Backpropagation of error (BP) is a widely used and highly successful learning algorithm. However, its reliance on non-local information in propagating error gradients makes it seem an unlikely candidate for learning in the brain. In the last decade, a number of investigations have been carried out focused upon determining whether alternative more biologically plausible computations can be used to approximate BP. This work builds on such a local learning algorithm -Gradient Adjusted Incremental Target Propagation (GAIT-prop) -which has recently been shown to approximate BP in a manner which appears biologically plausible. This method constructs local, layer-wise weight update targets in order to enable plausible credit assignment. However, in deep networks, the local weight updates computed by GAIT-prop can deviate from BP for a number of reasons. Here, we provide and test methods to overcome such sources of error. In particular, we adaptively rescale the locally-computed errors and show that this significantly increases the performance and stability of the GAIT-prop algorithm when applied to the CIFAR-10 dataset.Preprint. Under review.

In forensic investigations it is often of value to establish whether two phones were used by the same person during a given time period. We present a method that uses time and location of cell tower registrations of mobile phones to assess the strength of evidence that any pair of phones were used by the same person. The method is transparent as it uses logistic regression to discriminate between the hypotheses of same and different user, and a standard kernel density estimation to quantify the weight of evidence in terms of a likelihood ratio. We further add to previous theoretical work by training and validating our method on real world data, paving the way for application in practice. The method shows good performance under different modeling choices and robustness under lower quantity or quality of data. We discuss practical usage in court.

Retrieving information from memory can lead to forgetting of other, related information. The inhibition account of this retrieval-induced forgetting effect predicts that this form of forgetting occurs when competition arises between the practiced information and the related information, leading to inhibition of the related information. In the standard retrieval practice paradigm, a retrieval practice task is used in which participants retrieve the items based on a category-plus-stem cue (e.g., FRUIT-or___). In the current experiment, participants instead generated the target based on a cue in which the first 2 letters of the target were transposed (e.g., FRUIT-roange). This noncompetitive task also induced forgetting of unpracticed items from practiced categories. This finding is inconsistent with the inhibition account, which asserts that the forgetting effect depends on competitive retrieval. We argue that interference-based accounts of forgetting and the context-based account of retrieval-induced forgetting can account for this result.