Feedforward computational model for pattern recognition with spiking neurons

Humans and primates are remarkably good at pattern recognition and outperform the best machine vision systems with respect to almost any measure. Building a computational model that emulates the architecture and information processing in biological neural systems has always been an attractive target. To build a computational model that closely follows the information processing and architecture of the visual cortex, in this paper, we have improved the latency-phase encoding to express the external stimuli in a more abstract manner. Moreover, inspired by recent findings in the biological neural system, including architecture, encoding, and learning theories, we have proposed a feedforward computational model of spiking neurons that emulates object recognition of the visual cortex for pattern recognition. Simulation results showed that the proposed computational model can perform pattern recognition task well. In addition, the success of this computational model suggests a plausible proof for feedforward architecture of pattern recognition in the visual cortex

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin

Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competetive accuracy with respect to state-of-the-art non-spiking neural models.Comment: 13 figures (includes supplementary information

Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons