Editing Episodic memory (section)

==Neural network models==
{{Expand section|date=April 2012}}
Episodic memories can be stored in [[autoassociative neural network]]s (e.g., a [[Hopfield network]]) if the stored representation includes information on the spatiotemporal context in which an item was studied. Smaller memories such as words or references said by someone are labeled as inactive or active neurons in the entorhinal cortex.<ref>Michael E Hasselmo, James L McClelland, Neural models of memory, Current Opinion in Neurobiology, Volume 9, Issue 2, 1999, Pages 184-188, ISSN 0959-4388, https://doi.org/10.1016/S0959-4388(99)80025-7.</ref><ref>{{Cite journal |last1=Khalil|first1=Radwa|last2=Moftah|first2=Marie Z.|last3=Moustafa|first3=Ahmed A. |date=2017-10-19|title=The effects of dynamical synapses on firing rate activity: a spiking neural network model|journal=European Journal of Neuroscience|volume=46|issue=9|pages=2445–2470|doi=10.1111/ejn.13712 |pmid=28921686|s2cid=41810408|issn=0953-816X}}</ref>

Neural networks help us understand how the brain sends and receives different messages to the body, and how they are connected. These networks are a group of neurons or structures that are connected together. These structures work harmoniously to produce different cognitions within the brain. One of the largest proposals for this ideology is that of [[Diffusion MRI|Diffusion Tensor Imaging]].<ref>{{Cite journal|last=Henderson |first=Jaimie M.|date=2012 |title="Connectomic surgery": diffusion tensor imaging (DTI) tractography as a targeting modality for surgical modulation of neural networks|journal=Frontiers in Integrative Neuroscience |volume=6|pages=15 |doi=10.3389/fnint.2012.00015|pmid=22536176|pmc=3334531|issn=1662-5145|doi-access=free}}</ref> This technique traces the differing pathways of nerve fibres that further create communication throughout differing structures. These networks can be thought of as neural maps that can expand or contract according to the information being processed at that time. Neural Network Models can undergo learning patterns to use episodic memories to predict certain moments. Neural network models help the episodic memories by capturing the naturalistic state you are currently in such as scenery, rooms, time, smell, or even your current feeling.<ref>Qihong Lu, Uri Hasson, Kenneth A Norman (2022) A neural network model of when to retrieve and encode episodic memories eLife 11:e74445https://doi.org/10.7554/eLife.74445</ref><ref>{{Cite journal|last1=Nestor |first1=Paul G.|last2=Kubicki|first2=Marek|last3=Gurrera|first3=Ronald J.|last4=Niznikiewicz |first4=Margaret|last5=Frumin|first5=Melissa|last6=McCarley|first6=Robert W.|last7=Shenton|first7=Martha E. |date=October 2004|title=Neuropsychological Correlates of Diffusion Tensor Imaging in Schizophrenia. |journal=Neuropsychology|volume=18|issue=4|pages=629–637|doi=10.1037/0894-4105.18.4.629|pmc=2790923 |issn=1931-1559|pmid=15506830}}</ref>