Editing Cognitive neuroscience (section)

=== Cognitive Neuroscience and Artificial Intelligence ===
{{Main|Artificial Intelligence}}
Cognitive neuroscience has played a major role in shaping [[Artificial intelligence in healthcare|artificial intelligence]] (AI). By studying how the human brain processes information, researchers have developed AI systems that simulate cognitive functions like learning, pattern recognition, and decision-making. A good example of this is neural networks, which are inspired by the connections between neurons in the brain. These networks form the foundation of many AI applications.<ref>{{cite journal |last1=LeCun |first1=Yann |last2=Bengio |first2=Yoshua |last3=Hinton |first3=Geoffrey |title=Deep learning |journal=Nature |date=28 May 2015 |volume=521 |issue=7553 |pages=436–444 |doi=10.1038/nature14539 |pmid=26017442 |bibcode=2015Natur.521..436L }}</ref>

Deep learning, a subfield of AI, uses neural networks to replicate processes similar to those in the human brain. For instance, convolutional neural networks (CNNs) are modeled after the visual system and have transformed tasks like image recognition and speech analysis. AI also benefits from advancements in brain imaging technologies, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). These tools provide valuable insights into neural activity, which help improve AI systems designed to mimic human thought processes.<ref>{{Cite journal |last1=Lake |first1=Brenden M. |last2=Ullman |first2=Tomer D. |last3=Tenenbaum |first3=Joshua B. |last4=Gershman |first4=Samuel J. |date=2017 |title=Building machines that learn and think like people |journal=Behavioral and Brain Sciences |language=en |volume=40 |pages=e253 |doi=10.1017/S0140525X16001837 |pmid=27881212 |arxiv=1604.00289 }}</ref>

Despite the progress, replicating the complexity of human cognition remains a challenge. Researchers are now exploring hybrid models that combine neural networks with symbolic reasoning to better mimic how humans think and solve problems. This approach shows promise for addressing some of the limitations of current AI systems.<ref>{{cite journal |last1=Langley |first1=Christelle |last2=Cirstea |first2=Bogdan Ionut |last3=Cuzzolin |first3=Fabio |last4=Sahakian |first4=Barbara J. |title=Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review |journal=Frontiers in Artificial Intelligence |date=5 April 2022 |volume=5 |doi=10.3389/frai.2022.778852 |doi-access=free |pmc=9038841 |pmid=35493614 }}</ref>