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Autopilot v0.4.0 - Distributing development of a distributed experimental framework
Autopilot is a Python framework for performing complex behavioral neuroscience experiments by coordinating a swarm of Raspberry Pis. It was designed to not only give researchers a tool that allows them to perform the hardware-intensive experiments necessary for the next generation of naturalistic neuroscientific observation, but also to make it easier for scientists to be good stewards of the human knowledge project. Specifically, we designed Autopilot as a framework that lets its users contribute their technical expertise to a cumulative library of hardware interfaces and experimental designs, and produce data that is clean at the time of acquisition to lower barriers to open scientific practices. As autopilot matures, we have been progressively making these aspirations a reality. Currently we are preparing the release of Autopilot v0.4.0, which will include a new plugin system and wiki that makes use of semantic web technology to make a technical and contextual knowledge repository. By combining human readable text and semantic annotations in a wiki that makes contribution as easy as possible, we intend to make a communal knowledge system that gives a mechanism for sharing the contextual technical knowledge that is always excluded from methods sections, but is nonetheless necessary to perform cutting-edge experiments. By integrating it with Autopilot, we hope to make a first of its kind system that allows researchers to fluidly blend technical knowledge and open source hardware designs with the software necessary to use them. Reciprocally, we also hope that this system will support a kind of deep provenance that makes abstract "custom apparatus" statements in methods sections obsolete, allowing the scientific community to losslessly and effortlessly trace a dataset back to the code and hardware designs needed to replicate it. I will describe the basic architecture of Autopilot, recent work on its community contribution ecosystem, and the vision for the future of its development.
Sparse expansion in cerebellum favours learning speed and performance in the context of motor control
The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk will show that the expansion to the granule cell layer in the cerebellar cortex improves learning speed and performance in the context of motor control by considering a cerebellar-like network learning an internal model of a motor apparatus online. By expressing the general form of the learning rate for such a system, this talk will provide a calculation of how increasing the number of granule cells diminishes the effect of noise and increases the learning speed. The researchers propose that the particular architecture of cerebellar circuits modifies the geometry of the error function in a favourable way for learning faster. Their results illuminate a new link between cerebellar structure and function.
Swimming in the third domain: archaeal extremophiles
Archaea have evolved to survive in some of the most extreme environments on earth. Life in extreme, nutrient-poor conditions gives the opportunity to probe fundamental energy limitations on movement and response to stimuli, two essential markers of living systems. Here we use three-dimensional holographic microscopy and computer simulations to show that halophilic archaea achieve chemotaxis with power requirements one hundred-fold lower than common eubacterial model systems. Their swimming direction is stabilised by their flagella (archaella), enhancing directional persistence in a manner similar to that displayed by eubacteria, albeit with a different motility apparatus. Our experiments and simulations reveal that the cells are capable of slow but deterministic chemotaxis up a chemical gradient, in a biased random walk at the thermodynamic limit.