Implementation of biohybrid olfactory bulb on a high-density CMOS-chip to reveal large-scale spatiotemporal circuit information

Hu, Xin; Khanzada, Shahrukh; Klütsch, Diana; Calegari, Federico; Amin, Hayder

doi:10.1016/j.bios.2021.113834

Cited by 18 publications

(50 citation statements)

References 68 publications

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“…To estimate the mesoscale functional connectivity and measure the information flow and its direction within the correlated links in the circuitry, we employed multivariate Granger causality and directed transfer function (DTF) 11 . The ENR network showed both higher unidirectional (i.e., DG→CA3, CA1, EC, PC) and bidirectional (i.e., CA3↔CA3, CA1↔CA1, DG↔DG, Hilus↔Hilus, EC↔EC, and PC↔PC) interaction links compared to the SD network (Figures 1g-i) .…”

Section: Resultsmentioning

confidence: 99%

“…Next, we identified the topographic propagation delay of the spatiotemporal firing patterns by computing the time-delay between sequential occurring events in each hippo-cortical subregions, which yielded greater firing probability and higher synchrony in the ENR compared to the SD networks ( Figure 5d ). We then analyzed the spatiotemporal propagation patterns of the recorded events with an algorithm based on the center of activity trajectories (CATs) 11 and classified the CATs of network-wide activity with an unsupervised machine learning algorithm (see method). Thereby, we identified three categories of propagation pathways: 1) hippocampus to entorhinal/perirhinal-cortex, 2) intra-hippocampal circuits mediated by the recurrent network, and 3) entorhinal-cortex to hippocampus corresponding to the classical unidirectional tri-synaptic pathway ( Figures 5e-l ).…”

Section: Resultsmentioning

confidence: 99%

“…HD neurochips are composed of 4096 recording electrodes with a 42 μm pitch size to compose an active sensing area of ~7 mm 2 , ideal for recording entire hippocampal-parahippocampal regions. The on-chip amplification circuit allows for 0.1–5 kHz band-pass filtering conferred by a global gain of 60 dB sufficient to record slow and fast oscillations 11 . Slices were moved and coupled onto the neurochips using a custom-made platinum harp placed above the tissue.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the power distribution of the LFPs and the dominant frequencies within an oscillatory event, we constructed a frequency-time dynamic in pseudo-color spectrograms for a selected time window using filtered LFPs (1-100 Hz). To estimate the PSD, we used Welch’s method as previously described 11 by calculating the Fast Fourier Transform (FFT) of the detected LFPs.…”

Section: Methodsmentioning

confidence: 99%

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Rich Experience Boosts Functional Connectome and High-Dimensional Coding in Hippocampal Network

Emery

Khanzada

et al. 2022

Preprint

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Challenging the brain with experiential richness creates tissue-level changes and synaptic plasticity, but the interjacent network level has not been accessible. We here show that environmental enrichment has unexpectedly far-reaching effects on network connectivity and multi-dimensional coding in the hippocampus. We present direct evidence that experience impacts local and global network connectivity, synchrony, and rhythmic dynamics. For this, we investigated the hippocampi from standard-housed mice (SD) and mice living in an enriched environment (ENR) using large-scale ex vivo recordings with a high-density microelectrode sensing array that with the unprecedented spatiotemporal resolution allowed simultaneous electrophysiological assessment across the entire circuit. In the absence of extrinsic electrical network stimulation, we found enhanced functional connectivity and high-dimensional coding in hippocampal-cortical networks of ENR mice. The mapped connectome illustrated a scale-free small-world topology and an ENR-induced resilience to random failures. ENR enhanced large-scale spatiotemporal firing patterns, which facilitated efficient pattern separation and boosted the information encoded in the firing phases of slow oscillatory rhythms. Given that essentially all electrophysiological studies on network behaviors have been done on animals housed in stimulus-poor conditions, our SD mice showed the expected normal functionality. The literature consequently underestimates the extent of spontaneous network activity and connectivity under truly physiological conditions. Our results pave the way to unveil fundamental mechanisms of experience-dependent enhancement in the hippocampal network underlying high brain functions and provide markers for large-scale network remodeling and metaplasticity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Rich Experience Boosts Functional Connectome and High-Dimensional Coding in Hippocampal Network

Emery

Khanzada

et al. 2022

Preprint

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“…Other noteworthy studies in neuromorphic olfaction include the rank-order-based latency coding [16,17], hardware-based olfactory models based on the antennal lobe of fruit fly [18][19][20], a VLSI implementation of an SNN based on the neurophysiological architecture of a rodent olfactory bulb [21], hardware implementation of the olfactory bulb model [22], a classifier using a convolutional spiking neural network [23], a 3D SNN reservoir-based classifier for odor recognition [24], and the columnar olfactory bulb model inspired by the glomerular layer of the mammalian olfactory pathway that was recently extended for its implementation on Loihi, Intel's neuromorphic research chip [14,25]. However, most of the research in neuromorphic olfaction, such as [15,21,[26][27][28][29][30], is more driven towards implementing a high level of bio-realism to emulate the biological olfactory pathway, which results in impractical models with limited scope for real-world applications [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Vanarse¹,

Osseiran²,

Rassau

et al. 2022

Sensors

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Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

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Neuronal network-based biomimetic chip for long-term detection of olfactory dysfunction model in early-stage Alzheimer's disease

Liu

Chen

Gao

et al. 2022

Biosensors and Bioelectronics

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Implementation of biohybrid olfactory bulb on a high-density CMOS-chip to reveal large-scale spatiotemporal circuit information

Cited by 18 publications

References 68 publications

Rich Experience Boosts Functional Connectome and High-Dimensional Coding in Hippocampal Network

Rich Experience Boosts Functional Connectome and High-Dimensional Coding in Hippocampal Network

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Neuronal network-based biomimetic chip for long-term detection of olfactory dysfunction model in early-stage Alzheimer's disease

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