Context-Dependent Learning and Memory Based on Spatio-Temporal Learning Rule

2021 ◽  
pp. 89-94
Author(s):  
Hiromichi Tsukada ◽  
Minoru Tsukada
Author(s):  
Ching-Hang Chen ◽  
Tyng-Luh Liu ◽  
Yu-Shuen Wang ◽  
Hung-Kuo Chu ◽  
Nick C. Tang ◽  
...  

2021 ◽  
Vol 11 (4) ◽  
pp. 426
Author(s):  
Lydia Giménez-Llort ◽  
Mikel Santana-Santana ◽  
Míriam Ratia ◽  
Belén Pérez ◽  
Pelayo Camps ◽  
...  

A new hypothesis highlights sleep-dependent learning/memory consolidation and regards the sleep-wake cycle as a modulator of β-amyloid and tau Alzheimer’s disease (AD) pathologies. Sundowning behavior is a common neuropsychiatric symptom (NPS) associated with dementia. Sleep fragmentation resulting from disturbances in sleep and circadian rhythms in AD may have important consequences on memory processes and exacerbate the other AD-NPS. The present work studied the effect of training time schedules on 12-month-old male 3xTg-AD mice modeling advanced disease stages. Their performance in two paradigms of the Morris water maze for spatial-reference and visual-perceptual learning and memory were found impaired at midday, after 4 h of non-active phase. In contrast, early-morning trained littermates, slowing down from their active phase, exhibited better performance and used goal-directed strategies and non-search navigation described for normal aging. The novel multitarget anticholinesterasic compound AVCRI104P3 (0.6 µmol·kg−1, 21 days i.p.) exerted stronger cognitive benefits than its in vitro equipotent dose of AChEI huprine X (0.12 μmol·kg−1, 21 days i.p.). Both compounds showed streamlined drug effectiveness, independently of the schedule. Their effects on anxiety-like behaviors were moderate. The results open a question of how time schedules modulate the capacity to respond to task demands and to assess/elucidate new drug effectiveness.


2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.


2012 ◽  
Vol 19 (5) ◽  
pp. 182-189 ◽  
Author(s):  
J. N. Lugo ◽  
A. L. Brewster ◽  
C. M. Spencer ◽  
A. E. Anderson

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