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1. Velocity-Controlled Oscillators for Hippocampal Navigation on Spiking Neuromorphic Hardware

   https://doi.org/10.1109/ISCAS.2019.8702579  

   Cellon, Adam B.; Eisape, Adebayo A.; Furuta, Masanori; Etienne-Cummings, Ralph (May 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS))

   Grid, place, and border cells in the mammalian hippocampus and entorhinal cortex perform highly sophisticated navigational tasks with an extremely low power budget. While previous algorithms for simultaneous localization and mapping (SLAM) in robotics have used these cells for inspiration, they have sacrificed the robust, low-power gains achieved with bioplausible models for ease of implementation. This paper presents steps towards robotic navigation with biologically realistic hippocampal models by implementing velocity-controlled oscillators, a basis for any spatially-tuned neuron, on mixed-mode neuromorphic spiking hardware. 

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2. Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

   https://doi.org/10.1177/2398212820972871  

   Alexander, Andrew S.; Robinson, Jennifer C.; Dannenberg, Holger; Kinsky, Nathaniel R.; Levy, Samuel J.; Mau, William; Chapman, G. William; Sullivan, David W.; Hasselmo, Michael E. (January 2020, Brain and Neuroscience Advances)

   null (Ed.)

   Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories. 

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3. Dynamic representation of 3D auditory space in the midbrain of the free-flying echolocating bat

   https://doi.org/DOI: https://doi.org/10.7554/eLife.29053  

   Kothari, N.B.; Wohlgemuth, M.J.; Moss, C.F. (April 2018, eLife)

   Essential to spatial orientation in the natural environment is a dynamic representation of direction and distance to objects. Despite the importance of 3D spatial localization to parse objects in the environment and to guide movement, most neurophysiological investigations of sensory mapping have been limited to studies of restrained subjects, tested with 2D, artificial stimuli. Here, we show for the first time that sensory neurons in the midbrain superior colliculus (SC) of the free-flying echolocating bat encode 3D egocentric space, and that the bat’s inspection of objects in the physical environment sharpens tuning of single neurons, and shifts peak responses to represent closer distances. These findings emerged from wireless neural recordings in free-flying bats, in combination with an echo model that computes the animal’s instantaneous stimulus space. Our research reveals dynamic 3D space coding in a freely moving mammal engaged in a real-world navigation task. 

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4. Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration

   https://doi.org/10.1609/aaai.v33i01.33018034  

   Liu, Kai; Wang, Hua; Han, Fei; Zhang, Hao (July 2019, Proceedings of the AAAI Conference on Artificial Intelligence)

   Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications. 

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5. An Event-driven Neuromorphic System with Biologically Plausible Temporal Dynamics

   https://doi.org/10.1109/ICCAD45719.2019.8942083  

   Fang, Haowen; Shrestha, Amar; Zhao, Ziyi; Li, Yilan; Qiu, Qinru (November 2019, IEEE/ACM International Conference on Computer-Aided Design (ICCAD))

   Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there is an increasing demand to process streams of temporal data on embedded devices with limited energy and power resources. Among all potential solutions, neuromorphic computing with spiking neural networks (SNN) that mimic the behavior of brain, have recently been placed at the forefront. Encoding information into sparse and distributed spike events enables low-power implementations, and the complex spatial temporal dynamics of synapses and neurons enable SNNs to detect temporal pattern. However, most existing hardware SNN implementations use simplified neuron and synapse models ignoring synapse dynamic, which is critical for temporal pattern detection and other applications that require temporal dynamics. To adopt a more realistic synapse model in neuromorphic platform its significant computation overhead must be addressed. In this work, we propose an FPGA-based SNN with biologically realistic neuron and synapse for temporal information processing. An encoding scheme to convert continuous real-valued information into sparse spike events is presented. The event-driven implementation of synapse dynamic model and its hardware design that is optimized to exploit the sparsity are also presented. Finally, we train the SNN on various temporal pattern-learning tasks and evaluate its performance and efficiency as compared to rate-based models and artificial neural networks on different embedded platforms. Experiments show that our work can achieve 10X speed up and 196X gains in energy efficiency compared with GPU. 

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