Methods and systems for simulating dynamical systems via synaptic descent in artificial neural networks

Abstract

The present invention relates to methods and systems for using neural networks to simulate dynamical systems for purposes of solving optimization problems. More specifically, the present invention defines methods and systems that perform a process of 'synaptic descent' for performing “synaptic descent”, wherein the state of a given synapse in a neural network is a variable being optimized, the input to the synapse is a gradient defined with respect to this state, and the synapse implements the computations of an optimizer that performs gradient descent over time. Synapse models regulate the dynamics of a given neural network by governing how the output of one neuron is passed as input to another, and since the process of synaptic descent performs gradient descent with respect to state variables defining these dynamics, it can be harnessed to evolve the neural network towards a state or sequence of states that encodes the solution to an optimization problem.