Yansong Chua — Page 2

arXiv2018

Classifying neuromorphic data using a deep learning framework for image classification

In the field of artificial intelligence, neuromorphic computing has been around for several decades. Deep learning has however made much recent progress such that it consistently outperforms neuromorphic learning algorithms in classification tasks in terms of accuracy. Specifically in the field of image classification, neuromorphic computing has been traditionally using either the temporal or rate code for encoding static images in datasets into spike trains. It is only till recently, that neuromorphic vision sensors are widely used by the neuromorphic research community, and provides an alternative to such encoding methods. Since then, several neuromorphic datasets as obtained by applying such sensors on image datasets (e.g. the neuromorphic CALTECH 101) have been introduced. These data are encoded in spike trains and hence seem ideal for benchmarking of neuromorphic learning algorithms. Specifically, we train a deep learning framework used for image classification on the CALTECH 101 and a collapsed version of the neuromorphic CALTECH 101 datasets. We obtained an accuracy of 91.66% and 78.01% for the CALTECH 101 and neuromorphic CALTECH 101 datasets respectively. For CALTECH 101, our accuracy is close to the best reported accuracy, while for neuromorphic CALTECH 101, it outperforms the last best reported accuracy by over 10%. This raises the question of the suitability of such datasets as benchmarks for neuromorphic learning algorithms.

cs.CV↗

arXiv2017

Neurogenesis and multiple plasticity mechanisms enhance associative memory retrieval in a spiking network model of the hippocampus

Hippocampal CA3 is crucial for the formation of long-term associative memory. It has a heavily recurrent connectivity, and memories are thought to be stored as memory engrams in the CA3. However, despite its importance for memory storage and retrieval, spiking network models of the CA3 to date are relatively small-scale, and exist as only proof-of-concept models. Specifically, how neurogenesis in the dentate gyrus affects memory encoding and retrieval in the CA3 is not studied in such spiking models. Our work is the first to develop a biologically plausible spiking neural network model of hippocampal memory encoding and retrieval, with at least an order-of-magnitude more neurons than previous models. It is also the first to investigate the effect of neurogenesis on CA3 memory encoding and retrieval. Using such a model, we first show that a recently developed plasticity model is crucial for good encoding and retrieval. Next, we show how neural properties related to neurogenesis and neuronal death enhance storage and retrieval of associative memories in the CA3. In particular, we show that without neurogenesis, increasing number of CA3 neurons are recruited by each new memory stimulus, resulting in a corresponding increase in inhibition and poor memory retrieval as more memories are encoded. Neurogenesis, on the other hand, maintains the number of CA3 neurons recruited per stimulus, and enables the retrieval of recent memories, while forgetting the older ones. Our model suggests that structural plasticity (provided by neurogenesis and apoptosis) is required in the hippocampus for memory encoding and retrieval when the network is overloaded; synaptic plasticity alone does not suffice. The above results are obtained from an exhaustive study in the different plasticity models and network parameters.

q-bio.NC↗