scholarly journals Robust Working Memory through Short-Term Synaptic Plasticity

2022 ◽  
Author(s):  
Leo Kozachkov ◽  
John Tauber ◽  
Mikael Lundqvist ◽  
Scott L Brincat ◽  
Jean-Jacques Slotine ◽  
...  

Working memory has long been thought to arise from sustained spiking/attractor dynamics. However, recent work has suggested that short-term synaptic plasticity (STSP) may help maintain attractor states over gaps in time with little or no spiking. To determine if STSP endows additional functional advantages, we trained artificial recurrent neural networks (RNNs) with and without STSP to perform an object working memory task. We found that RNNs with and without STSP were both able to maintain memories over distractors presented in the middle of the memory delay. However, RNNs with STSP showed activity that was similar to that seen in the cortex of monkeys performing the same task. By contrast, RNNs without STSP showed activity that was less brain-like. Further, RNNs with STSP were more robust to noise and network degradation than RNNs without STSP. These results show that STSP can not only help maintain working memories, it also makes neural networks more robust.

2021 ◽  
Author(s):  
Quan Wan ◽  
Jorge A. Menendez ◽  
Bradley R. Postle

How does the brain prioritize among the contents of working memory to appropriately guide behavior? Using inverted encoding modeling (IEM), previous work (Wan et al., 2020) showed that unprioritized memory items (UMI) are actively represented in the brain but in a “flipped”, or opposite, format compared to prioritized memory items (PMI). To gain insight into the mechanisms underlying the UMI-to-PMI representational transformation, we trained recurrent neural networks (RNNs) with an LSTM architecture to perform a 2-back working memory task. Visualization of the LSTM hidden layer activity using Principle Component Analysis (PCA) revealed that the UMI representation is rotationally remapped to that of PMI, and this was quantified and confirmed via demixed PCA. The application of the same analyses to the EEG dataset of Wan et al. (2020) revealed similar rotational remapping between the UMI and PMI representations. These results identify rotational remapping as a candidate neural computation employed in the dynamic prioritization within contents of working memory.


Author(s):  
Francesco Panico ◽  
Stefania De Marco ◽  
Laura Sagliano ◽  
Francesca D’Olimpio ◽  
Dario Grossi ◽  
...  

AbstractThe Corsi Block-Tapping test (CBT) is a measure of spatial working memory (WM) in clinical practice, requiring an examinee to reproduce sequences of cubes tapped by an examiner. CBT implies complementary behaviors in the examiners and the examinees, as they have to attend a precise turn taking. Previous studies demonstrated that the Prefrontal Cortex (PFC) is activated during CBT, but scarce evidence is available on the neural correlates of CBT in the real setting. We assessed PFC activity in dyads of examiner–examinee participants while completing the real version of CBT, during conditions of increasing and exceeding workload. This procedure allowed to investigate whether brain activity in the dyads is coordinated. Results in the examinees showed that PFC activity was higher when the workload approached or reached participants’ spatial WM span, and lower during workload conditions that were largely below or above their span. Interestingly, findings in the examiners paralleled the ones in the examinees, as examiners’ brain activity increased and decreased in a similar way as the examinees’ one. In the examiners, higher left-hemisphere activity was observed suggesting the likely activation of non-spatial WM processes. Data support a bell-shaped relationship between cognitive load and brain activity, and provide original insights on the cognitive processes activated in the examiner during CBT.


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