Periodic Reporting for period 1 - SpikeZip (SpikeZip: a retinomorphic encoding method for low-latency and loss-less data compression and telemetry of large-scale in-vivo neural recordings)
Período documentado: 2023-11-01 hasta 2025-04-30
SpikeZIP aims to develop and validate a proof of concept prototype that perform low-loss compression on the neural data. There are three key objectives performed in three work packages
1. PoC hardware and software building:
- To design and fabricate a proof-of-concept hardware (HW) prototype capable of acquiring, compressing, transmission and reception high-channel-count neural signals using the SpikeZip encoding algorithm.
- To develop and implement a real-time software (SW) algorithm for reconstructing and analysing the signal quality of the retinomorphic encoded extracellular neural data.
- To optimize the hardware and software for small form factor, low-power consumption, low-latency data transfer, and compatibility with existing neural recording systems and PC.
2. validation with in-vivo neural recording
- To validate the SpikeZip encoding algorithm using in vivo neural recordings in mice models.
- To analyze the fidelity and compression performance of the SpikeZip-encoded neural data compared to traditional methods.
- To investigate the effect of SpikeZip encoding on waveform features of neural signals for spike sorting.
3. business development
- To develop a comprehensive business plan for the commercialization of the SpikeZip technology. This includes defining the target market, identifying potential customers, and developing a strategy for intellectual property protection. The ultimate goal of this work package is to create a roadmap for bringing the SpikeZip technology to market and maximizing its impact.
1. PoC hardware and software building:
- To design and fabricate a proof-of-concept hardware (HW) prototype capable of acquiring, compressing, transmission and reception high-channel-count neural signals using the SpikeZip encoding algorithm.
- To develop and implement a real-time software (SW) algorithm for reconstructing and analysing the signal quality of the retinomorphic encoded extracellular neural data.
- To optimize the hardware and software for small form factor, low-power consumption, low-latency data transfer, and compatibility with existing neural recording systems and PC.
2. validation with in-vivo neural recording
- To validate the SpikeZip encoding algorithm using in vivo neural recordings in mice models.
- To analyze the fidelity and compression performance of the SpikeZip-encoded neural data compared to traditional methods.
- To investigate the effect of SpikeZip encoding on waveform features of neural signals for spike sorting.
3. business development
- To develop a comprehensive business plan for the commercialization of the SpikeZip technology. This includes defining the target market, identifying potential customers, and developing a strategy for intellectual property protection. The ultimate goal of this work package is to create a roadmap for bringing the SpikeZip technology to market and maximizing its impact.
PoC hardware and software building (WP1)
The following activities have been performed
- Completion of spikeZip and spikeUnzip hardware design
- Demonstrated targeted compression ratio and reconstruction quality with the HW and SW using both synthetic data and in-vivo recording data.
Key achievements: the PoC prototype HW and SW demonstrates a compression ratio >7x, and a low compression error below 7 micro-volts RMS (well below the typical noise floor of the sensor readout front-end)
Validation with in-vivo neural recording (WP2)
- Record in-vivo data from mice with strong artifact as stress test data, and demonstrate targeted compression and reconstruction error
- Demonstrate the analysis result with widely used spike sorting tool, Kilosort, and identify the influence from the compression.
Key achievements: an in-vivo recording from mice with strong licking artifact is recorded, and is used as input data of the compression. The spike sorting analysis shows that >97% of the spikes from the original synthetic data with ground truth can be recovered with a correlation coefficient >0.95. This shows the compression method proposed in SpikeZip has very low information loss.
The following activities have been performed
- Completion of spikeZip and spikeUnzip hardware design
- Demonstrated targeted compression ratio and reconstruction quality with the HW and SW using both synthetic data and in-vivo recording data.
Key achievements: the PoC prototype HW and SW demonstrates a compression ratio >7x, and a low compression error below 7 micro-volts RMS (well below the typical noise floor of the sensor readout front-end)
Validation with in-vivo neural recording (WP2)
- Record in-vivo data from mice with strong artifact as stress test data, and demonstrate targeted compression and reconstruction error
- Demonstrate the analysis result with widely used spike sorting tool, Kilosort, and identify the influence from the compression.
Key achievements: an in-vivo recording from mice with strong licking artifact is recorded, and is used as input data of the compression. The spike sorting analysis shows that >97% of the spikes from the original synthetic data with ground truth can be recovered with a correlation coefficient >0.95. This shows the compression method proposed in SpikeZip has very low information loss.
Overview of the results:
- SpikeZIP compression and encoding hardware module
- SpikeZIP decoding hardware (clock and data recovery) and software reconstruction module
- Preclinical in-vivo neural recording dataset from two area of mice brain, based on 2x Neuropixels v2 probe (up to 768 channels)
- Preclinical in-vivo neural recording dataset with licking artifact
- IPR: 3 patents being related to retinomorphic compression being filed
Potential impacts:
- economical impacts:
- societal impacts: improve the precision of the BCI and neuroprosthesis to enhance patients' quality of life (from grab cup to tying shoelace)
Needs for further uptake:
- to achieve the impacts, following funding will be required to further increase TRL (preclinical in-vivo validation) and the development of MVP that fits to the market need
- SpikeZIP compression and encoding hardware module
- SpikeZIP decoding hardware (clock and data recovery) and software reconstruction module
- Preclinical in-vivo neural recording dataset from two area of mice brain, based on 2x Neuropixels v2 probe (up to 768 channels)
- Preclinical in-vivo neural recording dataset with licking artifact
- IPR: 3 patents being related to retinomorphic compression being filed
Potential impacts:
- economical impacts:
- societal impacts: improve the precision of the BCI and neuroprosthesis to enhance patients' quality of life (from grab cup to tying shoelace)
Needs for further uptake:
- to achieve the impacts, following funding will be required to further increase TRL (preclinical in-vivo validation) and the development of MVP that fits to the market need