A rotational motion perception neural network based on asymmetric spatiotemporal visual information processing

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing

On the potential role of lateral connectivity in retinal anticipation

We analyse the potential effects of lateral connectivity (amacrine cells and gap junctions) on motion anticipation in the retina. Our main result is that lateral connectivity can-under conditions analysed in the paper-trigger a wave of activity enhancing the anticipation mechanism provided by local gain control [8, 17]. We illustrate these predictions by two examples studied in the experimental literature: differential motion sensitive cells [1] and direction sensitive cells where direction sensitivity is inherited from asymmetry in gap junctions connectivity [73]. We finally present reconstructions of retinal responses to 2D visual inputs to assess the ability of our model to anticipate motion in the case of three different 2D stimuli

Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks

Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks

Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains