# Can One Design a Series of Brains for Neuromorphic Computing to solve complex inverse problems

## TL;DR

This paper discusses designing neuromorphic systems with memristive circuits to effectively solve complex inverse problems, emphasizing training algorithms for optimal memristive configurations.

## Contribution

It introduces a novel approach to train memristive circuits within neuromorphic computing to address ill-posed inverse problems based on finite element methods.

## Key findings

- Proposed a method to train memristive circuits for inverse problems
- Demonstrated the potential of neuromorphic systems in complex problem solving
- Analyzed the dynamics of memristive circuits in this context

## Abstract

In this position paper, we present a discussion on neuromorphic computing and especially the learning/training algorithm to design a series of brains with different memristive values to solve complex ill-posed inverse problems based on a Finite Element(FE) method. First, the neuromorphic computing is addressed and we focus on a type of memristive circuit computing that falls into the scope of neuromorphic computing. Secondly based on reference [1] in which the complex dynamics of the complex memristive circuit was studied, we design a method and an approach to train the memristive circuit so that the memristive values are optimally obtained.

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Source: https://tomesphere.com/paper/1903.02524