In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Input: Consider Input: Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network. While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed to convert gated recurrent neural networks, so far.
Recurrent neural networks in the form of networks of gating memory cells have been central in state-of-the-art solutions in problem domains that involve sequence recognition or generation. Here, we design an analog gated LSTM cell where its neurons can be substituted for efficient stochastic spiking neurons. These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains. For such neurons, we approximate the effective activation function, which resembles a sigmoid. We show how analog neurons with such activation functions can be used to create an analog LSTM cell; networks of these cells can then be trained with standard backpropagation. We train these LSTM networks on a noisy and noiseless version of the original sequence prediction task from Hochreiter & Schmidhuber (1997), and also on a noisy and noiseless version of a classical working memory reinforcement learning task, the T-Maze. Substituting the analog neurons for corresponding adaptive spiking neurons, we then show that almost all resulting spiking neural network equivalents correctly compute the original tasks.

Output: We demonstrate a gated recurrent asynchronous spiking neural network that corresponds to an LSTM unit.

Input: Consider Input: Synthesizing user-intended programs from a small number of input-output exam-
ples is a challenging problem with several important applications like spreadsheet
manipulation, data wrangling and code refactoring. Existing synthesis systems
either completely rely on deductive logic techniques that are extensively hand-
engineered or on purely statistical models that need massive amounts of data, and in
general fail to provide real-time synthesis on challenging benchmarks. In this work,
we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique
that combines the best of both symbolic logic techniques and statistical models.
Thus, it produces programs that satisfy the provided specifications by construction
and generalize well on unseen examples, similar to data-driven systems. Our
technique effectively utilizes the deductive search framework to reduce the learning
problem of the neural component to a simple supervised learning setup. Further,
this allows us to both train on sparingly available real-world data and still leverage
powerful recurrent neural network encoders. We demonstrate the effectiveness
of our method by evaluating on real-world customer scenarios by synthesizing
accurate programs with up to 12 speed-up compared to state-of-the-art systems.

Output: We integrate symbolic (deductive) and statistical (neural-based) methods to enable real-time program synthesis with almost perfect generalization from 1 input-output example.

Input: Consider Input: This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.
Output: We learn deep representation by maximizing mutual information, leveraging structure in the objective, and are able to compute with fully supervised classifiers with comparable architectures