A fundamental Challenge in neuroscience is to understand the elemental computations and algorithms by which brains perform information processing. This is of great significance to biologists, as well as, to engineers and computer scientists, who aim at developing energy efficient and intelligent solutions for the next generation of computers and autonomous devices. The benefits of collaborations between these fields are reciprocal, as brain-inspired computational algorithms and devices not only advance engineering, but also assist neuroscientists by conforming their models and making novel predictions. A large impediment toward such an efficient interaction is still the complexity of brains. We thus propose that the study of small model organisms should pioneer these efforts.

The nematode worm, C. elegans, provides a ready experimental system for reverse-engineering the nervous system, being one of the best studied animals in the life sciences. The neural connectome of C. elegans has been known for 30 years, providing the structural basis for building models of its neural information processing. Despite its small size, C. elegans exhibits complex behaviors, such as, locating food, mating partners and navigating its environment by integrating a plethora of environmental cues. Over the past years, the field has made an enormous progress in understanding some of the neural circuits that control sensory processing, decision making and locomotion. In laboratory, the crawling behavior of worms occurs mainly in 2D. This enables the use of machine learning tools to obtain quantitative behavioral descriptions of unprecedented accuracy. Moreover, neuronal imaging techniques have been developed so that the activity of nearly all nerve cells in the brain can be recorded in real time. Leveraging on these advancements, the community wide C. elegans OpenWorm project will make a realistic in silico simulation of a nervous system and the behavior it produces possible, for the first time. 

The goal of this workshop is to gather researchers in neuroscience and machine learning together, to advance understanding of the neural information processing of the worm and to outline what challenges still lie ahead. We particularly aim to:
- Comprehensively, introduce the nervous system of C. elegans. We will discuss the state-of-the-art findings and potential future solutions for modeling its neurons and synapses, complete networks of neurons and the various behaviors of the worm,
- Identify main challenges and their solutions in behavioral and neural data extraction, such as imaging techniques, generation of time series data from calcium imaging records and high resolution behavioral data, as well as cell recognition, cell tracking and image segmentation,
- Explore machine learning techniques for interpretation of brain data, such as time series analysis, feature extraction methods, complex network analysis, complex nonlinear systems analysis, large-scale parameter optimization methods, and representation learning,
- Get inspirations from this well-understood brain to design novel network architectures, control algorithms and neural processing units.
We have invited leading neuroscientists, machine learning scientists and interdisciplinary experts, to address these main objectives of the workshop, in the form of Keynote talks and a panel discussion. We also invite submissions of 4-page papers for posters, spotlight presentations and contributed talks, and offer travel awards.

Topics of interests are: Deep learning applications in nervous system data analysis, neural circuits analysis, behavior modeling, novel computational approaches and algorithms for brain data interpretations, brain simulation platforms, optimization algorithms for nonlinear systems, applications of machine learning methods to brain data and cell biology, complex network analysis, cell modeling, cell recognition and tracking, dynamic modeling of neural circuits and genetic regulatory networks.

The workshop’s webpage: https://sites.google.com/site/wwnip2017/