Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Poirazi, P."'
Autor:
Chavlis, Spyridon, Poirazi, Panayiota
Artificial neural networks (ANNs) are at the core of most Deep learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackl
Externí odkaz:
http://arxiv.org/abs/2404.03708
The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training wh
Externí odkaz:
http://arxiv.org/abs/2306.08007
The brain is a highly efficient system evolved to achieve high performance with limited resources. We propose that dendrites make information processing and storage in the brain more efficient through the segregation of inputs and their conditional i
Externí odkaz:
http://arxiv.org/abs/2306.07101
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets. However, the predictive ability of these architectures significantly decreases when learning new classes incrementally. This is due to t
Externí odkaz:
http://arxiv.org/abs/2110.13611
Autor:
Chavlis, Spyridon, Poirazi, Panayiota
Publikováno v:
Current Opinion in Neurobiology 70 (2021): 1-10
This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computa
Externí odkaz:
http://arxiv.org/abs/2106.07490
Autor:
Konstantinos-Evangelos Petousakis, Jiyoung Park, Athanasia Papoutsi, Stelios Smirnakis, Panayiota Poirazi
Publikováno v:
eLife, Vol 12 (2023)
Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical
Externí odkaz:
https://doaj.org/article/ed51f923562f451e918482b23e593d42
Autor:
Troullinou, Eirini, Tsagkatakis, Grigorios, Chavlis, Spyridon, Turi, Gergely, Li, Wen-Ke, Losonczy, Attila, Tsakalides, Panagiotis, Poirazi, Panayiota
Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological complexity. Typical
Externí odkaz:
http://arxiv.org/abs/1911.09977
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-16 (2023)
Biologically inspired spiking neural networks are highly promising, but remain simplified omitting relevant biological details. The authors introduce here theoretical and numerical frameworks for incorporating dendritic features in spiking neural net
Externí odkaz:
https://doaj.org/article/b556fcbd6d4b4547ac46d1919805abf6
Publikováno v:
Frontiers in Behavioral Neuroscience, Vol 17 (2023)
Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linea
Externí odkaz:
https://doaj.org/article/6835149d70e945a1acf1c44c4257b739
Publikováno v:
Cell Reports, Vol 42, Iss 1, Pp 111962- (2023)
Summary: The lateral entorhinal cortex (LEC) provides multisensory information to the hippocampus, directly to the distal dendrites of CA1 pyramidal neurons. LEC neurons perform important functions for episodic memory processing, coding for contextua
Externí odkaz:
https://doaj.org/article/9289c25201c94f46a68fff828d208029