recurrent neural networkIn an experiment of multi-trial task to obtain a reward, reward expectancy neurons, which responded only in the non-reward trials that are necessary to advance toward the reward, have been observed in the anterior cingulate cortex of monkeys. In this paper, to explain ...
Our results are directly applicable to infinite-width limit of neural networks that admit a kernel description (including feedforward, convolutional and recurrent neural networks)13,55,56,57,58,59, and explain their inductive bias towards simple functions35,51,60,61,62,63,64. We also note a ...
Deep belief net Convolutional neural network Recurrent neural network Reinforcement learning to neural network Use cases Driverless vehicles Virtual assistants chatbots Medical research Facial recognition Robotics robot is controlled by remote Robotics is a branch of engineering that involves the conception, d...
Long short term memory (LSTM) is a type of recurrent neural networks (RNN), which allows modelling temporal dynamic behaviour by incorporating feedback connections in their architecture35. Our exploratory and reward-oblivious models are both LSTM models with four units. We used a single layer LSTM...
Generating sequences with recurrent neural progress more towards more human-like text models. networks. arXiv preprint arXiv:1308.0850, 2013. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Gi- References annotti, F., and Pedreschi, D. A survey of methods for explaining black ...
Attractors in the high-dimensional network represent the neural patterns inferred from M2 population activity. Previous experiments showed that recurrent circuits between cortical areas such as M2 and subcortical areas such as thalamus (Guo et al., 2018, 2017) and basal ganglia nuclei (Hélie et ...
aEmergence of hierarchical structure mirroring linguistic composition in a recurrent neural network Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network[translate] aIF YOU LIKE COME AND TALK WITH ME 如果您喜欢来并且与我谈话[translate] ...
A recurrent neural network is able to explain variance in human prediction errors whereas the Rescorla-Wagner model performs less well. The Rescorla-Wagner prediction associations do however explain more variance in human reading times. Moreover, the Rescorla-Wagner model associations explain more ...
(1991) Positive feedback in the cerebro- cerebellar recurrent network may explain rotation of population vectors. In: Analysis and Modeling of Neural Systems Eeckman, F. (ed). Kluwer Academic Publishers. p371-376EISENMANIN, KEIFERJ, HOUK JC: Positive Feedback in the * Cerebro-Cerebellar ...
We then showed that metastable attractors, representing activity patterns with the requisite combination of reliable sequential structure and high transition timing variability, could be produced by reciprocally coupling a high dimensional recurrent network and a low dimensional feedforward one. Transitions ...