Building on advances in brain-computer interface technology and computational biology, the ReNaChip project lays the groundwork for the use of a prosthetic chip to recover function to nervous system deficiencies.
|Figure 1: Schematic of the closed-loop ReNaChip approach.
The use of biomimetic implants that mimic the biological world to replace lost pathways in the brain provides a tremendous opportunity to develop innovative therapy for neurodegenerative disease and injury. Supported by the EC Seventh Framework programme for future and emerging technologies, the ReNaChip project aims to develop and test the feasibility of a novel approach to behavioural rehabilitation in an aging model: a synthetic biomimetic chip is interfaced with the brain to complete a well-defined neuronal circuit rendered dysfunctional by the aging process.
Biohybrids and the brain-computer interface
As our understanding of how the brain works increases and advances are made in computing and electronics, there is increasing interest in the integration of biology and technology. The ability to provide direct communication between the nervous system and an artificial device is being used to restore damaged hearing (cochelar implants), sight (retinal implants) and movement (neuroprosthetics).
Deep brain stimulation (DBS), whereby the brain is stimulated directly by im-planted electrodes, has been shown to be effective in ameliorating the symptoms of Parkinson’s disease, chronic pain, tremor and dystonia. However, this method for replacing lost brain function is not applicable to all neurological disease, as it lacks the required specificity to manipulate local, defined circuits within the brain.
The ReNaChip approach
Recent developments in brain computer interface (BCI) and in computational biology have led to innovations in the rehabilitation of central nervous system deficiency beyond the direct DBS approach. We believe that the use of biomimetic implants provides a tremendous opportunity to recover function to a range of nervous system deficiencies. Our aim is to create a synthetic biomimetic model of the brain microcircuit associated with the motor eye-blink learning response and to implement this in a microchip. The device will be integrated with an animal model to create a biohybrid in which a lost motor function is replaced (Figure 1).
The cerebellar microcircuit
The motor eye-blink learning response is an example of a conditioned behaviour that takes place in a location in the brain called the cerebellum. Through repeated exposure to a noise or tone (a conditioned stimulus or CS) followed by an air puff to the eye (an unconditioned stimulus or US), an animal will learn to predict the unconditioned stimulus when it hears the tone and will close the eye in response to the noise (a conditioned response or CR). The ability to learn this behaviour is lost during the aging process. While the performance of the cerebellum is compromised, the inputs and outputs of the responsible microcircuit remain functional. Replacing the deficient part of the neural pathway with a biomimetic implant integrated with the brain should recover the lost function (i.e., the learning response).
To achieve the goal of the project, however, we need to make progress on related component technologies:
- development of effective physiological recording strategies
- robust detection of the stimulation signals from the data
- delivery of input to a neuromorphic chip that includes the cerebellar model.
We must develop a cerebellar model that can mimic the damaged microcircuit and learn from the stimuli the appropriate conditioned response output that will interface with the biology and bring about the motor eye-blink response. The current status of each of these challenges is discussed below.
The success of our technological solution is dependent upon our understanding of the underlying biological system. Much effort has been spent describing the cerebellar microcircuit and its modulation by other regions of the brain.
Tools have been developed to optimise the quality of the recorded physiological data. Silicon microelectrode arrays have been developed with precise positioning of the sensing pads that correspond to the specific brain anatomy with which we are concerned. Algorithmic methods are in place to select the appropriate timing of the stimulus delivery to facilitate learning and will be used to support signal detection.
These activities have supported development of the experimental paradigms required for the practical implementation of the biohybrid, such as simultaneous recordings from multiple sites within the brain.
The interface between the biology and technology takes place in signal detection. The detection of the onset and timing of the CS and US must be extracted from the physiological data in real time and be provided to the biomimetic chip. The vast amount of data present in the brain makes this challenging. We are developing two approaches to this problem: determining the upper limit of information that can be extracted from the data, and developing a model-based signal detection methodology that complies with the constraints of the model and its hardware implementation.
The cerebellar model and hardware implementation
|Figure 2: Aggregation boards for interfacing the electrodes to the physiological recording system and the device that provides the stimulus to action the eye-blink response are pictured.
The output of successful signal processing is the input to the synthetic cerebellar model. The details of cerebellar anatomy allow reliable bottom-up modelling of cerebellar architecture. By contrast, the physiology of the cerebellum allows top-down modelling of learning. A biologically constrained model was previously described1 and has been further refined in this project.
The CS and US identified from the physiological data are fed into the synthetic model, which must respond with a learning of the conditioned response and provide an output leading to stimulation of the biohybrid’s facial nerve to bring about an eye-blink response. For the first time, we have demonstrated the practical real-time bidirectional coupling between the physiological data and the synthetic system. The cerebellar model has been shown to fully substitute the function of the biological cerebellar microcircuit in eye-blink conditioning and acquire the learning response.
Our task now is to implement signal detection and the cerebellar model in hardware form. An abstract version of the model in field programmable gate array (FPGA) chip form has demonstrated positive results. Current activity is focused on implementation in an aVLSI chip, compatible with physiological recording methods.
The integration of the hardware form of the model with the biology is supported by a systems-integration approach that has developed the appropriate tools to effectively combine and control the different disciplines—interfacing the physiological recording system with the electrodes, integrating the signal processing and implementing the stimulation protocols (Figure 2).
The way forward
The next stage for the project is critical. The first series of integration experiments interfacing multiple components of the system has been carried out. Promising results have informed the planning of a large-scale demonstration test in which a “closed-loop” experiment will be conducted. This integration of the complete system in a biohybrid is the ultimate goal of the project as it moves into its final year. These results will put the project firmly on the path to demonstrating the ReNaChip concept and bringing the prospect of clinical therapy one step closer.
1. C. Hofstötter et al. “The Cerebellum Chip: an Analog VLSI Implementation of a Cerebellar Model of Classical Conditioning.” Advances in Neural Information Processing Systems, 577–584 (2005).
Angela Silmon, PhD
is ReNaChip Project Coordinator, Newcastle University, INEX, Herschel Building, Newcastle-Upon-Tyne NE1 7RU, UK
tel. +44 1912 223 500.