Velocity-Controlled Oscillators for Hippocampal Navigation on Spiking Neuromorphic Hardware

Cellon, Adam B.; Eisape, Adebayo; Furuta, Mamoru; Etienne‐Cummings, Ralph

doi:10.1109/iscas.2019.8702579

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…In the actual scenario however, we understand that the offset frequency F off cannot be eliminated all the way to zero, so the phase shift computation must be adjusted as in (4) due to the remaining offset frequency, even after offset reduction. The modulo operation is to account for digitized phase shift in an implemented system, such as a ring oscillator described in the next section that offers 8 digitized phase shifts, or a neural circuit model of theta cell in the form of a ring oscillator [20,23]…”

Section: Improved Oi Model With Theta Cell Variationsmentioning

confidence: 99%

“…the theta cells, poses difficulties as well. Our team's previous work has shown that it would be difficult to maintain the oscillation's stability while maintaining its sensitivity to velocity inputs due to its complex ring attractor structure [20]. To amend this problem, we designed a theta chip that employs the principle of abstract neuromorphism to implement the theta cells' behavior model by exploiting the simplicity of analog computation circuitries.…”

Section: Introductionmentioning

confidence: 99%

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Neuromorphic place cells

Chen,

Nasrallah,

Etienne-Cummings

2024

Neuromorph. Comput. Eng.

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A neuromorphic SLAM system shows potential for more efficient implementation than its traditional counterpart. At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic between each group of cells. Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents. The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from as much as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells. We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation. We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells.

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Section: Improved Oi Model With Theta Cell Variationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuromorphic place cells

Chen,

Nasrallah,

Etienne-Cummings

2024

Neuromorph. Comput. Eng.

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“…We also assume that a hardware-emulated theta cell is likely to output square waves rather than sinusoids. In addition, quantized phase shifts are adopted here to better emulate both the neural circuitry and hardware realization of theta cell as ring attractor [29,30] or oscillator. The AND interference operation can then be made analogous to the multiplication of two sinusoids, with the following form…”

Section: Multiplicative Interference and A Constructive Interference ...mentioning

confidence: 99%

Neuromorphic model of hippocampus place cells using an oscillatory interference technique for hardware implementation

Chen

Nasrallah

Alemohammad

et al. 2022

Neuromorph. Comput. Eng.

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In this paper, we propose a simplified and robust model for Place cell generation based on the Oscillatory Interference (OI) model concept. Aiming toward hardware implementation in bio-inspired Simultaneous Localization and Mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent Theta cells, and allows the Theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the Theta cell’s base oscillation frequency and gain—as a function of modulatory inputs—is demonstrated. Place cell composed of 48 Theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM. Hence, we propose a model for spatial cell formation using Theta cells with behaviors that are biologically plausible and hardware implementable for real world application in neurally-inspired SLAM.

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“…We also assume that a hardware-emulated Theta cell is likely to output square waves rather than sinusoids. In addition, quantized phase shifts are adopted here to better emulate both the neural circuitry and hardware realization of Theta cell as ring attractor [29,30] or oscillator. The AND interference operation can then be made analogous to the multiplication of two sinusoids, with the following form cos(2𝜋𝐹𝑡) cos(2𝜋𝐹 idle 𝑡)…”

Section: Multiplicative Interference and A Constructive Interference ...mentioning

confidence: 99%

Section: Oscillatory Interference Models Under Ideal Conditionsmentioning

confidence: 99%

Neuromorphic Model of Hippocampus Place cells using an Oscillatory Interference Technique for Hardware Implementation

Chen

Nasrallah

Alemohammad

et al. 2022

Preprint

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In this paper, we propose a simplified and robust model for Place cell generation based on the Oscillatory Interference (OI) model concept. Aiming toward hardware implementation in bio-inspired Simultaneous Localization and Mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent Theta cells, and allows the Theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the Theta cell's base oscillation frequency and gain as a function of modulatory inputs is demonstrated. Place cell composed of 48 Theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM.

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Velocity-Controlled Oscillators for Hippocampal Navigation on Spiking Neuromorphic Hardware

Cited by 5 publications

References 26 publications

Neuromorphic place cells

Neuromorphic place cells

Neuromorphic model of hippocampus place cells using an oscillatory interference technique for hardware implementation

Neuromorphic Model of Hippocampus Place cells using an Oscillatory Interference Technique for Hardware Implementation

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