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Can plants learn? This question stirs up controversy and speculation in the classroom, as it is currently doing in the scientific community at large. We leverage the controversy to ask students to contribute to the greater body of knowledge by using scientific principles in creative research projects. Ninth-grade honors biology students became familiar with original research and the surrounding controversy, and performed experiments testing two distinct forms of plant learning in Pisum sativum (pea) and Mimosa pudica (sensitive plant).

Neuroscience courses, largely relegated to advanced undergraduate or graduate universities, are now being offered in high schools and middle schools. Low-tech versions of advanced neuroscience research tools are being used in hands-on labs. In this NeuroView, I will argue the need for and provide an overview of neuroscience research beyond academia.

The use of robots to help teachers to engage students in STEM subjects has been increasing in recent years, and much progress has been made in K12 schools to incorporate robots in the pedagogy. Recent studies indicate that robots can play a significant role to engage students in neuroscience subjects, but little is known about teachers’ perceptions of using robots to teach neuroscience. In this paper, we present a study based on a survey questionnaire conducted with 84 teachers across multiple high schools in the United States to understand their perceptions about the usefulness of using robots to teach neuroscience. To situate teachers with an example of how robots can be used in neuroscience classrooms, we describe an educational tool called the SpikerBot. Our preliminary results indicate that there is an opportunity for neuroscience-oriented robots in secondary education, provided sufficient on-boarding and training videos.

The global pandemic caused by the novel coronavirus (SARS-COV-2) has forced many universities to abruptly change the delivery of courses from in-person to online. This change to remote learning requires creating new ways to deliver lectures, exams, and discussion groups through online meeting platforms. An often-overlooked challenge is performing lab courses that require access to specialized equipment and resources typically found in the undergraduate laboratory classrooms. Here we discuss some strategies for developing and implementing a full semester neuroscience laboratory course that allows students to fully participate in laboratory exercises at home or in their dorm rooms. Performing lab exercises remotely and independently was shown to significantly improve participant’s self-efficacy and confidence that they can learn complex neuroscience material, when compared to participants who passively watch experiments online. We review best practices to ensure that lessons can be successfully demonstrated by the instructor and carried out by all students. Finally, we discuss the need to provide a level playing field such that all students may succeed, regardless of their current technology resources at home.

Understanding the brain is a fascinating challenge, captivating the scientific community and the public alike. The lack of effective treatment for most brain disorders makes the training of the next generation of neuroscientists, engineers and physicians a key concern. Over the past decade there has been a growing effort to introduce neuroscience in primary and secondary schools, however, hands-on laboratories have been limited to anatomical or electrophysiological activities. Modern neuroscience research labs are increasingly using computational tools to model circuits of the brain to understand information processing. Here we introduce the use of neurorobots - robots controlled by computer models of biological brains - as an introduction to computational neuroscience in the classroom. Neurorobotics has enormous potential as an education technology because it combines multiple activities with clear educational benefits including neuroscience, active learning, and robotics. We describe a 1-week introductory neurorobot workshop that teaches high school students how to use neurorobots to investigate key concepts in neuroscience, including spiking neural networks, synaptic plasticity, and adaptive action selection. Our do-it-yourself (DIY) neurorobot uses wheels, a camera, a speaker, and a distance sensor to interact with its environment, and can be built from generic parts costing about $170 in under 4 h. Our Neurorobot App visualizes the neurorobot's visual input and brain activity in real-time, and enables students to design new brains and deliver dopamine-like reward signals to reinforce chosen behaviors. We ran the neurorobot workshop at two high schools (n = 295 students total) and found significant improvement in students' understanding of key neuroscience concepts and in students' confidence in neuroscience, as assessed by a pre/post workshop survey. Here we provide DIY hardware assembly instructions, discuss our open-source Neurorobot App and demonstrate how to teach the Neurorobot Workshop. By doing this we hope to accelerate research in educational neurorobotics and promote the use of neurorobots to teach computational neuroscience in high school.

Understanding the brain is a fascinating challenge, captivating the scientific community and the public alike. The lack of effective treatment for most brain disorders makes the training of the next generation of neuroscientists, engineers and physicians a key concern. Over the past decade there has been a growing effort to introduce neuroscience in primary and secondary schools, however hands-on laboratories have been limited to anatomical or electrophysiological activities. Modern neuroscience research labs are increasingly using computational tools to model circuits of the brain to understand information processing. Here we introduce the use of neurorobots - robots controlled by computer models of biological brains - as an introduction to computational neuroscience in the K-12 classroom. Neurorobotics has enormous potential as an education technology because it combines multiple activities with clear educational benefits including neuroscience, active learning, and robotics. We describe an introductory neurorobot workshop that teaches high school students how to use neurorobots to investigate key concepts in neuroscience, including spiking neural networks, synaptic plasticity, and adaptive action selection. Our do-it-yourself (DIY) neurorobot uses wheels, a camera, a speaker, and a distance sensor to interact with its environment, and can be built from generic parts costing about $150 in under 4 hrs. Our Neurorobot App visualizes the neurorobot's visual input and brain activity in real-time, and enables students to design new brains and deliver dopamine-like reward signals to reinforce chosen behaviors. We have tested the Neurorobot Workshop with high school students (n = 3 workshops, 9 students total) and have found that students were able to complete all exercises in under 3 hrs. In a post-workshop survey, students reported having gained the ability to develop neural networks that perform specific functions, including goal-directed behavior and memory. Here we provide DIY hardware assembly instructions, discuss our open-source Neurorobot App and demonstrate how to teach the Neurorobot Workshop. By doing this we hope to accelerate research in educational neurorobotics and promote the use of neurorobots to teach computational neuroscience in high school.

Summary Basal ganglia circuits are essential for the organization and execution of voluntary actions. Within the striatum, fast-spiking interneurons (FSIs) are thought to tightly regulate the activity of medium-spiny projection neurons (MSNs) through feed-forward inhibition, yet few studies have investigated the functional contributions of FSIs in behaving animals. We recorded presumed MSNs and FSIs together with motor cortex and globus pallidus (GP) neurons, in rats performing a simple choice task. MSN activity was widely distributed across the task sequence, especially near reward receipt. By contrast, FSIs showed a coordinated pulse of increased activity as chosen actions were initiated, in conjunction with a sharp decrease in GP activity. Both MSNs and FSIs were direction selective, but neighboring MSNs and FSIs showed opposite selectivity. Our findings suggest that individual FSIs participate in local striatal information processing, but more global disinhibition of FSIs by GP is important for initiating chosen actions while suppressing unwanted alternatives.

The isolation of single units in extracellular recordings involves filtering. Removing lower frequencies allows a constant threshold to be applied in order to identify and extract action potential events. However, standard methods such as Butterworth bandpass filtering perform this frequency excision at a cost of grossly distorting waveform shapes. Here, we apply wavelet decomposition and reconstruction as a filter for electrophysiology data and demonstrate its ability to better preserve spike shape. For the majority of cells, this approach also improves spike signal-to-noise ratio (SNR) and increases cluster discrimination. Additionally, the described technique is fast enough to be applied real-time.

The introduction of affordable, consumer-oriented 3-D printers is a milestone in the current "maker movement," which has been heralded as the next industrial revolution. Combined with free and open sharing of detailed design blueprints and accessible development tools, rapid prototypes of complex products can now be assembled in one's own garage--a game-changer reminiscent of the early days of personal computing. At the same time, 3-D printing has also allowed the scientific and engineering community to build the "little things" that help a lab get up and running much faster and easier than ever before.

The ability to control a prosthetic device directly from the neocortex has been demonstrated in rats, monkeys and humans. Here we investigate whether neural control can be accomplished in situations where (1) subjects have not received prior motor training to control the device (naive user) and (2) the neural encoding of movement parameters in the cortex is unknown to the prosthetic device (naive controller). By adopting a decoding strategy that identifies and focuses on units whose firing rate properties are best suited for control, we show that naive subjects mutually adapt to learn control of a neural prosthetic system. Six untrained Long-Evans rats, implanted with silicon micro-electrodes in the motor cortex, learned cortical control of an auditory device without prior motor characterization of the recorded neural ensemble. Single- and multi-unit activities were decoded using a Kalman filter to represent an audio "cursor" (90 ms tone pips ranging from 250 Hz to 16 kHz) which subjects controlled to match a given target frequency. After each trial, a novel adaptive algorithm trained the decoding filter based on correlations of the firing patterns with expected cursor movement. Each behavioral session consisted of 100 trials and began with randomized decoding weights. Within 7 +/- 1.4 (mean +/- SD) sessions, all subjects were able to significantly score above chance (P0.05, randomization method) in a fixed target paradigm. Training lasted 24 sessions in which both the behavioral performance and signal to noise ratio of the peri-event histograms increased significantly (P0.01, ANOVA). Two rats continued training on a more complex task using a bilateral, two-target control paradigm. Both subjects were able to significantly discriminate the target tones (P0.05, Z-test), while one subject demonstrated control above chance (P0.05, Z-test) after 12 sessions and continued improvement with many sessions achieving over 90% correct targets. Dynamic analysis of binary trial responses indicated that early learning for this subject occurred during session 6. This study demonstrates that subjects can learn to generate neural control signals that are well suited for use with external devices without prior experience or training.

The earthworm is ideal for studying action potential conduction velocity in a classroom setting, as its simple linear anatomy allows easy axon length measurements and the worm's sparse coding allows single action potentials to be easily identified. The earthworm has two giant fiber systems (lateral and medial) with different conduction velocities that can be easily measured by manipulating electrode placement and the tactile stimulus. Here, we present a portable and robust experimental setup that allows students to perform conduction velocity measurements within a 30-min to 1-h laboratory session. Our improvement over this well-known preparation is the combination of behaviorally relevant tactile stimuli (avoiding electrical stimulation) with the invention of minimal, low-cost, and portable equipment. We tested these experiments during workshops in both a high school and college classroom environment and found positive learning outcomes when we compared pre- and posttests taken by the students.

The goal of fixation is to rapidly and uniformly preserve tissue in a life-like state. While placing tissue directly in fixative works well for small pieces of tissue, larger specimens like the intact brain pose a problem for immersion fixation because the fixative does not reach all regions of the tissue at the same rate (5,7). Often, changes in response to hypoxia begin before the tissue can be preserved (12). The advantage of directly perfusing fixative through the circulatory system is that the chemical can quickly reach every corner of the organism using the natural vascular network. In order to utilize the circulatory system most effectively, care must be taken to match physiological pressures (3). It is important to note that physiological pressures are dependent on the species used. Techniques for perfusion fixation vary depending on the tissue to be fixed and how the tissue will be processed following fixation. In this video, we describe a low-cost, rapid, controlled and uniform fixation procedure using 4% paraformaldehyde perfused via the vascular system: through the heart of the rat to obtain the best possible preservation of the brain for immunohistochemistry. The main advantage of this technique (vs. gravity-fed systems) is that the circulatory system is utilized most effectively.

While the development of microelectrode arrays has enabled access to disparate regions of a cortex for neurorehabilitation, neuroprosthetic and basic neuroscience research, accurate interpretation of the signals and manipulation of the cortical neurons depend upon the anatomical placement of the electrode arrays in a layered cortex. Toward this end, this report compares two in vivo methods for identifying the placement of electrodes in a linear array spaced 100 µm apart based on in situ laminar analysis of (1) ketamine-xylazine-induced field potential oscillations in a rat motor cortex and (2) an intracortical electrical stimulation-induced movement threshold. The first method is based on finding the polarity reversal in laminar oscillations which is reported to appear at the transition between layers IV and V in laminar 'high voltage spindles' of the rat cortical column. Analysis of histological images in our dataset indicates that polarity reversal is detected 150.1 ± 104.2 µm below the start of layer V. The second method compares the intracortical microstimulation currents that elicit a physical movement for anodic versus cathodic stimulation. It is based on the hypothesis that neural elements perpendicular to the electrode surface are preferentially excited by anodic stimulation while cathodic stimulation excites those with a direction component parallel to its surface. With this method, we expect to see a change in the stimulation currents that elicits a movement at the beginning of layer V when comparing anodic versus cathodic stimulation as the upper cortical layers contain neuronal structures that are primarily parallel to the cortical surface and lower layers contain structures that are primarily perpendicular. Using this method, there was a 78.7 ± 68 µm offset in the estimate of the depth of the start of layer V. The polarity reversal method estimates the beginning of layer V within ±90 µm with 95% confidence and the intracortical stimulation method estimates it within ±69.3 µm. We propose that these methods can be used to estimate the in situ location of laminar electrodes implanted in the rat motor cortex.

Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices.

Local field potentials (LFPs) have been proposed for use in controlling neural prosthetic devices because they can provide reliable motor and sensory-related information, and can easily be recorded over long periods of time. While studies have shown that directional information about motor movements can be inferred from LFPs, it is not known at what depth these signals should be recorded from in order to maximize the amount of movement information. Towards this end, we used a directional motor task in Long Evans rats, while sampling LFPs with an electrode consisting of 16 vertical recording sites that were evenly-spaced 100μm apart. This allowed for simultaneous recording of all layers of the motor cortex. The frequency components of LFPs were then analyzed using k-means clustering to determine directional information as a function of depth. Here we report our initial findings that superficial layers (II/III) of motor cortex may provide more information about movement directions then deeper layers (V). Information analysis of LFPs was conducted using a non- parametric clustering algorithm that estimated the statistical similarity of LFP frequency components associated with leftward and rightward movements using data from all layers of the rat primary motor cortex. Our findings suggest that the superficial layers of the motor cortex may be optimal for determining the movement direction from LFPs. We also provide evidence, using current source density (CSD) analysis, that the better decoding may be due to the synaptic activity in the upper layers.

This paper presents a review of our neural interface research program and provides a brief introduction to the field. We focus on three key areas which aim to minimize encapsulating tissue response to chronic neural interfaces: interface architecture, mechanical stresses, and stem cell-seeded probes. We explore these areas and present our current solutions which have led to the demonstration of cortical control of neuromotor prostheses (NMPs).

Neuromotor prostheses (NMPs) seek to replace motor functions in paralyzed humans by controlling external devices directly from recording contacts placed within the brain (in effect, bypassing the damaged neural tissue). Studies to date have focused on cortical areas for NMP control, but deeper brain structures have also been implicated in generating motor commands. Recent studies combining non-invasive brain computer interface (BCI) experiments with functional brain imaging have revealed that the hemodynamic response related to activity of the Basal Ganglia (BG) increases significantly during BCI control. The BG has long been theorized to be involved in the selection of motor actions in the neocortex, and may be an ideal location for a discrete state NMP. In this report, we investigate the directional information contained in the firing rates of cells within the nuclei of the BG. Four Long-Evans rats performed a directional nose poke task while action potentials were recorded from 21 drivable tetrodes. Our preliminary analysis has identified 16 candidate interneurons and 8 primary cells from properties of the extracellular waveforms, and has determined the amount of directional information in each cell. Using an unsupervised classification decoder of the firing rates in the BG, we could predict the animal's movement direction correctly in >80% of the trials. We further analyzed the cell ensemble decoding and found that the proposed interneuron's directional information peaked ~100-175ms before the projection cells. The fast spiking interneurons of the striatum may be an important control source for NMPs as these cells are thought to play a large role in the selection of actions.

While the development of silicon-substrate microelectrode arrays has enabled chronic recording of single unit activity from multiple neurons simultaneously, accurate interpretation of the signals depend on the anatomical placement of the electrodes. Toward this end, this paper develops an in vivo method for identifying the placement of electrodes based on laminar analysis of ketamine-xylazien-induced field potential oscillations in rat motor cortex. The proposed method is based on finding the polarity reversal in laminar oscillations which is reported to appear in upper part of layer V in laminar High Voltage Spindles (HVSs) of rat cortical column. Analysis of histological images showed a 21 mum error in the estimate of the polarity reversal depth compared to the expected range (850-1050 mum). One out of the four rats did not undergo a phase reversal. Histology verified that the electrode was placed deeper than 1050 jim. We propose that this method can be used to determine an estimate of laminar electrodes implanted in rat motor cortex

Brain Machine Interfaces (BMIs) are emerging as an important research area in clinical therapy. A large range of potential BMI control signals can be found in the brain. In increasing order of volume of brain tissue being sampled, these signal includes recordings of electric discharges from multi unit activity (MUA), summed population activity of thousands of neurons via local field potentials (LFPs), and electrical activity recorded from either the surface of the brain via electrocorticograms (ECoGs) or the surface of the scalp via electroencephalograms (EEGs). While each of these signals have been studied separately, it has been difficult to compare the potential that each signal has for general prosthetic control across studies. Information theory has been proposed as an abstract measurement to bridge this gap, however the maximum information rates of any experiment is limited by the parameters defined by that experiment (e.g. inter-trial interval length, number of targets). Here we propose a different measure of information, which we call information capacity, which measures the maximum possible information rate that a signal can provide. An advantage of measuring information capacity is that it can readily be compared between different signals and different tasks. We show how to calculate information capacity making linear Gaussian assumptions, and we discuss more general possibilities. We present a case study involving a rat BMI task involving either MUA or LFP signals.

Control of prosthetic devices is possible via extra-cellular recordings from cortical neurons. Many of the current cortical control paradigms consist of analyzing the relationship between cortical activity and measured arm movements, and then using this known relationship to map cortical activity to similar prosthetic arm movements. However, measured arm movements are not feasible for amputees or patients with mobility limitations hindering their ability to perform such movements. Here we explore an alternative approach using a rat model in which subjects learn prosthesis control via an adaptive decoding filter that adjusts to the modulation patterns recorded from neurons in the motor cortex. Our methodology takes into account the ability of a subject to learn an effective response strategy in conjunction with online filter adaptation. A modified Kalman filter is demonstrated to "co-adapt" by training on past periods of significant modulation during expected prosthetic device movement. Feedback pertinent to completing the cortical task is given to aid the animal in adopting a response strategy maximizing reward. One subject was able to perform the task consistently above chance after 2 days (4 sessions) of training.

Traditional brain-machine interfaces have typically focused on methods that use rate-based codes as a source for control signals. Opposed to rate, timing of firing across different neurons and within each neuron could also provide information that can be used for controlling brain-machine interfaces or neuroprosthetic devices. Findings have indicated that synchronization of individual spike discharges may help serve the organization of cortical motor processes. We are investigating neural firing synchrony in the context of using it for real-time control for neuroprostheses systems. Our results with rats suggest that subjects can be trained to synchronize neural firing and increase unitary events i.e. spike coincidence patterns that are significantly above chance. Temporal coding methods could be used as additional or alternative cortical control signals for neuroprostheses and brain machine interfaces.

Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.

The neocortex is a six-layered tissue consisting of different cell types. How does unit activity in the different layers of the motor cortex relate to movement? Does implantation in a particular layer improve direction decoding ability for a neuroprosthetic device? We simultaneously recorded unit activity in different layers of the rat motor cortex using chronic multi-site silicon electrodes. We used a combination of histology and electrophysiological signatures of Local Field Potentials (LFPs) to accurately localize the electrode sites in the different layers of the cortex. We analyzed 142 units from two animals and found that 40 units (28%) in Layers II to V showed significant modulation with respect to movement. Of these units that showed significant modulation, 9/20 (45%) of units in Layers II/III encoded directional information as compared to 15/19 (79%) of the units in Layers IV/V. These preliminary results suggest that units in Layers IV/V relatively contain more directional information than other layers of the cortex.