It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain function into a universal cognitive system that relates mental operations/cognitive processes/psychological tasks to brain networks?

As observed by them, this "assumption

Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech.

However, nearly all existing subset selection techniques ignore underlying dynamics of data and treat items independently, leading to incompatible sets of representatives.

Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users.

In this paper, we suggest a novel data-driven approach to active learning (AL).

Neuroscientists classify neurons into different types tha t perform similar computations at different locations in the visual field. Traditio nal methods for neural system identification do not capitalize on this separation o f "what" and "where". Learning deep convolutional feature spaces that are shared among many neurons provides an exciting path forward, but the architectural de sign needs to account for data limitations: While new experimental techniques enabl e recordings from thousands of neurons, experimental time is limited so that one ca n sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural d ata is the estimation of the individual receptive field locations - a problem that h as been scratched only at the surface thus far. W e propose a CNN architecture with a s parse readout layer factorizing the spatial (where) and feature (what) dimensi ons. Our network scales well to thousands of neurons and short recordings and can be t rained end-to-end. W e evaluate this architecture on ground-truth data to explo re the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system ide ntification models of mouse primary visual cortex.

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.

Neural Information Processing Systems http://nips.cc/

Adaptive stimulus selection methods in neuroscience have primarily focused on maximizing the firing rate of a single recorded neuron.