[00:00.000 --> 00:04.700] Hello everybody, my name is Jack Twitty and today I'm going to be getting an introduction to the [00:04.700 --> 00:09.940] world of neuroengineering, how it intersects with biohacking, and how you might one day [00:09.940 --> 00:16.820] grow a brain in a jar yourself. First some real quick biotype information about myself. I'm [00:16.820 --> 00:21.800] currently a doctoral student in the UNC NCSU Joint Department of Biomedical Engineering in [00:21.800 --> 00:26.240] the Research Triangle part of North Carolina. You may or may not have heard of one or both [00:26.240 --> 00:29.720] of those schools depending on whether or not you're a basketball fan, so I've included a [00:29.720 --> 00:35.480] map just to be safe. So even though I've been working in STEM for a while now, I actually [00:35.480 --> 00:40.360] started out as a philosophy and studio art major in undergrad at UNC. I always like to bring up [00:40.360 --> 00:44.540] that disclaimer because I feel it's important to, you know, destroy any bit of credibility I might [00:44.540 --> 00:49.640] have early on in the talk like this. After undergrad I took a hard right turn into engineering [00:49.640 --> 00:54.540] and after teaching myself some electronics for fun and getting myself involved in 3D printing, [00:54.540 --> 00:58.780] which is sort of the hacker gateway drug, I decided I liked that kind of thing and I wanted [00:58.780 --> 01:05.000] to pursue it full-time. So I went on to get an engineering master's at NC State focusing on [01:05.000 --> 01:09.880] microfabrication and then I moved into biomedical engineering specifically first at the City College [01:09.880 --> 01:15.400] of New York and then back at a joint department run by both of my earlier schools. Another thing [01:15.400 --> 01:18.840] I'm interested in is implantable hardware development and I'm actually in collaboration [01:18.840 --> 01:23.720] with some of the other Biohacking Village speakers on a few projects in that area, so stay tuned next [01:23.720 --> 01:28.580] year for that hopefully. And in the almost non-existent spare time I have outside of all [01:28.580 --> 01:34.740] that, I also work as an EMT occasionally. So this talk is not going to be focused on my own research [01:34.740 --> 01:39.320] but just to cover that briefly, my main interest is in the development of tools for neuroscience [01:39.320 --> 01:44.180] and neurology research. I'm really interested in studies of consciousness in particular as sort of [01:44.300 --> 01:50.000] a holdover from my past life as a philosophy major. Some specific areas I've worked in in the [01:50.000 --> 01:55.200] include electronics and hardware for a variety of neurostimulation devices, circuits for energy [01:55.200 --> 02:00.920] harvesting and self-powered systems, a variety of medical type sensing devices, primarily in the [02:00.920 --> 02:07.280] form of wearables for health care. My current research focus involves using electrochemistry [02:07.280 --> 02:12.980] to detect and monitor neurotransmitters both in vivo and in culture models. And for my doctoral [02:12.980 --> 02:17.280] work I'm developing a cell culture system to try and explore some questions related to the [02:17.280 --> 02:24.120] simultaneous release of two neurotransmitters in a specific region of the brain. So again, my goal [02:24.120 --> 02:28.860] with this talk is not really to cover my own research in this area but rather to provide a [02:28.860 --> 02:33.500] very, very high-level overview of neuroengineering and some interesting work that falls under that [02:33.500 --> 02:38.540] umbrella. So neuroengineering is very loosely defined and even a very restrictive slice of [02:38.540 --> 02:43.000] that kind of work would still be a tremendous amount of ground to cover. Since it's an impossible [02:43.000 --> 02:47.080] task to try to do this field justice in such a short talk, I'm not really going to try to do that. [02:47.280 --> 02:50.960] Instead I'll be providing a roadmap for the kind of work being done in neuroengineering [02:50.960 --> 02:54.700] and I'll be highlighting some particular developments that I feel are important just to [02:54.700 --> 02:59.680] give a sampling. This is intended merely as a starting point and in that spirit I'll conclude [02:59.680 --> 03:03.700] by discussing what might be involved in getting started in neuroengineering in a garage, [03:03.700 --> 03:07.560] basement workshop, or other improvised mad science lab if you're choosing. [03:09.260 --> 03:14.220] So to start out, because neuroengineering is such an expansive and diverse field, I think it's useful [03:14.220 --> 03:18.200] to start with some basic delineations of the kind of work being done in that space. [03:18.600 --> 03:23.340] Many different groups define neuroengineering in different ways, sometimes vastly so, [03:23.340 --> 03:27.360] and that can make categorizing this type of work fairly difficult. And since all these different [03:27.360 --> 03:31.820] groups are putting forth their own definitions of the term, I figure I might as well join in. [03:31.820 --> 03:37.380] So I'm going to share my own totally arbitrary ad hoc taxonomy of neuroengineering as it makes [03:37.380 --> 03:42.700] sense to me. Your mileage may vary. I like to divide up neuroengineering projects based on [03:42.700 --> 03:48.300] where the system is being used and specifically what it's being used for. The first main branch [03:48.300 --> 03:52.320] under this scheme is in vivo neuroengineering and that's probably the part of neuroengineering [03:52.320 --> 03:56.520] people in general are most familiar with and what you might think of when I say the term [03:56.520 --> 04:02.020] neuroengineering. I define this branch to include systems that involve creating an interface with [04:02.020 --> 04:07.080] the nervous system of a living human or other animal if applicable. This includes a huge range [04:07.080 --> 04:11.100] of equipment encompassing both invasive and non-invasive devices which can be used for [04:11.100 --> 04:16.460] sensing stimulation or in some cases both. This family includes things like prosthetic devices [04:16.460 --> 04:20.960] that interact with peripheral nerves, stimulators found anywhere in the body like pacemakers, [04:20.960 --> 04:26.620] deep brain stimulation leads, EEG arrays, electrocorticogram probes, and many other [04:26.620 --> 04:30.720] devices depending on where exactly you want to draw the line between nervous system and not [04:30.720 --> 04:36.520] nervous system. As far as recent development goes, this is the category into which virtually all brain [04:36.520 --> 04:41.720] interfaces would fall which have been a huge area of active research for the past few decades. [04:43.000 --> 04:48.260] The second main branch of neuroengineering is the sort of the odd one out and I think a lot [04:48.260 --> 04:52.840] of people might not actually categorize it as neuroengineering proper. But that being said, I [04:52.840 --> 04:57.760] think its potential overlap with the other two branches justifies its inclusion. If we think of [04:57.760 --> 05:03.460] in vivo neuroengineering as using human-designed electronic systems to work in a biological context, [05:03.460 --> 05:07.620] neuromorphic engineering could be posed as the exact opposite of this process. [05:07.720 --> 05:11.980] So in neuromorphic engineering, researchers take inspiration from the behavior of the nervous system [05:11.980 --> 05:16.780] and apply those behaviors and mechanisms to problems of computation, often in a context that [05:16.780 --> 05:21.600] doesn't directly involve biology at all. This works because the nervous system of humans and other [05:21.600 --> 05:26.980] animals exists primarily as a powerful processing and control system, one which has evolved over [05:26.980 --> 05:31.840] millions of years to be highly robust in the face of interference and edge cases. These are things [05:31.840 --> 05:37.000] that computing systems, we typically envision them, are notoriously bad at handling. So while [05:37.280 --> 05:41.520] a nervous system can't compete with digital circuits in terms of processing volume and speed, [05:41.520 --> 05:46.360] all its being equal, many real-world environments would benefit tremendously from the kind of [05:46.360 --> 05:52.020] flexibility offered by neuro-inspired processing. In particular, this combined analog and digital [05:52.020 --> 05:56.220] nature of the sum and fire model of neural processing, where a bunch of digital inputs [05:56.220 --> 06:01.340] are weighted in an analog fashion and determine a single digital output. This allows for advanced [06:01.340 --> 06:06.800] categorization and even quasi-learning behaviors. As testament to the applicability of this idea, [06:06.800 --> 06:10.920] just look at the explosion of the use of neural networks as part of the machine learning tool set [06:10.920 --> 06:15.180] in recent years. While these are implemented almost exclusively in software, the same [06:15.180 --> 06:20.580] neuro-inspired principles apply in a loose way to vastly improve performance in specific use cases. [06:20.800 --> 06:25.660] And under this totally arbitrary taxonomy, neural networks would also fall under the loose umbrella [06:25.660 --> 06:30.160] of neuroengineering. However, more interesting, in my opinion, are recent attempts to apply the [06:30.160 --> 06:35.860] same strategy to hardware directly. So in this approach, electronic stand-ins for neurons, which [06:35.860 --> 06:40.320] vary in terms of how closely they mimic actual neural behavior, are assembled together to create [06:40.480 --> 06:44.800] a processing unit as needed. So this often involves either the use of microelectronic [06:44.800 --> 06:48.680] circuits consisting of multiple conventional components, which together generate neuron-like [06:48.680 --> 06:53.720] behavior, or in more recent examples, the use of newer exotic components to directly create this [06:53.720 --> 06:58.500] desired behavior. So by designing integrated circuits to intrinsically operate in a neuro-inspired [06:58.500 --> 07:03.080] fashion rather than implementing this behavior in software using a conventional digital processor, [07:03.080 --> 07:07.120] this process can be massively paralyzed and sped up, greatly increasing the benefits of this [07:07.120 --> 07:12.400] approach. The final branch in this taxonomy is one that's probably less well-known than the other two. [07:12.400 --> 07:16.420] In in vitro neuroengineering, neural cells themselves are used in the construction of other [07:16.420 --> 07:22.040] systems engineered towards some purpose other than their intrinsic biological function. So much like [07:22.040 --> 07:26.920] the previous branch, this often involves leveraging the inherent processing capabilities of neuron-like [07:26.920 --> 07:31.640] units to perform some computational task which may itself be totally unrelated to biology. [07:31.820 --> 07:35.580] However, unlike neuromorphic computing, where we're using an engineered electronic device as our [07:35.580 --> 07:39.880] building block, under the in vitro approach we're utilizing actual neurons and interfacing these to [07:39.880 --> 07:44.560] external inputs and outputs. So rather than trying to recreate the behavior of a natural system, we're [07:44.560 --> 07:48.820] cutting out the middleman and harnessing the real thing. While plenty of research has been done on [07:48.820 --> 07:53.220] neuronal behavior in vitro over the past century, using neurons ex vivo as part of a larger [07:53.220 --> 07:58.140] non-biological system is a much newer idea. One of the challenges inherent in this approach is that [07:58.140 --> 08:02.600] unlike most circuits, neurons are living biological entities with constant metabolic requirements and [08:02.700 --> 08:07.020] a propensity to change in real time, sometimes in undesired ways. Short of freezing in sub-zero [08:07.020 --> 08:10.160] temperatures, there's no way to turn off a cell culture the way you can turn off an electronic [08:10.160 --> 08:15.060] device. That said, the biological nature of these systems can also be beneficial. These same inherent [08:15.060 --> 08:19.580] behaviors can offer the ability to self-organize, change in response to inputs, and theoretically [08:19.580 --> 08:24.320] heal to an extent impossible in electronic circuits. Finally, and of particular interest to [08:24.320 --> 08:29.300] the biohacker-turned-neuroengineer, cells can, with care, be grown and manipulated in a variety of [08:29.300 --> 08:33.700] settings, including those available to the enterprising biohacker. Certainly less stringent [08:33.700 --> 08:37.220] environments than the type of clean room required for microelectronic fabrication. [08:38.320 --> 08:41.640] So hopefully at this point, my method of delineating these different branches of research [08:41.640 --> 08:45.880] makes more sense. Between any two branches, there are a number of parallels that can be drawn, with [08:45.880 --> 08:50.200] variation only in either the end goal of the system or the exact strategy and methods used to [08:50.200 --> 08:54.940] accomplish that goal. It's also worth noting that, as defined, projects that fall into one of these [08:54.940 --> 08:59.400] branches have tended to remain within only one branch of neuroengineering. However, I would argue [08:59.400 --> 09:04.200] that as these technologies mature in these use cases to which they're applied grow in number, [09:04.200 --> 09:08.060] we'll see more efforts to join these approaches and leverage the unique strengths of each to allow [09:08.060 --> 09:12.280] us to do things we can only dream of at this point. This kind of synergistic approach is something [09:12.280 --> 09:17.000] that I'm personally interested in exploring in my own future research. So speaking of doing things, [09:17.000 --> 09:20.860] what are some actual examples of each of these types of neuroengineering? And as a reminder, [09:20.860 --> 09:25.280] this is intended to be a highlight reel and not a comprehensive overview. My goal is just to point [09:25.280 --> 09:29.840] out some specific efforts I think are of particular importance or interest, and I encourage you to use [09:29.840 --> 09:32.760] them as a starting point if you're interested in learning more about this kind of thing. [09:34.000 --> 09:38.140] So here's an example of a state-of-the-art neural interface array, which is the culmination of [09:38.140 --> 09:42.560] several years of development work by multiple collaborating labs. The Shepard and Viventi [09:42.560 --> 09:46.420] groups are both big players in the world of electrode arrays, and John Rogers is practically [09:46.420 --> 09:50.740] the founder of the field of soft electronics. I really encourage you to look in the work done by [09:50.740 --> 09:54.980] these labs if that kind of thing interests you. This device is an example of an electrode array [09:54.980 --> 09:59.240] that's similar in operating principle to an EEG array, but with some key differences that dramatically [09:59.240 --> 10:03.800] improve sensitivity and resolution. So this type of device is called an electrocorticogram, or [10:03.800 --> 10:07.540] ECoG array, and it's designed to be placed directly on the surface of the brain. [10:07.700 --> 10:12.060] Since neural activity recording resolution depends on both surface coverage and distance [10:12.060 --> 10:16.700] from the neurons being monitored, an array like this offers tremendous advantages. So first, [10:16.700 --> 10:21.220] the huge number of recording sites allow for high inherent spatial precision. Second, the thinness [10:21.220 --> 10:24.920] and flexibility of this array allows it to be molded to the curvature of the brain, improving [10:24.920 --> 10:29.740] coverage and minimizing separation distance. This is particularly important in the case of primates, [10:29.740 --> 10:33.480] and especially humans, where there's a large degree of surface contouring. So this design [10:33.480 --> 10:37.440] allows the array to be placed around the gyri and into the cell side, which would be impossible [10:37.440 --> 10:41.600] with a thicker array. Further, this array incorporates active electronics into the [10:41.600 --> 10:46.120] electrodes themselves, allowing for signals to be amplified near their origin and allowing multiple [10:46.120 --> 10:50.060] electrodes to be multiplexed between a smaller number of conductors running between the skull [10:50.060 --> 10:54.480] and the outside instrumentation. Without this type of signal management, high density arrays quickly [10:54.480 --> 10:59.520] become impossible to manage practically. And because it's sort of difficult to appreciate [10:59.520 --> 11:04.360] that scale, here's that array in higher resolution. So this is 1,008 electrodes in [11:04.360 --> 11:10.600] less than one square centimeter, which is pretty darn impressive. As mentioned previously, [11:10.600 --> 11:14.200] neuromorphic engineering applies biologically inspired processing to other computational [11:14.200 --> 11:18.300] problems, which might not have anything to do with biology at all. However, in this particular case, [11:18.300 --> 11:22.520] artificial neurons were used to model the activity of simulated portions of the brain. [11:22.800 --> 11:27.460] So due to the immense scale of connectivity in the brain, which is made up of about 86 billion [11:27.460 --> 11:31.820] neurons and vastly more synaptic connections, and the difficulty of accessing the living brain [11:31.820 --> 11:36.300] using instrumentation, simulation efforts like the Human Brain Project have played a vital role in [11:36.300 --> 11:40.980] the study of cognitive processing. That said, even these massive joint efforts are limited due to the [11:40.980 --> 11:45.820] sheer computational resources required. To help ameliorate this problem, researchers at the Human [11:45.820 --> 11:50.240] Brain Project leveraged the fact that artificial neurons fabricated from metallic and semiconducting [11:50.240 --> 11:55.080] materials have a huge intrinsic speed advantage over biological neurons due to differences in [11:55.080 --> 12:00.260] conduction mechanisms. So as a result, by simulating real neurons using engineered ones in silicon, [12:00.760 --> 12:05.000] limited neuronal processes were able to be simulated at a much faster rate than what's [12:05.000 --> 12:10.460] observed in real physiology. The brain scale system shown here utilizes a large number of [12:10.460 --> 12:15.080] artificial neurons fabricated on silicon wafers using conventional integrated circuit manufacturing [12:15.080 --> 12:20.640] techniques. These are created in bulk across the wafer surface, and unlike most integrated circuits, [12:20.640 --> 12:25.360] these are actually directly accessed from the intact wafer, so there's no dicing. [12:26.080 --> 12:30.640] So here's a closer look at the individual neuron circuits. At the bottom, that's a layout of two [12:30.640 --> 12:36.380] of the neural equivalent circuits that are fabricated in mass on that chip. They're [12:36.380 --> 12:42.700] arranged into one of 384 clusters of 512 neurons each onto a single wafer, and the assembly into [12:42.700 --> 12:47.540] which that wafer is loaded is shown on the right with supporting and interface electronics. [12:47.540 --> 12:56.020] So 20 wafer modules consisting of 384 clusters of 114,688 programmable synapses and 512 neurons [12:56.020 --> 13:02.820] each give a total of 880 million synapses and 3.9 million neurons for the entire system, [13:02.820 --> 13:08.700] which is further capable of running 100,000 times faster than the same set of neurons would [13:08.700 --> 13:13.780] in vivo. So these advantages and the ability to continuously monitor each simulated neuron [13:13.780 --> 13:19.100] in real time have enabled considerable advances in the field of computational neuroscience. [13:19.800 --> 13:24.680] So as I mentioned earlier, artificial hardware or software-based neurons aren't the only tools [13:24.680 --> 13:28.820] that neuroengineers use to solve processing tasks. So here's an example of real neurons [13:28.820 --> 13:33.820] being used to do the same thing. In 2005, researchers at the University of Florida were [13:33.820 --> 13:39.360] able to extract about 25,000 neurons from rat cortical tissue, seed them onto a 60-electrode [13:39.360 --> 13:44.400] microarray treated to function as a culture disk, and then observe the spontaneous formation [13:44.400 --> 13:48.420] of interneuronal connections and eventually synchronized firing activity around 10 days [13:48.420 --> 13:54.360] after initial seeding. Two electrodes were used for both stimulation and recording, and these [13:54.360 --> 13:58.400] outputs were fed to a flight simulator program where neuronal activity of these electrodes was [13:58.400 --> 14:03.580] used to establish pitch and yaw control. So by feeding back a stimulatory error signal based on [14:03.580 --> 14:08.240] telemetry output by the flight simulator, firing activity and neurons near the electrode were [14:08.240 --> 14:13.620] modified. And over the course of repeating the simulation multiple times, neuronal activity was [14:13.620 --> 14:18.060] found to change in self-weight in order to produce stable flight in response to changes in telemetry [14:18.060 --> 14:24.380] data over the course of about 15 minutes. So this feedback adjust and test mechanism is by itself [14:24.380 --> 14:28.880] very similar to techniques used to train software neural networks, which isn't surprising given the [14:28.880 --> 14:33.660] fact that these were in turn inspired by learning mechanisms seen in the nervous system. So it's [14:33.660 --> 14:39.400] come full circle here. So now that we've seen some examples of different types of neuroengineering [14:39.400 --> 14:44.020] research, I want to briefly discuss how biohackers might go about getting involved in this type of [14:44.020 --> 14:50.120] work on their own. So growing cells in general is not a difficult process. To quote Jeff Goldblum, [14:50.120 --> 14:55.240] life finds a way. The real difficulty in this process arises from the same principle, life [14:55.240 --> 15:00.340] finds a way, but it's not always the way that you want it to. So in order to successfully culture [15:00.480 --> 15:05.080] a specific cell type, you have to take careful consideration of the cell source, the conditions [15:05.080 --> 15:10.400] in which the cells are kept, and the intended final use of those cells. So regardless of type, [15:10.400 --> 15:16.020] though, a key principle of cell culture is aseptic technique. So care must be taken at all times in [15:16.020 --> 15:19.920] the process to avoid introducing other microorganisms into the culture environment, [15:19.920 --> 15:25.920] as these might outcompete or otherwise harm the cells of interest. So prior to culture, [15:25.920 --> 15:30.420] the cells must first be acquired from some suitable source. The simplest mechanism for [15:30.420 --> 15:35.040] obtaining neural cells is the harvest of primary cells from tissue samples, either acquired directly [15:35.040 --> 15:40.320] from the donor animal or ordered through a supplier such as BrainBits or some other vendor. [15:40.540 --> 15:44.640] The most common sources for these cells are rats, mice, and embryonic chicks. [15:45.280 --> 15:49.500] After harvest, the cells of interest have to be purified from this sample, which will also likely [15:49.500 --> 15:54.580] contain other cell types as well as other biomolecules and extracellular matrix proteins [15:54.580 --> 15:59.020] and connected tissue. In order to enrich the primary culture, these extra components have to [15:59.020 --> 16:03.540] be removed, typically with enzymes known as proteases, which break down matrix proteins, [16:03.540 --> 16:08.560] allowing the cells to be dissociated. Additional sorting based on cell type may also be needed to [16:08.560 --> 16:13.780] remove glial cells and other neuron phenotypes if necessary. So since mature neurons don't normally [16:13.780 --> 16:18.140] undergo division, primary cultures need to be reestablished periodically as more cells are [16:18.140 --> 16:23.340] required. To avoid this problem, another option is using immortalized secondary cultures, [16:23.340 --> 16:29.140] and these are derivatives of primary cells which have been modified to exhibit tumor-like behavior, [16:29.140 --> 16:33.820] including indefinite division. So these cells tend to take on very homogeneous characteristics, [16:33.820 --> 16:38.200] which can be beneficial. However, this modification is the possible downside of modifying [16:38.200 --> 16:42.340] the cellular characteristics in a way that adversely impacts the intended application, [16:42.340 --> 16:46.620] and it definitely can differ from the normal phenotype of the cell. So whether or not that's [16:46.740 --> 16:51.060] a deal breaker depends on the application. So a more complex third option is the use of [16:51.060 --> 16:55.360] progenitor cells, which are cells that are stem-like in that they're capable of undergoing [16:55.360 --> 17:00.220] both division and differentiation, which allows for a baseline stock of cells to be maintained [17:00.220 --> 17:04.260] while simultaneously producing an essentially indefinite supply of the final differentiated [17:04.260 --> 17:09.240] cells needed. So this route's tricky because the cells rely on specific and sometimes subtle [17:09.240 --> 17:13.300] external cues to guide their ultimate differentiation, which must be carefully [17:13.300 --> 17:19.920] monitored during culturing because it can change pretty rapidly. So one major advantage of this [17:19.920 --> 17:25.620] technique is that this represents one of the only ways of acquiring human neural cells. So mature [17:25.620 --> 17:30.460] cells can undergo induced dedifferentiation, which reverts them back into stem-like cells [17:31.120 --> 17:35.800] when appropriate genetic modifications are made. This procedure is moderately difficult, [17:35.800 --> 17:38.960] but it's fairly well established at this point and remains one of the only ways that you can [17:38.960 --> 17:45.020] get human neural cells as a culture, which is pretty cool. So in order to grow, cells have [17:45.020 --> 17:49.360] to be stormed in a warm, humid environment, and the supply of gas must be suitable for the type [17:49.360 --> 17:54.080] of cell being cultured. It's important for the environment to remain aseptic again, as it's [17:54.080 --> 17:59.220] essential for preventing other opportunistic organisms such as yeasts, bacteria, and viruses [17:59.220 --> 18:04.260] from colonizing the culture. Most abiding cell types will grow to the point where their density [18:04.260 --> 18:08.840] in the culture begins to negatively impact cell survival. When the cells reach a certain coverage [18:08.840 --> 18:13.920] of the culture chamber surface, termed confluence, some of the cells must be removed to prevent [18:13.920 --> 18:19.120] adverse effects from damaging the remaining cells. So this removal of cells can also be [18:19.120 --> 18:24.400] used to expand the culture back into additional flasks to build up stock if desired. This is a [18:24.400 --> 18:29.100] technique called passaging. And in addition to the different temperatures and atmospheric [18:29.500 --> 18:33.660] requirements, cells tend to differ even more with respect to their metabolic requirements. [18:33.660 --> 18:38.660] So fresh media has to be changed regularly and supplied appropriately, and it has to contain [18:38.660 --> 18:43.280] the required nutrients specific to that cell type. Further, neural cells are particularly [18:43.280 --> 18:47.640] sensitive to the choice of substrate or the material on which the cells directly rest. [18:47.820 --> 18:51.340] So frequently the culture chamber itself will be coated with an intermediate substance, [18:51.340 --> 18:55.840] often a polymer or substances normally found in the extracellular matrix like collagen. [18:56.080 --> 19:00.220] Different substrates can actually induce or prohibit different cell differentiation routes [19:00.220 --> 19:04.840] in some cases, or it may even cause de-differentiation. It entirely depends on the cell [19:04.840 --> 19:10.660] specific type. So certain substrates are able to quickly polymerize into 3D structures suitable [19:10.660 --> 19:15.760] for seeding cells into to form volumetric cultures. So this is sometimes used with [19:15.760 --> 19:20.760] neural progenitor cells to form 3D spatially differentiated structures called organoids. [19:22.120 --> 19:26.020] And then finally, you need to be able to interact with your cultured cells in a way that's [19:26.020 --> 19:30.320] appropriate for your specific application. So typically this is going to involve electrical [19:30.320 --> 19:35.260] or chemical detection or stimulation, usually referred to as electrophysiology and neurochemistry [19:35.260 --> 19:40.280] respectively. In some applications, both methodologies might be used at the same time. [19:40.280 --> 19:44.760] So groups of cells can be stimulated and local potentials can be measured using electrode arrays, [19:44.760 --> 19:49.820] which is those discussed earlier. If a neuron specific electrical activity needs to be measured, [19:49.820 --> 19:54.080] patch clamps and other similar devices might be used. In this technique, you take a glass [19:54.080 --> 19:58.720] capillary that's formed into a very small point that's filled with electrolyte. You then aspirate [19:58.720 --> 20:02.560] that against the membrane of a target cell. And what that does is it creates an electrical [20:02.560 --> 20:07.040] connection with the interior of the cell, allowing for the cell membrane potential to be measured or [20:07.040 --> 20:12.400] even driven with respect to the extracellular space. So neurochemical measurements, [20:12.400 --> 20:16.580] which are a little trickier, can be made using electrochemical techniques where specific [20:16.580 --> 20:20.740] analytes are oxidized or reduced against the surface of an electrode in response to a varying [20:20.740 --> 20:25.800] electrode potential. This works with only a subset of analytes, however, but there are additional [20:25.800 --> 20:30.880] tricks that can be used to increase selectivity or expand applicability to other molecules through [20:30.880 --> 20:36.260] the use of enzymes or other functional coatings. And then when the cells need to be arranged in [20:36.260 --> 20:42.240] specific ways, microfluidic devices can be created using soft lithography and seeded with cells, [20:42.240 --> 20:47.340] sort of like this one here. It's also entirely possible to create larger millifluidic devices [20:47.340 --> 20:52.440] that can be fabricated using fairly basic tools. I've seen 3D printed ones, for example. [20:52.840 --> 20:57.720] Generally, a positive pattern is made and an elastomer like PDMS or polydimethylsiloxane [20:57.720 --> 21:02.220] is poured onto the mold and then removed. And then that resulting cast is then bonded to a [21:02.220 --> 21:07.760] microscope slide using ozone to create a really tight seal. And then this can have fluid basically [21:08.200 --> 21:14.320] flowed through it with a watertight joint between the cast and the glass. And you can put cells in [21:14.320 --> 21:18.040] there and basically they're allowed to grow freely and then interact based on the geometry that's [21:18.040 --> 21:24.940] specified by the design. So to finish up, I have some useful links here. One of the benefits of [21:24.940 --> 21:28.900] doing this virtually is you all get access to the stuff after the fact. So if you're interested in [21:28.900 --> 21:33.920] suppliers of neural culture reagents and whatnot, BrainBits is a great resource for that. [21:34.040 --> 21:39.020] A bunch of the other vendors like Thermo Fisher have reagent and media guides and also tend to [21:39.020 --> 21:44.040] publish protocols for de-differentiation and differentiation of neural cells. So if you want [21:44.040 --> 21:47.760] to try getting started on any of this stuff at home, that would be a resource I would recommend. [21:47.960 --> 21:51.620] If you're interested in the Human Brain Project, which has really taken a lot of these [21:53.120 --> 21:57.020] methodologies and some of the systems that have been developed and applied them to problems of [21:57.020 --> 22:01.880] neuroscience, as I mentioned earlier, it sort of extends beyond neuroengineering, but I encourage [22:01.880 --> 22:06.060] you to check out the Human Brain Project in that case, and specifically their neuromorphic [22:06.060 --> 22:10.920] computing research platform, which I discussed earlier, of which the BrainScale system is just [22:10.920 --> 22:16.740] one facet. There's a bunch of other systems like that and other examples like IBM is coming out [22:16.740 --> 22:22.660] with some models of that on dedicated ICs currently. So there's a huge variety of different [22:22.660 --> 22:27.000] projects in all of these areas of neuroengineering. Like I said, this is just a very brief [22:27.620 --> 22:32.700] skim of some of the projects that I think are interesting and representative of each area. [22:33.200 --> 22:35.840] And so I definitely encourage you to check it out more if you're interested. [22:36.380 --> 22:39.620] With that, here's some other references as well. [22:39.900 --> 22:44.020] And thank you very much for the talk. I'd be happy to take any questions during the Q&A.