Scale Invariance in Lateral Head Scans During Spatial Exploration

Validation of a system for automatic quantitative analysis of laboratory mice behavior based on locomotor pose

Automatic recognition and accurate quantitative analysis of rodent behavior play an important role in brain neuroscience, pharmacological and toxicological. Currently, most behavior recognition systems used in experiments mainly focus on the indirect measurements of animal movement trajectories, while neglecting the changes of animal body pose that can indicate more psychological factors. Thus, this paper developed and validated an hourglass network-based behavioral quantification system (HNBQ), which uses a combination of body pose and movement parameters to quantify the activity of mice in an enclosed experimental chamber. In addition, The HNBQ was employed to record behavioral abnormalities of head scanning in the presence of food gradients in open field test (OFT). The results proved that the HNBQ in the new object recognition (NOR) experiment was highly correlated with the scores of manual observers during the latent exploration period and the cumulative exploration time. Moreover, in the OFT, HNBQ was able to capture the subtle differences in head scanning behavior of mice in the gradient experimental groups. Satisfactory results support that the combination of body pose and motor parameters can regard as a new alternative approach for quantification of animal behavior in laboratory.

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of low-footprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the NeuroSwarms control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5-12 Hz) sequence generation. We demonstrate that multi-agent swarming and reward-approach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.