Keynotes

Insect navigation has been a focus of behavioural study for many years, and provides a striking example of cognitive complexity in a miniature brain. We have used computational modelling to bridge the gap from behaviour to neural mechansims by relating the computational requirements of navigational tasks to the type of computation offered by invertebrate brain circuits.

We have shown that visual memory of multiple views could be acquired by associative learning in the mushroom body neuropil, and allow insects to recapitulate long routes. We have also proposed a circuit in the central complex neuropil that integrates sky compass and optic flow information on an outbound path and can thus steer the animal directly home; moreover this circuit can be used for additional vector calculations such as finding novel shortcuts. The models are strongly constrained by neuroanatomy, and are tested in realistic agent and robot simulations.

The talk discusses briefly current challenges in AI, including: efficient learning of data (interactive, adaptive, life-long; transfer); interpretability and explainability; personalised predictive modelling and profiling; multiple modality of data (e.g. genetic, clinical, behaviour, cognitive, static, temporal, longitudinal); computational complexity; energy consumption; human-machine interaction.  

Opportunities to address these challenges are presented through advancement in Neuroinformatics, Neural networks and Neurocomputers (the 3N).  Neuroinformatics offer a tremendous amount of data and knowledge about how the human brain and the nervous system work. Many brain information processing principles can be now implemented in novel Neural network computational models, such as:  sparseness of computation, leading to a much less computational complexity and a significant energy consumption; diversity in the NN architecture in terms of type of neurons and compartmentalisation of computations, which can improve results; cognitive computation, where bottom-up sensory information and top-down prior knowledge are used to  speeds-up the learning process; life-long and transfer learning; interactive and reinforcement learning (rather than batch-mode); self-organisation (rather than pre-defined number of layers and neurons); evolving spatio-temporal knowledge and many more.  Some of these principles have already been used in neural network models, such as SOM (Kohonen), ART (Grossberg), ECOS (^[1,2]), spiking neural networks (SNN) (Maass), ^[3]. The latter ones have inspired the development of neuromorphic hardware chips and Neurocomputers, characterised by much low power consumption, massive parallelism and fast processing.

Perception and Neurodynamics Laboratory
Ohio State University

As the most widely-used spatial filtering approach for multi-channel signal separation, beamforming extracts the target signal arriving from a specific direction. We present an emerging approach based on multi-channel complex spectral mapping, which trains a deep neural network (DNN) to directly estimate the real and imaginary spectrograms of the target signal from those of the multi-channel noisy mixture. In this all-neural approach, the trained DNN itself becomes a nonlinear, time-varying spectrospatial filter. How does this conceptually simple approach perform relative to commonly-used beamforming techniques on different array configurations and in different acoustic environments? We examine this issue systematically on speech dereverberation, speech enhancement, and speaker separation tasks. Comprehensive evaluations show that multi-channel complex spectral mapping achieves speech separation performance comparable to or better than beamforming for different array geometries, and reduces to monaural complex spectral mapping in single-channel conditions, demonstrating the versatility of this new approach for multi-channel and single-channel speech separation. In addition, such an approach is computationally more efficient than popular mask-based beamforming. We conclude that this neural spectrospatial filter is capable of superseding traditional and mask-based beamforming.