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An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications

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An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 26, NO. 1, JANUARY 2018 233 An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications Boyuan Yan and Sheila Nirenberg Abstract— Optogenetics offers a powerful new approach temporal resolution. Since these proteins require bright for controlling neural circuits. It has numerous applica- light [23], [24], standard high resolution devices such as LCD tions in both basic and clinical science. These applications monitors are ineffective. While laser or LED-based illumi- require stimulating devices with small processors that can perform real-time neural signal processing, deliver high- nation systems can meet the requirements of light intensity, intensity light with high spatial and temporal resolution, they are not readily suitable for providing spatially-patterned and do not consume a lot of power. In this paper, we stimuli. Digital light processing (DLP) projectors, however demonstrate the implementation of neuronal models in a can achieve this: the key component, a chip called Digital platform consisting of an embedded system module and Micromirror Device (DMD) [25], consists of an array of a portable digital light processing projector. As a replace- ment for damaged neural circuitry, the embedded module several hundred thousand micromirrors, which can change processes neural signals and then directs the projector to position at kHz frequencies. These offer the most flexibility in optogenetically activate a downstream neural pathway. We terms of the spatial and temporal modulation of the activating present a design in the context of stimulating circuits in the light. visual system, but the approach is feasible for a broad range In addition to an effective stimulator, the other essential of biomedical applications. component is an efficient processor to mimic the processing of Index Terms— Embedded processor, neural signal the neural pathway being bypassed. To replace the function of processing, neural pathway modeling, optogenetic the bypassed system, processors are required to simulate neural stimulation, digital light processing, neuroprosthesis. processing and signaling in real time. To achieve sufficient spatial and temporal resolution, it may involve the simulation I. INTRODUCTION of a large number of model cells at a temporal scale that is PTOGENETICS allows neurons to be selectively turned on the order of milliseconds, which is computationally inten- Oon or off with unprecedented precision [1]–[5]. This sive [26]–[28]. Besides the real-time computing constraint, the offers great opportunities for clinical applications because it processor also needs to be small and consume less power provides a way to activate or inactivate specific neurons or so that the prosthetic device can be portable and energy classes of neurons in a malfunctioning or damaged circuit and efficient (so batteries last long enough for the device to be re-engage them into normal activity. As such, the development useful). These requirements make embedded processors with of optogenetic tools offers new hope for patients suffering low power consumption, small size, and low cost a perfect from neurological disorders [6] such as epilepsy [7]–[9] candidate. Parkinson’s disease [10], [11], hearing impairments [12], or In this paper, we demonstrate the implementation of neu- vision impairments [13]–[22]. ronal models in a platform consisting of an embedded system The basic strategy of an optogenetic prosthesis is to bypass module and a portable DLP projector for high spatial and a damaged neural pathway and provide direct stimulation to temporal resolution optogenetic stimulation. The platform is downstream components in the pathway. To implement the described in the context of driving visual circuits: briefly, the strategy in a device, two components are needed: a processor, embedded module takes visual input (data from a camera), which processes neural signals in real time, to mimic the passes it through an array of model neurons to produce function of the pathway being bypassed, and a stimulator to firing patterns that are similar to the patterns produced by optogenetically activate the downstream cells. the targeted downstream cells. The DLP projector then opto- From the stimulator point of view, it is necessary genetically actives the targeted downstream cells to fire in to target optogenetic proteins with precise spatial and these same patterns. To allow processing in real time, models Manuscript received February 7, 2017; revised July 29, 2017 and are simulated with a heterogeneous architecture composed September 25, 2017; accepted October 2, 2017. Date of publication of a general-purpose processor (GPP) and a digital signal October 16, 2017; date of current version January 8, 2018. This work was processor (DSP). Although presented in the context of visual supported in part by the National Eye Institute under Grant R01 EY12978 and in part by the Department of Defense under Grant W81XWH-12-1- systems, the platform can be easily adapted for a broad range 0231. (Corresponding author: Sheila Nirenberg.) of biomedical applications. The authors are with the Department of Physiology and Biophysics, The paper is organized as follows: In Section II, we Weill Cornell Medicine, Cornell University, New York City, NY 10021 USA (e-mail: [email protected]; [email protected]). describe our strategy for implementing a neuronal model in Digital Object Identifier 10.1109/TNSRE.2017.2763130 a digital signal processor. In Section III and Section IV, the 1534-4320 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 234 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 26, NO. 1, JANUARY 2018 hardware platform and software design are presented, respec- time model. For many practical applications, the inputs are tively. In Section V, an illustrative example is provided to typically represented by discrete-time stimulus sequences demonstrate the capability of the platform. Section VI gives a (e.g, a sequence of frames from camera), and the storage of detailed discussion on the applicability and limitations. any time function in a computer or digital signal processor can only be as a discrete function. Therefore, a discrete-time II. MODEL system model is often needed from the implementation point In this section, we describe our implementation of compact of view. neuronal models in digital signal processors. 1) Difference Equation: Discrete-time systems are described by difference equations. In general, a Qth order discrete- A. Firing-Rate Model time system with a number of P inputs is described by the A variety of neuronal models have been proposed to mimic input/output difference equation the processing performed by neurons in the nervous system. Q P M In this paper, we use the classic firing-rate model as an I[k]=− aq I[k − q]+ bpmu p[k − m], (4) example (reviewed in [29]). This model [29] is described in q=1 p=1 m=0 a network setting, where both inputs and outputs are charac- where k is the integer-valued discrete-time index, u p is the pth terized by firing rates. Given a neuron receiving P synaptic input, I is the output, and a and b are constant coefficients. = , ,..., q pm inputs labeled by i 1 2 P, the synaptic current entering The equation shows a Qth order recursion process, i.e., the ( ) the soma I t in terms of the firing rates of the presynaptic next value of the output is computed from the Q previous neurons is determined by the following first-order differential values of the output and the P(M + 1) values of the input at equation the cost of O(Q + P(M + 1)). There is a trade-off between P accuracy and simplicity. Models with a larger Q and M dI(t) τ =−I (t) + w u (t − d ), (1) typically provide a more accurate description of the responses dt p p p p=1 characteristics but they are also more expensive to calculate. The time interval between any pair of consecutive sample where τ denotes the time constant, u p denotes the firing rate times is the signal’s sampling period, Ts, and the sampling of the pth input, and dp and wp denote the corresponding axonal conduction delay and synaptic weight, respectively. rate Fs is the reciprocal of the sampling period ( ) λ( ) Given I t , the firing rate of postsynaptic neuron t can Fs = 1/Ts. (5) be expressed as 2) Spike Generation: For an inhomogeneous Poisson λ(t) = F(I (t) + I0), (2) process, the probability of observing exactly n spikes in a particular interval is given by (3). where F is called an activation function, and I is a constant 0 For sufficiently small intervals , the average number of offset which sets the baseline firing rate of the model. spikes can be approximated by n=λ. In addition, the Firing rate models can be used to generate stochastic spike interval can be reduced until the probability of occurrence sequences from a deterministically computed rate. A spike of more than one spike in the interval is sufficiently small train is typically described by an inhomogeneous Poisson that it can be ignored. In this case, the probability of a spike process, which involves a time-dependent firing rate λ(t) [29]. occurring in a brief time interval is equal to the product of the If n is the observed number of spikes during a given time instantaneous firing rate during that interval and the length of interval , the probability of such an event is given by a the interval Poisson distribution (λ(t))n P(n = 1|λ) = λ. (6) P(n|λ(t)) = exp(−λ(t)). ! (3) n Therefore, in order to generate spikes, we need to divide The Poisson process provides a useful approximation of sto- the sampling periods (e.g., frame periods), which are typically chastic neuronal firing, and we show how we use it to generate large (e.g., Ts = 16.7ms, 33.3ms, 66.7ms for Fs = 60Hz, Poissonspikestrainsbelow.
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