Novel integration and packaging concepts of highly miniaturized inductively powered neural implants

This work proposes solutions to the current bulky packaged neural implants. We describe the next generation of miniaturized wirelessly powered neural interface that are distributed and free floating in the nervous system. This paper focuses on the microassembly, hermetic packaging and its effect on the inductive power link.

A CMOS Current Steering Neurostimulation Array With Integrated DAC Calibration and Charge Balancing

An 8-channel current steerable, multi-phasic neural stimulator with on-chip current DAC calibration and residue nulling for precise charge balancing is presented. Each channel consists of two sub-binary radix DACs followed by wide-swing, high output impedance current buffers providing time-multiplexed source and sink outputs for anodic and cathodic stimulation. A single integrator is shared among channels and serves to calibrate DAC coefficients and to closely match the anodic and cathodic stimulation phases. Following calibration, the differential non-linearity is within ±0.3 LSB at 8-bit resolution, and the two stimulation phases are matched within 0.3%. Individual control in digital programming of stimulation coefficients across the array allows altering the spatial profile of current stimulation for selection of stimulation targets by current steering. Combined with the self-calibration and current matching functions, the current steering capabilities integrated on-chip support use in fully implanted neural interfaces with autonomous operation for and adaptive stimulation under variations in electrode and tissue conditions. As a proof-of-concept we applied current steering stimulation through a multi-channel cuff electrode on the sciatic nerve of a rat.

A Bidirectional Neural Interface IC With Chopper Stabilized BioADC Array and Charge Balanced Stimulator

We present a bidirectional neural interface with a 4-channel biopotential analog-to-digital converter (bioADC) and a 4-channel current-mode stimulator in 180 nm CMOS. The bioADC directly transduces microvolt biopotentials into a digital representation without a voltage-amplification stage. Each bioADC channel comprises a continuous-time first-order ∆Σ modulator with a chopper-stabilized OTA input and current feedback, followed by a second-order comb-filter decimator with programmable oversampling ratio. Each stimulator channel contains two independent digital-to-analog converters for anodic and cathodic current generation. A shared calibration circuit matches the amplitude of the anodic and cathodic currents for charge balancing. Powered from a 1.5 V supply, the analog and digital circuits in each recording channel draw on average [Formula: see text] and [Formula: see text] of supply current, respectively. The bioADCs achieve an SNR of [Formula: see text] and a SFDR of [Formula: see text] , for better than 9-b ENOB. Intracranial EEG recordings from an anesthetized rat are shown and compared to simultaneous recordings from a commercial reference system to validate performance in-vivo . Additionally, we demonstrate bidirectional operation by recording cardiac modulation induced through vagus nerve stimulation, and closed-loop control of cardiac rhythm. The micropower operation, direct digital readout, and integration of electrical stimulation circuits make this interface ideally suited for closed-loop neuromodulation applications.

Intraspinal microstimulation produces over-ground walking in anesthetized cats

OBJECTIVE

Spinal cord injury causes a drastic loss of motor, sensory and autonomic function. The goal of this project was to investigate the use of intraspinal microstimulation (ISMS) for producing long distances of walking over ground. ISMS is an electrical stimulation method developed for restoring motor function by activating spinal networks below the level of an injury. It produces movements of the legs by stimulating the ventral horn of the lumbar enlargement using fine penetrating electrodes (≤50 μm diameter).

APPROACH

In each of five adult cats (4.2-5.5 kg), ISMS was applied through 16 electrodes implanted with tips targeting lamina IX in the ventral horn bilaterally. A desktop system implemented a physiologically-based control strategy that delivered different stimulation patterns through groups of electrodes to evoke walking movements with appropriate limb kinematics and forces corresponding to swing and stance. Each cat walked over an instrumented 2.9 m walkway and limb kinematics and forces were recorded.

MAIN RESULTS

Both propulsive and supportive forces were required for over-ground walking. Cumulative walking distances ranging from 609 to 835 m (longest tested) were achieved in three animals. In these three cats, the mean peak supportive force was 3.5 ± 0.6 N corresponding to full-weight-support of the hind legs, while the angular range of the hip, knee, and ankle joints were 23.1 ± 2.0°, 29.1 ± 0.2°, and 60.3 ± 5.2°, respectively. To further demonstrate the viability of ISMS for future clinical use, a prototype implantable module was successfully implemented in a subset of trials and produced comparable walking performance.

SIGNIFICANCE

By activating inherent locomotor networks within the lumbosacral spinal cord, ISMS was capable of producing bilaterally coordinated and functional over-ground walking with current amplitudes <100 μA. These exciting results suggest that ISMS may be an effective intervention for restoring functional walking after spinal cord injury.

A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface

Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

A Mixed-Signal VLSI System for Producing Temporally Adapting Intraspinal Microstimulation Patterns for Locomotion

Neural pathways can be artificially activated through the use of electrical stimulation. For individuals with a spinal cord injury, intraspinal microstimulation, using electrical currents on the order of 125 μ A, can produce muscle contractions and joint torques in the lower extremities suitable for restoring walking. The work presented here demonstrates an integrated circuit implementing a state-based control strategy where sensory feedback and intrinsic feed forward control shape the stimulation waveforms produced on-chip. Fabricated in a 0.5 μ m process, the device was successfully used in vivo to produce walking movements in a model of spinal cord injury. This work represents progress towards an implantable solution to be used for restoring walking in individuals with spinal cord injuries.

Compact all-CMOS spatiotemporal compressive sensing video camera with pixel-wise coded exposure

We present a low power all-CMOS implementation of temporal compressive sensing with pixel-wise coded exposure. This image sensor can increase video pixel resolution and frame rate simultaneously while reducing data readout speed. Compared to previous architectures, this system modulates pixel exposure at the individual photo-diode electronically without external optical components. Thus, the system provides reduction in size and power compare to previous optics based implementations. The prototype image sensor (127 × 90 pixels) can reconstruct 100 fps videos from coded images sampled at 5 fps. With 20× reduction in readout speed, our CMOS image sensor only consumes 14μW to provide 100 fps videos.

HFirst: A Temporal Approach to Object Recognition

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% ± 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% ± 1.9% for a new more difficult 36 class character recognition task.

A closed-loop compressive-sensing-based neural recording system

OBJECTIVE

This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device.

APPROACH

The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases.

MAIN RESULTS

Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode.

SIGNIFICANCE

Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

Energy-efficient multi-mode compressed sensing system for implantable neural recordings

Widely utilized in the field of Neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored. Built upon our previous on-chip CS implementation, we propose an energy efficient multi-mode CS framework that focuses on improving the off-chip components, including (i) a two-stage sensing strategy, (ii) a sparsifying dictionary directly using data, (iii) enhanced compression performance from Full Signal CS mode and Spike Restoration mode to Spike CS + Restoration mode and; (iv) extension of our framework to the Tetrode CS recovery using joint sparsity. This new framework achieves energy efficiency, implementation simplicity and system flexibility simultaneously. Extensive experiments are performed on simulation and real datasets. For our Spike CS + Restoration mode, we achieve a compression ratio of 6% with a reconstruction SNDR > 10 dB and a classification accuracy > 95% for synthetic datasets. For real datasets, we get a 10% compression ratio with  ∼  10 dB for Spike CS + Restoration mode.

An efficient and compact compressed sensing microsystem for implantable neural recordings

Multi-Electrode Arrays (MEA) have been widely used in neuroscience experiments. However, the reduction of their wireless transmission power consumption remains a major challenge. To resolve this challenge, an efficient on-chip signal compression method is essential. In this paper, we first introduce a signal-dependent Compressed Sensing (CS) approach that outperforms previous works in terms of compression rate and reconstruction quality. Using a publicly available database, our simulation results show that the proposed system is able to achieve a signal compression rate of 8 to 16 while guaranteeing almost perfect spike classification rate. Finally, we demonstrate power consumption measurements and area estimation of a test structure implemented using TSMC 0.18 μm process. We estimate the proposed system would occupy an area of around 200 μm ×300 μm per recording channel, and consumes 0.27 μ W operating at 20 KHz .

A model of proto-object based saliency

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, however, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Prevention and diagnosis of retained foreign bodies through the years: past, present, and future technologies

Post operative retained foreign bodies are a rare but recalcitrant problem. We detail reports of interventions over the last two centuries and review the most current interventions using automated data identity capture and computer aided detection. This was one of earliest areas in which multidisciplinary collaboration was achieved in patient safety. This multidisciplinary collaboration was unique because most other initiatives had been internal: among the disciplines working in the OR i.e. surgeons, nurses and anesthesiologists; this collaboration, to achieve optimal patient safety at that point in time was between surgeons and radiologists to ensure a lack of post operative retained foreign bodies.

A miniaturized platform for laser speckle contrast imaging

Imaging the brain in animal models enables scientists to unravel new biological insights. Despite critical advancements in recent years, most laboratory imaging techniques comprise of bulky bench top apparatus that require the imaged animals to be anesthetized and immobilized. Thus, animals are imaged in their non-native state severely restricting the scope of behavioral experiments. To address this gap, we report a miniaturized microscope that can be mounted on a rat's head for imaging in awake and unrestrained conditions. The microscope uses laser speckle contrast imaging (LSCI), a high resolution yet wide field imaging modality for imaging blood vessels and perfusion. Design details of both the image formation and acquisition modules are presented. A Monte Carlo simulation was used to estimate the depth of tissue penetration achievable by the imaging system while the produced speckle Airy disc patterns were simulated using Fresnel's diffraction theory. The microscope system weighs only 7 g and occupies less than 5 cm³ and was successfully used to generate proof of concept LSCI images of rat brain vasculature. We validated the utility of the head-mountable system in an awake rat brain model by confirming no impairment to the rat's native behavior.

Sponges and incorrect sponge count: Minor contributions to the process of detecting retained foreign bodies

Background: Postoperative retained foreign bodies [RFBs] can be a serious event, but they are rare. The x-ray is the current gold standard to detect RFBs. There has been scant research on the process of detection as opposed to the consequence of RFBs. Surgical sponges incorporating automatic data identity capture technology (radiofrequency tags, barcodes) have been proposed to detect RFBs. Because resources in healthcare are scarce, careful consideration needs to be given to developing the right technology in order to maximize the process of RFB elimination. There have been few studies that identify factors contributing to the process of RFB detection. Study design: Our goal was to determine the frequency with which x-rays were ordered to detect abdominal surgery post operative RFBs and the indications for ordering them. We reviewed the Johns Hopkins Hospital's Department of Radiology database to retrospectively study the demographic and radiologic data on patients who underwent exploratory surgery for RFBs following abdominal procedures performed between April 2004 and April 2008. Results: Of the 13,335 portable abdominal x-rays taken during the period, 203 (1.5%) were ordered to assess patients for the presence of an RFB. Of these, 57 (28%) were taken because no RFB count was made (e.g., for emergency procedures), 57 (28%) were taken per procedure or protocol, 51 (25%) were taken because of an incorrect instrument count, and 39 (19%) were taken because of an incorrect sponge count. Of the 203 x-rays, 192 (95%) were negative for RFBs, 11 (5%) were positive or had suspicious findings, and of these 3 (2%) revealed more than 1 RFB. The 11 patients with positive or suspicious findings underwent exploratory procedures immediately during the same operation; of these, 8 (72%) actually had an RFB and 3 (28%) had a negative result at exploration. Conclusion: Multiple pathways lead to the decision to obtain X-rays for RFBs, of which sponges/Incorrect sponge counts make up only one in five. Therefore, technology that focuses on sponges alone may not majorly impact clinical outcome because x-rays will still be required in the majority of cases of suspected high risk.

Parameter estimation of a spiking silicon neuron

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model's output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron's parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron's output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron's parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed.

Feed forward and feedback control for over-ground locomotion in anaesthetized cats

The biological central pattern generator (CPG) integrates open and closed loop control to produce over-ground walking. The goal of this study was to develop a physiologically based algorithm capable of mimicking the biological system to control multiple joints in the lower extremities for producing over-ground walking. The algorithm used state-based models of the step cycle each of which produced different stimulation patterns. Two configurations were implemented to restore over-ground walking in five adult anaesthetized cats using intramuscular stimulation (IMS) of the main hip, knee and ankle flexor and extensor muscles in the hind limbs. An open loop controller relied only on intrinsic timing while a hybrid-CPG controller added sensory feedback from force plates (representing limb loading), and accelerometers and gyroscopes (representing limb position). Stimulation applied to hind limb muscles caused extension or flexion in the hips, knees and ankles. A total of 113 walking trials were obtained across all experiments. Of these, 74 were successful in which the cats traversed 75% of the 3.5 m over-ground walkway. In these trials, the average peak step length decreased from 24.9 ± 8.4 to 21.8 ± 7.5 (normalized units) and the median number of steps per trial increased from 7 (Q1 = 6, Q3 = 9) to 9 (8, 11) with the hybrid-CPG controller. Moreover, within these trials, the hybrid-CPG controller produced more successful steps (step length ≤ 20 cm; ground reaction force ≥ 12.5% body weight) than the open loop controller: 372 of 544 steps (68%) versus 65 of 134 steps (49%), respectively. This supports our previous preliminary findings, and affirms that physiologically based hybrid-CPG approaches produce more successful stepping than open loop controllers. The algorithm provides the foundation for a neural prosthetic controller and a framework to implement more detailed control of locomotion in the future.

Sensor and display human factors based design constraints for head mounted and tele-operation systems

For mobile imaging systems in head mounted displays and tele-operation systems it is important to maximize the amount of visual information transmitted to the human visual system without exceeding its input capacity. This paper aims to describe the design constraints on the imager and display systems of head mounted devices and tele-operated systems based upon the capabilities of the human visual system. We also present the experimental results of methods to improve the amount of visual information conveyed to a user when trying to display a high dynamic range image on a low dynamic range display.

Modified map-seeking circuit: use of computer-aided detection in locating postoperative retained foreign bodies

BACKGROUND

More than 98% of intra-operative X-rays taken to search for postoperative retained foreign bodies (RFBs) have negative findings; in over 30% of cases of such X-rays, the finding is a false negative. Newer technologies created to find RFBs must not only reduce the false-negative rate, but also must not increase the burden of detecting RFBs. We have introduced the use of computer-aided detection (CAD) to facilitate the detection of RFBs on X-rays utilizing a modified version of map-seeking circuit (MSC) algorithm the referenced map-seeking circuit (RMSC), for our proof-of-concept study for detection of needles in plain abdominal X-rays.

METHODS

Images were obtained by using a portable cassette-based X-ray machine and a C-arm (digital) machine, both of which are commonly used in the operating room. The images obtained using these machines were divided into subimages of approximately 250 × 250 pixels each, for a total of 455 subimages from the cassette-based machine (A) and 365 from the digital machine (B) for use as test samples. Images obtained from A and B were analyzed separately using our modified MSC algorithm with a minimum (τ = 0) and a maximum threshold (τ = 0.5).

RESULTS

The automated detection rate (positive predictive value) was 86%, with a false positive/negative rate of 10% to 15% when τ was zero.

CONCLUSION

The CAD-based RMSC algorithm has the potential to improve the accuracy with which RFBs can be found in X-rays. Further research is needed to optimize the detection rate and to identify a wider range of RFBs.

Silicon modeling of the Mihalaş-Niebur neuron

There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model--a generalized model of the leaky integrate-and-fire neuron with adaptive threshold--that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties--tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability--are demonstrated in this model.

A CMOS In-Pixel CTIA High Sensitivity Fluorescence Imager

Traditionally, charge coupled device (CCD) based image sensors have held sway over the field of biomedical imaging. Complementary metal oxide semiconductor (CMOS) based imagers so far lack sensitivity leading to poor low-light imaging. Certain applications including our work on animal-mountable systems for imaging in awake and unrestrained rodents require the high sensitivity and image quality of CCDs and the low power consumption, flexibility and compactness of CMOS imagers. We present a 132×124 high sensitivity imager array with a 20.1 μm pixel pitch fabricated in a standard 0.5 μ CMOS process. The chip incorporates n-well/p-sub photodiodes, capacitive transimpedance amplifier (CTIA) based in-pixel amplification, pixel scanners and delta differencing circuits. The 5-transistor all-nMOS pixel interfaces with peripheral pMOS transistors for column-parallel CTIA. At 70 fps, the array has a minimum detectable signal of 4 nW/cm(2) at a wavelength of 450 nm while consuming 718 μA from a 3.3 V supply. Peak signal to noise ratio (SNR) was 44 dB at an incident intensity of 1 μW/cm(2). Implementing 4×4 binning allowed the frame rate to be increased to 675 fps. Alternately, sensitivity could be increased to detect about 0.8 nW/cm(2) while maintaining 70 fps. The chip was used to image single cell fluorescence at 28 fps with an average SNR of 32 dB. For comparison, a cooled CCD camera imaged the same cell at 20 fps with an average SNR of 33.2 dB under the same illumination while consuming over a watt.

Estimating parameters of generalized integrate-and-fire neurons from the maximum likelihood of spike trains

When a neuronal spike train is observed, what can we deduce from it about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate-and-fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that, at least in principle, its unique global minimum can thus be found by gradient descent techniques. Many biological neurons are, however, known to generate a richer repertoire of spiking behaviors than can be explained in a simple integrate-and-fire model. For instance, such a model retains only an implicit (through spike-induced currents), not an explicit, memory of its input; an example of a physiological situation that cannot be explained is the absence of firing if the input current is increased very slowly. Therefore, we use an expanded model (Mihalas & Niebur, 2009 ), which is capable of generating a large number of complex firing patterns while still being linear. Linearity is important because it maintains the distribution of the random variables and still allows maximum likelihood methods to be used. In this study, we show that although convexity of the negative log-likelihood function is not guaranteed for this model, the minimum of this function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) usually reaches the global minimum.

Neuromorphic silicon neuron circuits

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.