Neural Mechanism of Corticofugal Modulation of Tuning Property in Frequency Domain of Bat’s Auditory System

Dynamic relationship between noseleaf and pinnae in echolocating hipposiderid bats

Old World leaf-nosed bats (family Hipposideridae) can deform the shapes of their 'noseleaves' (i.e. ultrasonic emission baffles) and outer ears during echolocation behaviors. Prior work has shown that deformations on the emission as well as on the reception side can have an impact on the properties of the emitted/received sonar signals. The occurrence of the deformations on the emission and reception sides raises the question of whether the bats coordinate these two dynamic biosonar features to achieve synergistic effects. To address this question, simultaneous three-dimensional reconstructions of the trajectories of landmarks on the dynamic noseleaf and pinna geometries have been obtained in great roundleaf bats (Hipposideros pratti). These joint kinematics data on the noseleaf and pinnae have shown both qualitative and quantitative relationships between the noseleaf and pinna motions: large noseleaf deformations (opening or closing) tended to be associated with non-rigid pinna motions. Furthermore, closing deformations of the noseleaves tended to co-occur with closing motions of the pinna. Finally, a canonical correlation analysis of the motion trajectories has revealed a tight correlation between the motions of the landmarks on the noseleaf and both pinnae. These results demonstrate that the biosonar system of hipposiderid bats includes coordinated emission and reception dynamics.

An Analytical Comparison of Locally-Connected Reconfigurable Neural Network Architectures Using a C. elegans Locomotive Model

The scale of modern neural networks is growing rapidly, with direct hardware implementations providing significant speed and energy improvements over their software counterparts. However, these hardware implementations frequently assume global connectivity between neurons and thus suffer from communication bottlenecks. Such issues are not found in biological neural networks. It should therefore be possible to develop new architectures to reduce the dependence on global communications by considering the connectivity of biological networks. This paper introduces two reconfigurable locally-connected architectures for implementing biologically inspired neural networks in real time. Both proposed architectures are validated using the segmented locomotive model of the C. elegans, performing a demonstration of forwards, backwards serpentine motion and coiling behaviours. Local connectivity is discovered to offer up to a 17.5× speed improvement over hybrid systems that use combinations of local and global infrastructure. Furthermore, the concept of locality of connections is considered in more detail, highlighting the importance of dimensionality when designing neuromorphic architectures. Convolutional Neural Networks are shown to map poorly to locally connected architectures despite their apparent local structure, and both the locality and dimensionality of new neural processing systems is demonstrated as a critical component for matching the function and efficiency seen in biological networks.