I’ve build a prototype visual exploration tool for the connectome of c. elegans. The data describing the worm’s neural network is preprocessed from publicly available information and stored as a graph database in neo4j. The d3.js visualization then fetches either the whole network or a subgraph and displays it using a force-directed layout (for now).
My latest paper has just been published by Frontiers in Computational Neuroscience and can be accessed free of charge here. It concerns the question of whether we need internal models (simulations) in order to control our movements, or whether our body and the lower-level neural circuits innervating it provide some control “for free”.
From the abstract:
The dynamic interaction of limb segments during movements that involve multiple joints creates torques in one joint due to motion about another. Evidence shows that such interaction torques are taken into account during the planning or control of movement in humans. Two alternative hypotheses …
The ubiquity of mobile phones equipped with a wide range of sensors presents interesting opportunities for data mining applications. In this report we aim to find out whether data from accelerometers and gyroscopes can be used to identify physical activities performed by subjects wearing mobile phones on their wrist.
The data used in this analysis is based on the “Human activity recognition using smartphones” data set available from the UCL Machine Learning Repository [1]. A preprocessed version was downloaded from the Data Analysis online course [2]. The set contains data derived from 3-axial linear acceleration and 3-axial angular velocity …
Dash+ is a python web application I built with Flask, which imports Nike+ running data into a NoSQL database (MongoDB) and uses D3.js to visualize and analyze it.
The app is work in progress and primarily intended as a personal playground for exploring d3 visualization of my own running data. Having said that, if you want to fork your own version on github, simply add your Nike access token in the corresponding file.
In this report I will provide an overview of my solution to kaggle’s “Titanic” competition. The aim of this competition is to predict the survival of passengers aboard the titanic using information such as a passenger’s gender, age or socio-economic status. I will explain my data munging process, explore the available predictor variables, and compare a number of different classification algorithms in terms of their prediction performance. All analysis presented here was performed in R. The corresponding source code is available on github.
The data set provided by kaggle contains 1309 records of passengers aboard the …
Our new paper on sensorimotor contingencies is out. It tackles what seems like a paradox in the sensorimotor approach: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? We propose a Piagetian solution to this problem, according to which we learn to perceive by re-shaping pre-existing sensorimotor structures (the earliest of which are already present at birth) in coupling with dynamical regularities of the world.
We have published a new paper on sensorimotor contingencies (SMCs). It provides operational definitions for four different notions of SMCs that have not previously been distinguished. The paper illustrates these using a minimal cognition model and hypothesises about their link to personal-level concepts fundamental to the sensorimotor approach, such as the mastery of sensorimotor skills.
Frontiers in Cognition | A Dynamical Systems Account of Sensorimotor Contingencies
Science daily has covered our work on sensorimotor contingencies:
Perceive first, act afterwards. The architecture of most of today’s robots is underpinned by this control strategy. The eSMCs project has set itself the aim of changing the paradigm and generating more dynamic computer models in which action is not a mere consequence of perception but an integral part of the perception process. It is about improving robot behavior by means of perception models closer to those of humans…
Read the full article here: Science Daily: Robots that perceive the world like humans