How neurogenesis is involved in pattern separation

Introduction

This page is the second part of a study of the article

Adult neurogenesis: integrating theories and separating functions by James B. Aimone, Wei Deng, and Fred H. Gage

Computational theories

Theories in neuroscience aren’t always easy to understand as they hold a large amount of variants and combine ideas from many fields. Such as mathematics.

This is one of the reasons why most brain models are made in a computer. The computer also has an advantage of being able to replicate some characteristics of the brain and neurons.

These include, the amount of cells in the brain ( around 86 billion neurons and much more glia cells ) and their digital way of response.

Computational theories of pattern separation and neurogenesis

Computational theories of pattern separation show that only a small amount of neurons in the dentate gyrus are activated for any event.

This decreases the probability that the same neural pattern won’t be assigned to another event.

New neurons, from neurogenesis, may be important in this aspect by increasing the number of patterns the dentate gyrus can create and therefore increase its capacity.

They also aren’t linked to any existing memories, meaning they possibly help create memories of new events.

These new, immature neurons are easily excited and more plastic meaning they are more likely to create a response and be involved in it.

This enables it from integrating the neural network of the dentate gyrus whilst also creating a similarity in certain dentate gyrus outputs called pattern integration.

Although, with the number of mature neurons, this will hardly make a difference in the distinction of events, this will help in finding the relations of different events.

Part 1 : The dentate gyrus and pattern separation

Next Part : The maturation after neurogenesis