neuron ensemble
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Author(s):  
Satoshi Iwasaki ◽  
Yuji Ikegaya

Abstract Memory retrieval depends on reactivation of memory engram cells. Inadvertent activation of these cells is expected to cause memory-retrieval failure, but little is known about how noisy activity of memory-irrelevant neurons impacts mnemonic processes. Here, we report that optogenetic nonselective activation of only tens of hippocampal CA1 cells (∼0.01% of the total cells in the CA1 pyramidal cell layer) impairs contextual fear memory recall. Memory recall failure was associated with altered neuronal reactivation in the basolateral amygdala. These results indicate that hippocampal memory retrieval requires strictly regulated activation of a specific neuron ensemble and is easily disrupted by the introduction of noisy CA1 activity, suggesting that reactivating memory engram cells as well as silencing memory-irrelevant neurons are both crucial for memory retrieval.



eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Maryna Pilkiw ◽  
Nathan Insel ◽  
Younghua Cui ◽  
Caitlin Finney ◽  
Mark D Morrissey ◽  
...  

The lateral entorhinal cortex (LEC) is thought to bind sensory events with the environment where they took place. To compare the relative influence of transient events and temporally stable environmental stimuli on the firing of LEC cells, we recorded neuron spiking patterns in the region during blocks of a trace eyeblink conditioning paradigm performed in two environments and with different conditioning stimuli. Firing rates of some neurons were phasically selective for conditioned stimuli in a way that depended on which room the rat was in; nearly all neurons were tonically selective for environments in a way that depended on which stimuli had been presented in those environments. As rats moved from one environment to another, tonic neuron ensemble activity exhibited prospective information about the conditioned stimulus associated with the environment. Thus, the LEC formed phasic and tonic codes for event-environment associations, thereby accurately differentiating multiple experiences with overlapping features.



2017 ◽  
Author(s):  
Maryna Pilkiw ◽  
Nathan Insel ◽  
Younghua Cui ◽  
Caitlin Finney ◽  
Mark D Morrissey ◽  
...  




2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Cao ◽  
Yaoyao Hao ◽  
Yuxi Liao ◽  
Kai Xu ◽  
Yiwen Wang ◽  
...  

Previous studies have shown that the dorsal premotor cortex (PMd) neurons are relevant to reaching as well as grasping. In order to investigate their specific contribution to reaching and grasping, respectively, we design two experimental paradigms to separate these two factors. Two monkeys are instructed to reach in four directions but grasp the same object and grasp four different objects but reach in the same direction. Activities of the neuron ensemble in PMd of the two monkeys are collected while performing the tasks. Mutual information (MI) is carried out to quantitatively evaluate the neurons’ tuning property in both tasks. We find that there exist neurons in PMd that are tuned only to reaching, tuned only to grasping, and tuned to both tasks. When applied with a support vector machine (SVM), the movement decoding accuracy by the tuned neuron subset in either task is quite close to the performance by full ensemble. Furthermore, the decoding performance improves significantly by adding the neurons tuned to both tasks into the neurons tuned to one property only. These results quantitatively distinguish the diversity of the neurons tuned to reaching and grasping in the PMd area and verify their corresponding contributions to BMI decoding.



Pharmacology ◽  
2012 ◽  
Vol 90 (5-6) ◽  
pp. 264-273 ◽  
Author(s):  
Janine M. Prast ◽  
Kai K. Kummer ◽  
Constanze M. Barwitz ◽  
Christian Humpel ◽  
Georg Dechant ◽  
...  


2011 ◽  
Vol 23 (8) ◽  
pp. 2032-2057 ◽  
Author(s):  
Dequan Jin ◽  
Jigen Peng ◽  
Bin Li

In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.



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