<commit_before>---
author: "<NAME>, <NAME> & <NAME>"
description: "This paper proposes a network model composed of columns and layers that performs robust object learning and recognition. The model introduces a new feature to cortical columns, location information, which is represented relative to the object being sensed."
keywords: sensorimotor inference, object recognition
category: neuroscience, sensorimotor
date: 2018/10/05
hideImage: true
image: ../images/network-diagram.svg
link:
org: "Preprint of journal submission"
sort: a
title: " Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells"
type: post

---

| | |
|-|-|
| **Authors** | <NAME>, <NAME>, <NAME>, and <NAME>|
| **Journal** | Submitted |
| **Online** | [Read or Download the Preprint][1] |
| **Published** | October 5, 2018 |

Our October 2017 paper, “[A Theory of How Columns in the Neocortex Enable Learning the Structure of the World](/neuroscience-research/research-publications/papers/a-theory-of-how-columns-in-the-neocortex-enable-learning-the-structure-of-the-world/),” proposed a model for learning objects by using object-specific location signals. This new paper explains how these signals can be generated with a location layer that utilizes grid-cell-like neurons. This paper includes:

1.	A location layer implementation that has multiple grid-like modules that can uniquely encode locations on many complex objects. The layer uses sensory-derived and motor signals to infer the object and location over several sensations and movements.<commit_msg>updating authors<commit_after>---
author: "<NAME>, <NAME>, <NAME>, and <NAME>"
description: "This paper proposes a network model composed of columns and layers that performs robust object learning and recognition. The model introduces a new feature to cortical columns, location information, which is represented relative to the object being sensed."
keywords: sensorimotor inference, object recognition
category: neuroscience, sensorimotor
date: 2018/10/05
hideImage: true
image: ../images/network-diagram.svg
link:
org: "Preprint of journal submission"
sort: a
title: " Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells"
type: post

---

| | |
|-|-|
| **Authors** | <NAME>, <NAME>, <NAME>, and <NAME>|
| **Journal** | Submitted |
| **Online** | [Read or Download the Preprint][1] |
| **Published** | October 5, 2018 |

Our October 2017 paper, “[A Theory of How Columns in the Neocortex Enable Learning the Structure of the World](/neuroscience-research/research-publications/papers/a-theory-of-how-columns-in-the-neocortex-enable-learning-the-structure-of-the-world/),” proposed a model for learning objects by using object-specific location signals. This new paper explains how these signals can be generated with a location layer that utilizes grid-cell-like neurons. This paper includes:

1.	A location layer implementation that has multiple grid-like modules that can uniquely encode locations on many complex objects. The layer uses sensory-derived and motor signals to infer the object and location over several sensations and movements.
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