# Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)

## TL;DR

This paper identifies a non-uniqueness problem in using deep convolutional neural networks to model neural object representations in primate cortex, highlighting a theoretical limitation of this approach.

## Contribution

It uncovers an inherent non-uniqueness issue in DCNN-based modeling of neural object representations, emphasizing the need for caution in practical applications.

## Key findings

- Non-uniqueness phenomenon exists in DCNN models
- Highlights theoretical limitations of DCNN in neural modeling
- Calls for careful interpretation of DCNN-based neural representations

## Abstract

Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex. In this work, we show that some inherent non-uniqueness problem exists in the DCNN-based modelling of image object representations. This non-uniqueness phenomenon reveals to some extent the theoretical limitation of this general modelling approach, and invites due attention to be taken in practice.