4) One of the most difficult barriers to permeate is the blood-brain barrier. This highly selective Assignment 1 membrane owes this to A fibroblasts B. specialized capillaries C. microglia D. astrocytes

please address this question: 1.Compare and contrast symptoms and circuits in depression with symptoms in circuits in mania. 2.Discuss the neurotransmitters implicated in mood disorders. For this discussion, place particular emphasis on the monoamine hypothesis of depression.

1) 3-part question: A Please draw a schematic of a multipolar neuron receiving information from adjacent neurons and propagating the signal to a bipolar neuron which will continue the signal. Upload your drawing here in part A, after you have finished parts B and C. B. On the schematic, please label all the parts of the drawn neurons using numbers and define the numbers bellow. C. On the schematic, please label all events happening with letters and provide a detailed description below of each event occurring from the beginning to the end of the process.

2) Which of the following statement is not true of myasthenia gravis? A. There is progressive muscle weakness. B. Emergence of cognitive dysfunctions. C. antibodies bind to the acetylcholine receptors and cause underactivity of acetylcholine. D. Sustained motor activity helps identify the muscle weakness.

3) Emergence of myelin plaques in the CNS white matter could result in the following A Parkinson disease B. cerebral palsy C. multiple sclerosis D. Huntington chorea E. Both A and C F. Both B and D

4) One of the most difficult barriers to permeate is the blood-brain barrier. This highly selective Assignment 1 membrane owes this to A fibroblasts B. specialized capillaries C. microglia D. astrocytes E. oligodendroglia CASD2277 February 6, 2024

5) A patient was diagnosed with a tum or associated with the myelin form ing glia in the PNS. Which of the following cells is likely involved? A astrocyte cells B. microglia cells C. Schwann cells D. oligodendroglia cells E. ependym al cells

Instructions: Need to Do the peer review for the wiki article about 300 - 450 words./n 1/23/24, 9:18 AM <> Code 180D-FW-2023/ Knowledge-Base-Wiki Issues Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Pull requests Actions Introduction Projects Application of Kalman Filter in Neural Signal Decoding Edit New page Hongchang Kuang edited this page 2 weeks ago. 6 revisions Hongchang Kuang Word count: 2610 (including Latex formula and HTML code) Wiki Application of Kalman Filter in Neural Signal Decoding Security https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding R Jump to bottom Have you imagined a world that we could control things without moving our fingers? Will you fancy playing video games or driving your cars just with your brain? I have to tell you that this fantasy is no longer impossile with the help of Brain Machine Interface (BMI). In recent decades, as technology advancing and social ethics system maturing, a heated debate has been triggered over the usage of BMI. Some argues that current innovations are still not applicable for widely marketing products. However, most engineers and neuroscientists are excited by technological progress that facilitates brain mapping, the most sanguine of them comparing their growing ability to tremendous advances that led to the unimaginable success of the BMI projects. Companies like NeuralLink and Paradromics, as well as government agency have invested on the projects to test the possibilities and work on the real world applications [1]. This articles briefly introduces one of the prevailing technology in BMI: the application of Kalman filter in neural signal decoding, discussing its advantages and potential improvements, and what makes it robust and powerful that could possibly allows BMI to enhance our daily life. 1/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Backgrounds in Neuroscience Before we get to the real technique, a simple background in neuroscience is neccessary to unravel you from the potential confusion in the world of jargons, since the mind, with our brain in general, is our main subject of research. The main study theme of neuroscience is to link molecules to mind. Human brain is a highly integrated network of approximately 100 billion individual nerve cells. Therefore, to fully understand the mechanism behind the BMI devices, a brief understanding of how neurons are organized into signaling pathways and how they communicated is necessary. We will introduce how brain functions from three different levels: brain, neuron, and neural signal (action potential). Further readings in related areas are also encouraged. The Brain and Behavior What does our brain look like? Surprisingly, it is complex and ordered at the same time. The central nervous system (CNS) is bilateral and symmetrical. Studies on brain with modern imaging techniques discovers that different regions on brain are specialized for different functions. As you may have gussed, for BMI to decode human cognitive information, the most important part that we would like to focus on is the cerebral cortex, where brain operations that are responsible for human cognitive abilities occur. As shown in the graph, It consists of four anatomically distinct lobes with different functionalities, including planning future actions, hearing, learning, vision, etc. With the concept of functional engineering prevailing in neuroengineering, it is natural to focus on a specific area on cerebral cortex to gather information of neuron activity. Therefore, to achieve fancy operations like controlling the cursor with our mind, for example, we will mainly focus on motor cortex in frontal lobe, which is responsible for planning future actions and the control of movement. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 2/15 1/23/24, 9:18 AM A Neurons B Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Motor cortex (Precentral gyrus) Central sulcus Frontal lobe Lateral sulcus Arcuate fasciculus Broca's area Vocalization region of motor area Parietal lobe Temporal lobe Somatic sensory cortex (Postcentral gyrus) Primary auditory cortex Occipital lobe Angular gyrus Wernicke's area Visual cortex Figure 1-4 The major areas of the cerebral cortex are shown in this lateral view of the of the left hemisphere. A. Outline of the left hemisphere. B. Areas involved in language. Wernicke's area processes the auditory input for language and is important to the understand- ing of speech. It lies near the primary auditory cortex and the angular gyrus, which combines auditory input with information from other senses. Broca's area controls the production of in- telligible speech. It lies near the region of the motor area that controls the mouth and tongue movements that form words. Wernicke's area communicates with Broca's area by a bidirec- tional pathway, part of which is made up of the arcuate fasci- culus. (Adapted from Geschwind 1979.) Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 3/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki What is the basic unit of our brain and what makes it complex? We have to give credit to these simple but powerful biological creatures: nerve cells (neurons). Just like VLSI transistors, even though they have relatively basic morphology and architecture, approximately 10^11 neurons in the brain can support long-ranged and intricate anatomical circuits, where the "complexity" arises from. Neuron have four regions: cell body (soma), dendrites, axon, and presynaptic terminals. The main component axon is what we should pay special attention to: it conveys signals (action potential) to other neurons in long distance (0.1mm - 3m), while ensure the signals propagate without distortion or failure and preserving its shape at very high speed (1 - 100m/s). Compared to transmission line in our real life, the efficiency and accuracy of neural signal transmission are mind-blowing. Two neurons can also communicate at the synapse chemically using neurotransmitters. Neurons, whose structure diagram is attached below, are the most powerful building blocks of the most complicared circuits in the world. https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding 4/15 1/23/24, 9:18 AM Application of Kalman Filter in Neural Signal Decoding 180D-FW-2023/Knowledge-Base-Wiki Wiki Excitatory terminal fiber of an axon -Presynaptic cell- .....Postsynaptic cells- Inhibitory terminal fiber of an axon Axon (initial segment) Node of Ranvier Myelin sheath- Neural Signal (Action Potential) Axon Apical dendrites Cell body -Nucleus Basal dendrites Axon hillock Presynaptic terminal Synaptic cleft -Postsynaptic dendrite Act the brai These s nervous great va on our odorant vey info carry ir other k https://github.com/180D-FW-2023/Knowledge-Base-Wiki/wiki/Application-of-Kalman-Filter-in-Neural-Signal-Decoding convey the for travels patterns creates smell, a To conduc ing she lar inte sulated become nation i ter 9. Ne branch rons. T known called t Source: Principles of Neural Science, Fourth Edition. Eric R. Kandel, James H. Schwartz, Thomas M. Jessell [2] Synapse 5/15

1. Too Technical: It may be challenging for readers without a background in neuroscience or signal processing. Add some signal processing knowledge (overview everyday example). a. Mobile Communications b. Health Monitoring and Medical Imaging c. Automotive Safety and Autonomous Vehicles 2. Focused solely on Kalman Filters: Provide some alternative or similar methods in neural signal decoding and give some reasons/examples why Kalman filters in some cases are the best way to decode the neural signal. 3. Add code and mathematical proof and provide some background knowledge related to Kalman Filters Neural Network, https://www.ibm.com/docs/en/spss-modeler/18.0.0?topic=networks-neural-mod el Math on Kalman filter https://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.p df Code: https://github.com/KordingLab/Neural_Decoding/blob/master/Examples_kf_dec oder.ipynb 4. More practical examples: a. GPS or navigation for ships, control of vehicles, and aircraft(dynamical position) b. Tracking object c. Economics d. Computer Vision Applications 5. State some restrictions or drawbacks on Kalman Filters. a. It assumes that both the system and observation model equations are linear, which is not realistic in many real-life situations. b. It assumes that the state belief is Gaussian distributed.