivity) and a few predictions to be corroborated in the pyloric system of the lobster.