Sensory Systems/Neurosensory Implants/Retinal Implants

Retinal Implants
Since the late 20th century, restoring vision to blind people by means of artificial eye prostheses has been the goal of numerous research groups and some private companies around the world. Similar to cochlear implants, the key concept is to stimulate the visual nervous system with electric pulses, bypassing the damaged or degenerated photoreceptors on the human retina. In this chapter we will describe the basic functionality of a retinal implant, as well as the different approaches that are currently being investigated and developed. The two most common approaches to retinal implants are called “epiretinal” and “subretinal” implants, corresponding to eye prostheses located either on top or behind the retina respectively. We will not cover any non-retina related approaches to restoring vision, such as the BrainPort Vision System that aims at stimulating the tongue from visual input, cuff electrodes around the optic nerve, or stimulation implants in the primary visual cortex.

Retinal Structure and Functionality
Figure 1 depicts the schematic nervous structure of the human retina. We can differentiate between three layers of cells. The first, located furthest away from the eye lens, consists of the photoreceptors (rods and cones) whose purpose is to transduce incoming light into electrical signals that are then further propagated to the intermediate layer, which is mainly composed of bipolar cells. These bipolar cells, which are connected to photoreceptors as well as cell types such as horizontal cells and amacrine cells, pass on the electrical signal to the retinal ganglion cells (RGC). For a detailed description on the functionality of bipolar cells, specifically with respect to their subdivision into ON- and OFF-bipolar cells, refer to chapter on Visual Systems. The uppermost layer, consisting of RGCs, collects the electric pulses from the horizontal cells and passes them on to the thalamus via the optic nerve. From there, signals are propagated to the primary visual cortex. There are some key aspects worth mentioning about the signal processing within the human retina. First, while bipolar cells, as well as horizontal and amacrine, generate graded potentials, the RGCs generate action potentials instead. Further, the density of each cell type is not uniform across the retina. While there is an extremely high density of rods and cones in the area of the fovea, with in addition only very few photoreceptors connected to RGCs via the intermediate layer, a far lower density of photoreceptors is found in the peripheral areas of the retina with many photoreceptors connected to a single RGC. The latter also has direct implications on the receptive field of a RGC, as it tends to increase rapidly towards the outer regions of the retina, simply because of the lower photoreceptor density and the increased number of photoreceptors being connected to the same RGC.



Implant Use Case: Retinal Degenerative Diseases
As mentioned previously in this wiki, the retina is a light-sensitive tissue located in the back of the eye consisting of different layers which contain a variety of cell types. The retina is primarily involved in neural visual processing with signals originating at photoreceptors and travelling to the brain by the axons of the ganglion cells. When this stratified tissue degenerates, permanent vision loss can occur. This is often caused by retinal degenerative diseases such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP), which are the two most prevalent conditions that progressively lead to permanent visual impairments and loss. Currently, there are no cures for these two retinal diseases and with modern therapies only having the capacity to slow down disease progression, strategies are needed to restore patients’ vision. One of the tools currently being investigated is retinal prosthesis technology that stimulates viable retina tissue to reinstate vision, which will be described in a later section.

Age-Related Macular Degeneration (AMD)
As suggested by its name, macular degeneration is a retinal degenerative disease with an onset occurring primarily in elderly individuals. AMD revolves around the progressive degeneration of cone photoreceptors in the macula, leading to blurred vision in the center of the visual field. This can progress to a point where the individual has complete vision loss in the center of the visual field, known as blind spots. Though AMD can affect one or both eyes, it rarely leads to complete blindness, since the peripheral vision of the patient remains intact. There are two main types of AMD: dry and wet. Dry AMD accounts for the majority of the cases of the disease and is characterized by small yellow deposits, known as drusen, occurring in the macula between the retinal pigment epithelium and choroid. The progression in this form of AMD is initially slow with very few symptoms and only intensifies when retinal atrophy occurs. The wet form of AMD is characterized by choroid neovascularization, which is the abnormal growth of blood vessels that are prone to breaking and lead to blood, protein leakage, and scarring ultimately leading to permanent damage of the cones and therefore, vision loss. The progression of the wet form and vision loss is much more rapid than in dry AMD.

Retinitis Pigmentosa (RP)
Retinitis Pigmentosa is an inherited degenerative eye disease involving rod photoreceptor cells that has an early onset in younger individuals. In this disease, the rods deteriorate progressively and eventually lead to vision loss in the periphery vision field as well as night vision. This loss first occurs externally then progresses inwards, creating an effect of “tunnel vision” in the patient. Visual impairment occurs symmetrically, with both eyes affected in similar timeframes. Unlike AMD, this eye disease can extend beyond the periphery and begin to affect the central visual field through degeneration of cone photoreceptor cells. This leaves the individual with continuous vision loss that can eventually lead to complete blindness, though quite rare. Retinitis Pigmentosa is genetically inherited and has a variety of gene mutations that can lead to an RP phenotype, leading to a variety of inheritance patterns. However, when the inheritance pattern is autosomal dominant, the majority of cases are linked to mutations in the rhodopsin gene. This mutation disrupts the function of rod-opsin, which is an essential protein in the phototransduction cascade. There is currently no cure for Retinitis Pigmentosa. However, in 2008 Shigeru Sato and his colleagues discovered an extracellular matrix-like retinal protein named Pikachurin, which could lead to a potential disease therapy due to its involvement with interactions between photoreceptor cells and bipolar cells.

Microelectrode Arrays for Retina Stimulation
As mentioned above, there are no cures for the progressive visual impairments caused by macular degeneration and Retinitis Pigmentosa. However, in both diseases, even though there is substantial photoreceptor cell loss, a significant amount of the inner retinal neurons survive years after disease onset. This provides an opportunity for artificial stimulation of the remaining, still properly functioning retina cells, through electrodes, to restore visual information for the human patient. Microelectrode arrays use electrodes to stimulate the retina extracellularly by tight placement that allows an electrochemical interface to be formed with the array and saline found around the retina. Current is injected through to the array-retina interface and ultimately drives the depolarization of the membranes of the neurons leading to action potentials. This stimulation can be cathodic or anodic. In cathodic stimulation, negative charges arise outside the membrane thereby driving positive charges intracellularly, resulting in a depolarization gradient that is strongest at close proximity to the electrode. In anodic stimulation, hyperpolarization occurs in the areas closest to the electrodes and depolarization occurs at further distances. Therefore, cathodic is generally viewed as more efficient for stimulation since it requires a much lower current injection. The phase of stimulation is not the only factor that affects the efficacy of stimulation. The waveform, which can take on a variety of shapes such as monophasic and biphasic, plays a large role in the safety of stimulation of retinal neurons. For example, in monkeys, it was found that a monophasic current with only an anodic phase could damage previously viable cells. Therefore, implants that use retinal stimulation will make use of a charge-balanced biphasic waveform. This waveform utilizes a cathodic phase for stimulation and an anodic phase for discharging, thereby balancing the charges around on the membrane. With this ability to stimulate, a retinal prosthetic can be implanted either behind the retina, and is then referred to as subretinal implant. This brings the electrodes closest to the damaged photoreceptors and the still properly functioning bipolar cells, which are the real stimulation target here. If the stimulation electrodes penetrate the choroid, which contains the blood supply of the retina, the implants are sometimes called "suprachoroidal" implants. Or the implant may be put on top of the retina, closest to the Ganglion cell layer, aiming at stimulation of the RGCs instead. These implants are referred to as epiretinal implants. Both approaches are currently being investigated by several research groups. They both have significant advantages as well as drawbacks. Before we treat them in more detail separately, we describe some key challenges that need consideration in both cases.

Electrode Technology Challenges
A big challenge for retinal implants comes from the extremely high spatial density of nervous cells in the human retina. There are roughly 125 million photoreceptors (rods and cones) and 1.5 million ganglion cells in the human retina, as opposed to approximately only 15000 hair cells in the human cochlea . In the fovea, where the highest visual acuity is achieved, as many as 150000 cones are located within one square millimeter. While there are much fewer RGCs in total compared to photoreceptors, their density in the foveal area is close to the density of cones, imposing a tremendous challenge in addressing the nervous cells in high enough spatial resolution with artificial electrodes. Virtually all current scientific experiments with retinal implants use micro-electrode arrays (MEAs) to stimulate the retina cells. High resolution MEAs achieve an inter-electrode spacing of roughly 50 micrometers, resulting in an electrode density of 400 electrodes per square millimeter. Therefore, a one to one association between electrodes and photoreceptors or RGCs respectively is impossible in the foveal area with conventional electrode technology. However, spatial density of both photoreceptors as well as RGCs decrease s quickly towards the outer regions of the retina, making one-to-one stimulation between electrodes and peripheral nerve cells more feasible . Another challenge is operating the electrodes within safe limits. Imposing charge densities above 0.1 mC/cm² may damage the nervous tissue . Generally, the further a cell is away from the stimulating electrode, the larger is the current amplitude required for stimulation of the cell. Furthermore, the lower the stimulation threshold, the smaller the electrode may be designed and the compacter the electrodes may be placed on the MEAs, thereby enhancing the spatial stimulation resolution. Stimulation threshold is defined as the minimal stimulation strength necessary to trigger a nervous response in at least 50% of the stimulation pulses. For these reasons, a primary goal in designing retinal implants is to use as low a stimulation current as possible while still guaranteeing a reliable stimulation (i.e. generation of an action potential in the case of RGCs) of the target cell. This can either be achieved by placing the electrode as close as possible to the area of the target cell that reacts most sensitive to an applied electric field pulse or by making the cell projections, i.e. dendrites and/or axons, grow on top the electrode, allowing a stimulation of the cell with very low currents even if the cell body is located far away. Further, an implant fixed to the retina automatically follows the movements of the eyeball. While this entails some significant benefits, it also means that any connection to the implant – for adjusting parameters, reading out data, or providing external power for the stimulation – requires a cable that moves with the implant. As we move our eyes approximately three times a second, this exposes the cable and involved connections to severe mechanical stress. For a device that should remain functioning for an entire life time without external intervention, this imposes a severe challenge on the materials and technologies involved.

Biocompatibility Challenges
Besides electrical challenges, a key challenge in a retinal implant is its contact with biological tissue. When a foreign substance, such as an implant, comes into contact with physiological substances, an immune response is triggered. This response is typically in the form of inflammation or isolation of the substance, which often leads to scarring of the involved tissues. This is an issue especially with retinal implants because the prosthetic has to be inserted, through tissue, to the appropriate location. If the material used is too sharp or is not placed carefully, injury to the tissue can occur further intensifying an immune response. Additionally, these responses can lead to a loss of electrical signal over time as the immune system can “encapsulate” the stimulated area over time, making it difficult for a long-lasting implant. So far, one epi-retinal implant, Argus II, has been able to circumvent biocompatibility issues by having a retinal implant still functioning after 3 years in a patient. This implant makes use of silicone, which is a material that has good long term biocompatibility, but is a stiff substrate that doesn’t allow the device’s configuration to be easily modified. Other materials such as Polyimide and gold have been investigated for retinal implant functionality and biocompatibility. Polyimide is a promising polymer for future implants, since implants made of this material have been functional on human eyes in short-term studies. Such a material is advantageous due to its high biocompatibility, flexibility, and low costs. Optimization of materials suitable for retinal implants is ongoing as technological advances produce more complex microelectrode arrays that need different substrates for maximum functionality .

Subretinal Implants
As the name already suggest, subretinal implants are visual prosthesis located behind the retina. Therefore, the implant is located closest to the damaged photoreceptors, aiming at bypassing the rods and cones and stimulating the bipolar cells in the next nervous layer in the retina. The main advantage of this approach lies in relatively little visual signal processing that takes place between the photoreceptors and the bipolar cells that need to be imitated by the implant. That is, raw visual information, for example captured by a video camera, may be forwarded directly, or with only relatively rudimentary signal processing respectively, to the MEA stimulating the bipolar cells, rendering the procedure rather simple from a signal processing point of view. However, this approach has some severe disadvantages. The high spatial resolution of photoreceptors in the human retina imposes a big challenge in developing and designing a MEA with sufficiently high stimulation resolution and therefore low inter-electrode spacing. Furthermore, the stacking of the nervous layers in z-direction (with the x-y plane tangential to the retina curvature) adds another difficulty when it comes to placing the electrodes close to the bipolar cells. With the MAE located behind the retina, there is a significant spatial gap between the electrodes and the target cells that needs to be overcome. As mentioned above, an increased electrode to target cell distance forces the MAE to operate with higher currents, enlarging the electrode size, the number of cells within the stimulation range of a single electrode and the spatial separation between adjacent electrodes. All of this results in a decreased stimulation resolution as well as opposing the retina to the risk of tissue damage caused by too high charge densities. As shown below, one way to overcome large distances between electrodes and the target cells is to make the cells grow their projections over longer distances directly on top the electrode.

In late 2010, a German research group in collaboration with the private German company “Retina Implant AG”, published results from studies involving tests with subretinal implants in human subjects . A three by three millimeter micro-photodiode array (MPDA) containing 1500 pixels, which each pixel consisting of an individual light-sensing photodiodes and an electrode, was implanted behind the retina of three patients suffering from blindness due to macular degeneration. The pixels were located approximately 70 micrometer apart from each other, yielding a spatial resolution of roughly 160 electrodes per square millimeter – or, as indicated by the authors of the paper, a visual cone angle of 15 arcmin for each electrode. It should be noted, that, in contrast to implants using external video cameras to generate visual input, each pixel of the MPDA itself contains a light-sensitive photodiode, autonomously generating the electric current from the light received through the eyeball for its own associated electrode. So each MPDA pixel corresponds in its full functionality to a photoreceptor cell. This has a major advantage: Since the MPDA is fixed behind the human retina, it automatically drags along when the eyeball is being moved. And since the MPDA itself receives the visual input to generate the electric currents for the stimulation electrodes, movements of the head or the eyeball are handled naturally and need no artificial processing. In one of the patients, the MPDA was placed directly beneath the macula, leading to superior results in experimental tests as opposed to the other two patients, whose MPDA was implanted further away from the center of the retina. The results achieved by the patient with the implant behind the macula were quite extraordinary. He was able to recognize letters (5-8cm large) and read words as well as distinguish black-white patterns with different orientations.

The experimental results with the MPDA implants have also drawn attention to another visual phenomenon, revealing an additional advantage of the MPDA approach over implants using external imaging devices: Subsequent stimulation of retinal cells quickly leads to decreased responses, suggesting that retinal neurons become inhibited after being stimulated repeatedly within a short period of time. This entails that a visual input projected onto a MEA fixed on or behind the retina will result in a sensed image that quickly fades away, even though the electric stimulation of the electrodes remains constant. This is due to the fixed electrodes on the retina stimulating the same cells on the retina all the time, rendering the cells less and less sensitive to a constant stimulus over time. However, the process is reversible, and the cells regain their initial sensitivity once the stimulus is absent again. So, how does an intact visionary system handle this effect? Why are healthy humans able to fix an object over time without it fading out? As mentioned in, the human eye actually continuously adjusts in small, unnoticeable eye movements, resulting in the same visual stimulus to be projected onto slightly different retinal spots over time, even as we tend to focus and fix the eye on some target object. This successfully circumvents the fading cell response phenomenon. With the implant serving both as photoreceptor and electrode stimulator, as it is the case with the MPDA, the natural small eye adjustments can be readily used to handle this effect in a straight forward way. Other implant approaches using external visual input (i.e. from video cameras) will suffer from their projected images fading away if stimulated continuously. Fast, artificial jittering of the camera images may not solve the problem as this external movement may not be in accordance with the eye movement and therefore, the visual cortex may interpret this simply as a wiggly or blurry scene instead of the desired steady long term projection of the fixed image. A further advantage of subretinal implants is the precise correlation between stimulated areas on the retina and perceived location of the stimulus in the visual field of the human subject. In contrast to RGCs, whose location on the retina may not directly correspond to the location of their individual receptive fields, the stimulation of a bipolar cell is perceived exactly at that point in the visual field that corresponds to the geometric location on the retina where that bipolar cell resides. A clear disadvantage of subretinal implants is the invasive surgical procedure involved.

Epiretinal Implants
Epiretinal implants are located on top of the retina and therefore closest to the retina ganglion cells (RGCs). For that reason, epiretinal implants aim at stimulating the RGCs directly, bypassing not only the damaged photoreceptors, but also any intermediate neural visual processing by the bipolar, horizontal and amacrine cells. This has some advantages: First of all, the surgical procedure for an epiretinal implant is far less critical than for a subretinal implant, since the prosthesis need not be implanted from behind the eye. Also, there are much fewer RGCs than photoreceptors or bipolar cells, allowing a more course grained stimulation with increased inter-electrode distance (at least in the peripheral regions of the retina), or an electrode density even superior to that of the actual RGC density, allowing for more flexibility and accuracy when stimulating the cells. A study on the epiretinal stimulation of peripheral parasol cells conducted on macaque retina provides quantitative details. Parasol cells are one type of RGCs forming the secondmost dense visual pathway in the retina. Their main purpose is to encode the movement of objects in the visual field, thus sensing motion. The experiments were performed in vitro by placing the macaque retina tissue on a 61 electrode MEA (60 micrometer inter-electrode spacing). 25 individual parasol cells were identified and stimulated electronically while properties such as stimulation threshold and best stimulation location were analyzed. The threshold current was defined as the lowest current that triggered a spike on the target cell in 50% of the stimulus pulses (pulse duration: 50 milliseconds) and was determined by incrementally increasing the stimulation strength until sufficient spiking response was registered. Please note two aspects: First, parasol cells as RGCs exhibit action potential behavior, as opposed to bipolar cells which work with graded potentials. Second, the electrodes on the MAE were both used for the stimulation pulses as well as for recording the spiking response from the target cells. 25 parasol cells were located on the 61 electrode MAE with a electrode density significantly higher than the parasol cell density, effectively yielding multiple electrodes within the receptive fields of a single parasol cell. In addition to measuring the stimulation thresholds necessary to trigger a reliable cell response, also the location of best stimulation was determined. The location of best stimulation refers to the location of the stimulating electrode with respect to the target cell where the lowest stimulation threshold was achieved. Surprisingly, this was found out to not be on the cell soma, as one would expect, but roughly 13 micrometers further down the axon path. From there on, the experiments showed the expected quadratic increase in stimulation threshold currents with respect to increasing electrode to soma distance. The study results also showed that all stimulation thresholds were well below the safety limits (around 0.05mC/cm², as opposed to 0.1mC/cm² being a (low) safety limit) and that the cell response to a stimulation pulse was fast (0.2 ms latency on average) and precise (small variance on latency). Further, the superior electrode density over parasol cell density allowed a reliable addressing of individual cells by the stimulation of the appropriate electrode, while preventing neighboring cells from also evoking a spike.

Overview of Alternative Technical Approaches
In this section, we give a short overview over some alternative approaches and technologies currently being under research.

Nanotube Electrode
Classic MAEs contain electrodes made out of titanium nitride or indium tin oxide exposing the implant to severe issues with long-term biocompatibility. A promising alternative to metallic electrodes consists of carbon nanotubes (CNT) which combine a number of very advantageous properties. First, they are fully bio compatible since they are made from pure carbon. Second, their robustness makes them suited for long term implantation, a key property for visual prosthesis. Further, the good electric conductivity allows them to operate as electrodes. And finally, their very porous nature leads to extremely large contact surfaces, encouraging the neurons to grow on top the CNTs, thus improving the neuron to electrode contact and lowering the stimulation currents necessary to elicit a cell response. However, CNT electrodes have only emerged recently and at this point only few scientific results are available.

Wireless Implant Approaches
One of the main technical challenges with retinal implant relates to the cabling that connects the MEA with the external stimuli, the power supply as well as the control signals. The mechanical stress on the cabling affects its long term stability and durability, imposing a big challenge on the materials used. Wireless technologies could be a way to circumvent any cabling between the actual retinal implant and external devices. The energy of the incoming light through the eye is not sufficient to trigger neural responses. Therefore, to make a wireless implant work, extra power must be provided to the implant. An approach presented by the Stanford School of Medecine uses an infrared LCD display to project the scene captured by a video camera onto goggles, reflecting infrared pulses onto the chip located on the retina. The chip also uses a photovoltaic rechargeable battery to provide the power required to transfer the IR light into sufficiently strong stimulation pulses. Similar to the subretinal approach, this also allows the eye to naturally fix and focus onto objects in the scene, as the eye is free to move, allowing different parts of the IR image on the goggles to be projected onto different areas on the chip located on the retina. Instead of using infrared light, inductive coils can also be used to transmit electrical power and data signals from external devices to the implant on the retina. This technology has been successfully implemented and tested in the EPIRET3 retinal implant . However, those tests were more a proof-of-concept, as only the patient’s ability to sense a visual signal upon applying a stimulus on the electrodes was tested.

Directed Neural Growth
One way to allow a very precise neural stimulation with extremely low currents and even over longer distances is to make the neurons grow their projections onto the electrode. By applying the right chemical solution onto the retinal tissue, neural growth can be encouraged. This can be achieved by applying a layer of Laminin onto the MEA’s surface. In order to control the neural paths, the Laminin is not applied uniformly across the MEA surface, but in narrow paths forming a pattern corresponding to the connections, the neurons should form. This process of applying the Laminin in a precise, patterend way, is called “microcontact printing”. A picture of what these Lamini paths look like is shown in Figure 5. The successful directed neural growth achieved with this method allowed applying significantly lower stimulation currents compared to classic electrode stimulation while still able to reliably trigger neural response . Furthermore, the stimulation threshold no longer follows the quadratic increase with respect to electrode-soma distance, but remains constant at the same low level even for longer distances (>200 micrometer).

Microelectrode Arrays for Characterization of Retinal Function: A CMOS Based Technology
As explained earlier in the challenges section of retinal implants, many microelectrode arrays suffer from a large pitch and low number of electrodes, affecting their specificity and targeting of neurons in neural networks. This is a limiting factor in being able to see network dynamics and functionalities of neural populations. Specifically, many cellular details such as axonal propagation velocities and axonal information processing are lost in lower density arrays. Recently, researchers have taken advantage of complimentary-oxide-semiconductor (CMOS) technology to create high density microelectrode arrays with high spatial resolution that allow the detection of this cellular information as well as a high signal-to-noise ratio through platinum black deposition. Such arrays can have 26400 microelectrodes over sensing array of 3.85 x 2.10 mm². With a pitch of 17.5 μm, the electrode density is 3265 electrodes per μm² to accompany the 1024 readout channels . With many switches below the electrodes, various electrode configurations can be used to assess the neural population on the chip. With such a sensitive and dense microelectrode chip, single cell identification, network level analysis, and axonal information can be recorded from neural cells. This technology opens the door to electrophysiological phenotypes “biomarkers” to be determined for disease modeling and for functionality of tissues since a dissected retina can be plated and recorded on a microelectrode array.

Retinal Recordings
Light signals are interpreted in the retina and this information is stored in the neurons of the ganglion layer, known as retinal ganglion cells (RGCs). These cells then send this information via action potentials which can be recorded by microelectrode arrays to understand retinal circuitry, development, and the encoding of a visual scene. These in vitro experiments are typically performed by first isolating the retina from its native tissue, plating the tissue with the retinal ganglion cells facing downwards on the array, and recording using light stimulation. Afterwards the data is analyzed using spike sorting, which will be explained later. Drug blockers and different light stimuli can be used to determine photoreceptor response and evaluate functionality. Furthermore, researchers can evaluate the effect of retinal mutations on RGC spiking behaviour to determine electrophysiological biomarkers. In one experiment, researchers used a microelectrode array for wild type mouse retinas and mice with a FRMD7 knockout. FMRD7 is a mutation associated with a horizontal, gaze-dependent rapid eye movements in affected individuals. The data from the recording sessions on the microelectrode array indicated that there was a loss of response to horizontal direction selective cells in the retina. The wild type mice did not have loss of response in either horizontal or vertical direction selective cells. Such a finding indicates the ability to use microelectrode array technology to determine electrophysiological biomarkers of retinal diseases in future research.

Spike Sorting
With the latest microelectrode technologies that allow neural recordings from thousands of electrodes, large quantities of simultaneous electrophysiological data from neural tissue and networks can be analyzed to unveil pertinent electrical information about the nervous system. When using a microelectrode array for neuroscience, electrical signals from neurons (action potentials) are recorded extracellularly. This means that the signal acquired in these recordings is the opposite of patch clamp; the amplitude of the action potential is negative as opposed to patch clamp. These extracellular signatures contain information not only about the action potentials, but also synaptic mechanisms (local field potentials), which can be identified through filtering and analysis. The process to analyze and assign this electrophysiological information to a single neuron is known as spike sorting.

The main aspect of a recording that is analyzed in a microelectrode recording is the spike-train. A neuron can be identified by its spiking activity since the timing of each event is dependent on the size, shape, and position of the neuron relative to the electrode. When recording from thousands of neurons, spike sorting becomes challenging to the cocktail party phenomenon. With multiple neurons in close vicinity to one another, it is very easy for an electrode to record signals from several neurons. Therefore, spike sorting has to identify a single neuron by its electrical “chatter” when there is a lot of background “chatter” occurring as well. Spike sorting is a multi-step process that takes the raw data from the neural population and assigns spikes to a single neuron despite this background noise.

The overview for the spike sorting process can have the following steps: Preprocessing raw data → Spike detection → Extraction of spikes and alignment → feature extraction → clustering → classification. In this general workflow, a spike sorting algorithm takes the raw data from the neural population and first preprocesses it by filtering out the low-frequency part of the action potential (noise). Spikes are then detected by setting a voltage threshold. Afterwards, the extracted spike waveforms need to be aligned with time in respect with a general feature of the action potential, such as its position. Then, the features are extracted from each individual waveform by using principal component analysis or wavelets, which is necessary for reducing the data to the necessary dimensions containing the information of interest. The spikes are then clustered so to create a template for a single neuron. This is done for the individual neurons in the data. There is not a “one size fits all” spike sorting algorithm as multielectrode recordings can differ between different cell types, species, and the type of recording done. Therefore, algorithms have to be adjusted and optimized to produce results that can accurately represent the raw data. However, once the data is spike sorted, a heap of information can be acquired from the data such as interspike intervals, refractory periods, and the ability to plot data of individual neurons against one another to detect differences.