iPROBE: in-vivo Platform for the Real-time Observation of Brain Extracellular activity

See Resources/Spike Sorting Platform

Completed Project (2013-2016)

Research Team:  Song Luan, Ian Williams, Lieuwe Leene, Yan Liu, Timothy Constandinou
Collaborators: Andrew Jackson (Newcastle), Kenneth Harris (UCL), Gyorgy Buzsaki (NYU)
Funding: Engineering and Physical Sciences Research Council (EPSRC) EP/K015060/1

iPROBE, a digital 1k+ channel neural recording interface for neuroscience research
iPROBE, a digital 1k+ channel neural recording interface for neuroscience research

Understanding how the trillions of action potentials of the brain's billions of neurons produce our thoughts, perceptions, and actions is one of the greatest challenges of 21st-century science. Similarly, understanding how this activity is disrupted by neurological and psychiatric diseases is one of the greatest challenges of 21st-century medicine.

Due to the massively parallel nature of the brain's computations, answering these questions experimentally relies on being able to monitor very large numbers of neurons simultaneously.

Advances in electrode microfabrication and high-throughput data analysis have allowed scientists to record from hundreds of neurons in a small local area of the brain. However, as both healthy and unhealthy neural operation arises from the interaction of multiple, widely distributed brain circuits, its understanding requires a technological step-change that allows the monitoring of much larger numbers of neurons over many brain areas.

Large-scale neuronal recording relies on the use of microfabricated multielectrode arrays (MEAs). Arrays capable of recording from hundreds of local neurons are now commercially available. In principle, these arrays provide the ability to record from thousands of neurons across multiple brain structures, simply by using a large number of probes simultaneously. However, accessing the data produced by these electrodes cannot be achieved with current technologies, as it is simply impossible to pass a sufficient number of very low amplitude analogue signals, as in current passive connection systems.

iPROBE addresses this by leveraging communication protocols used commonly in high-performance computing. Allowing simple, robust, and low-noise connection of several multi-electrode arrays, will allow us to monitor thousands of neurons from multiple probe structures using a single communication interface.

iPROBE exploits cheap, commercially-available microelectrode arrays (eg. NeuroNexus), connected to a custom CMOS Integrated Circuit (IC) via high-density flexible ribbon cables. CMOS ICs are low-cost and produce high-yield and area-efficient active electronics suitable for amplifying, filtering, analog-to-digital conversion, and encoding of each electrode array's spiking neuron data. Each group of serially connected probes will terminate into a standard USB interface. The new USB-3.0 protocol (marketed using the SuperSpeed term) can allow for serial data speeds of 5Gbps. For data sampled at 25kS/sec at 12-bit resolution, this could provide a bandwidth capable of supporting over 10,000 electrodes: two orders of magnitude beyond current technology.

Publications

Publications

2018

  • M. Maslik, Y. Liu, T. S. Lande, and T. G. Constandinou, “Continuous-time acquisition of biosignals using a charge-based ADC topology,” IEEE Transactions in Biomedical Circuits and Systems, vol. 12, no. 3, pp. 471–482, 2018. doi: https://doi.org/10.1109/TBCAS.2018.2817180
  • Y. Liu, J. a. Pereira, and T. G. Constandinou, “Event-driven processing for hardware-efficient neural spike sorting,” Journal of Neural Engineering, vol. 15, no. 1, pp. 1–14, 2018. doi: https://doi.org/10.1088/1741-2552/aa9124

2017

  • L. Leene and T. G. Constandinou, “A 0.016 mm2 12b ∆ΣSAR with 14fJ/conv. for ultra low power biosensor arrays,” IEEE Transactions in Circuits and Systems I: Regular Papers, vol. 64, no. 10, pp. 2655–2665, 2017. doi: https://doi.org/10.1109/TCSI.2017.2703580
  • Y. Liu, S. Luan, I. Williams, and T. G. Constandinou, “A 64-channel versatile neural recording SoC with activity dependant data throughput,” IEEE Transactions in Biomedical Circuits and Systems, vol. 11, no. 6, pp. 1344–1355, 2017. doi: https://doi.org/10.1109/TBCAS.2017.2759339

2016

  • L. Leene and T. G. Constandinou, “A 2.7 μW/Mips, 0.88 GOPS/mm2 distributed processor for implantable brain machine interfaces,” in IEEE Biomedical Circuits and Systems (BioCAS) Conference, pp. 360–363, 2016. doi: https://doi.org/10.1109/BioCAS.2016.7833806
  • biosensor arrays,” IEEE Transactions in Circuits and Systems I: Regular Papers, vol. 64, no. 10, pp. 2655–2665, 2017. doi: https://doi.org/10.1109/TCSI.2017.2703580
  • Y. Liu, S. Luan, I. Williams, and T. G. Constandinou, “A 64-channel versatile neural recording SoC with activity dependant data throughput,” IEEE Transactions in Biomedical Circuits and Systems, vol. 11, no. 6, pp. 1344–1355, 2017. doi: https://doi.org/10.1109/TBCAS.2017.2759339
  • L. Leene and T. G. Constandinou, “A 2.7 μW/Mips, 0.88 GOPS/mm2 distributed processor for implantable brain machine interfaces,” in IEEE Biomedical Circuits and Systems (BioCAS) Conference, pp. 360–363, 2016. doi: https://doi.org/10.1109/BioCAS.2016.7833806
  • M. Elia, L. Leene, and T. G. Constandinou, “Continuous-time micropower interface for neural recording applications,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 534–537, 2016. doi: https://doi.org/10.1109/ISCAS.2016.7527295
  • Y. Liu, J. a. Pereira, and T. G. Constandinou, “Clockless continuous-time neural spike sorting: Method, implementation and evaluation,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 538–541, 2016. doi: https://doi.org/10.1109/ISCAS.2016.7527296
  • M. Elia, L. Leene, and T. G. Constandinou, “Continuous-time micropower interface for neural recording applications,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 534–537, 2016. doi: https://doi.org/10.1109/ISCAS.2016.7527295
  • Y. Liu, J. a. Pereira, and T. G. Constandinou, “Clockless continuous-time neural spike sorting: Method, implementation and evaluation,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 538–541, 2016. doi: https://doi.org/10.1109/ISCAS.2016.7527296

2015

  • K. Faliagkas, L. Leene, and T. G. Constandinou, “A novel neural recording system utilising continuous time energy based compression,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 2015. doi: https://doi.org/10.1109/ISCAS.2015.7169318
  • K. Faliagkas, L. Leene, and T. G. Constandinou, “A novel neural recording system utilising continuous time energy based compression,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 2015. doi: https://doi.org/10.1109/ISCAS.2015.7169318

2014

  • L. Leene and T. G. Constandinou, “An ultra low power design strategy for two stage amplifier topologies,” IET Electronics Letters, vol. 50, pp. 583–585, 2014. doi: https://doi.org/10.1049/el.2013.4196
  • L. Leene and T. G. Constandinou, “An ultra low power design strategy for two stage amplifier topologies,” IET Electronics Letters, vol. 50, pp. 583–585, 2014. doi: https://doi.org/10.1049/el.2013.4196

2013

  • L. Leene, Y. Liu, and T. G. Constandinou, “A compact recording array for neural interfaces,” in Proc. IEEE Biomedical Circuits and Systems (BioCAS) Conference, 2013. doi: https://doi.org/10.1109/BioCAS.2013.6679648