A tool to discover research papers - Research Rabbit Artem Kirsanov 2021-06-12 | Referral code: WKELe4GIn this video I'll talk about Research Rabbit - a new tool (still in beta testing) which uses AI to help you discover relevant and interesting research articles, tailored specifically to your interests Link to the info-website: www.researchrabbit.aiLink to the app website: www.researchrabbitapp.comOUTLINE:0:00 - Introduction1:20 - Requesting an early access2:25 - Move to the top of the list 2:53 - Two different websites4:18 - Using the website: create a collection5:17 - Different sections 7:36 - Graph visualization8:50 - Return to the starting point9:17 - Workflow conclusion10:00 - Individualised emails10:08 - Outro & ReferencesSocials:VK: vk.com/atpsynthase
Differential Equations: The Language of Change Artem Kirsanov 2024-10-08 | To try everything Brilliant has to offer—free—for a full 30 days, visit brilliant.org/ArtemKirsanov . You’ll also get 20% off an annual premium subscriptionSocials:X/Twitter: https://x.com/ArtemKRSVPatreon: patreon.com/artemkirsanovMy name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).In this video, we explore the fascinating world of dynamical systems and differential equations, powerful tools for understanding how things change over time. We introduce key concepts like state variables, phase portraits, and limit cycles, using intuitive examples such as predator-prey models to visualize complex mathematical ideas.Outline:00:00 Introduction01:44 State Variables04:22 Differential Equations08:24 Numerical solutions11:52 Predator-Prey model15:13 Phase Portraits16:49 Equilibrium points & Stability18:51 Limit Cycles20:19 Conclusion21:25 Sponsor: Brilliant.org22:48 OutroThis video was sponsored by Brilliant
The Key Equation Behind Probability Artem Kirsanov 2024-08-23 | Get 4 months extra on a 2 year plan here: nordvpn.com/artemkirsanov. It’s risk free with Nord’s 30 day money-back guarantee! Socials:X/Twitter: https://x.com/ArtemKRSVPatreon: patreon.com/artemkirsanovMy name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).In this video, we explore the fundamental concepts that underlie probability theory and its applications in neuroscience and machine learning. We begin with the intuitive idea of surprise and its relation to probability, using real-world examples to illustrate these concepts.From there, we move into more advanced topics:1) Entropy – measuring the average surprise in a probability distribution.2) Cross-entropy and the loss of information when approximating one distribution with another. 3) Kullback-Leibler (KL) divergence and its role in quantifying the difference between two probability distributions.OUTLINE:00:00 Introduction02:00 Sponsor: NordVPN04:07 What is probability (Bayesian vs Frequentist)06:42 Probability Distributions10:17 Entropy as average surprisal13:53 Cross-Entropy and Internal models19:20 Kullback–Leibler (KL) divergence20:46 Objective functions and Cross-Entropy minimization24:22 Conclusion & OutroCREDITS:Special thanks to Crimson Ghoul for providing English subtitles!Icons by freepik.com
Generative Model That Won 2024 Nobel Prize Artem Kirsanov 2024-08-13 | Get 20% off at shortform.com/artemIn this video we explore Boltzmann Machines – one of the first generative models that learns probability distribution of data, leveraging stochastic rules and latent representations.Socials:X/Twitter: https://x.com/ArtemKRSVPatreon (to view the extended script): patreon.com/artemkirsanovOUTLINE:00:00 Introduction01:56 Goal of Boltzmann Machines05:26 Boltzmann Distribution13:29 Stochastic Update Rule17:39 Contrastive Hebbian Rule25:41 Hidden Units28:25 Restricted Boltzmann Machines29:38 Conclusion & OutroReferences:1. Ackley, D., Hinton, G. & Sejnowski, T. A learning algorithm for boltzmann machines. Cognitive Science 9, 147–169 (1985).2. Downing, K. L. Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks. (The MIT Press, Cambridge, Massachusetts, 2023).3. Hinton, G. E. & Salakhutdinov, R. R. Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507 (2006).4. Hinton, G. E. A Practical Guide to Training Restricted Boltzmann Machines. in Neural Networks: Tricks of the Trade (eds. Montavon, G., Orr, G. B. & Müller, K.-R.) vol. 7700 599–619 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2012).Special thanks to Crimson Ghoul for providing English subtitles!
A Brain-Inspired Algorithm For Memory Artem Kirsanov 2024-07-03 | Get 20% off at shortform.com/artemIn this video we will explore the concept of Hopfield networks – a foundational model of associative memory that underlies many important ideas in neuroscience and machine learning, such as Boltzmann machines and Dense associative memory.Socials:X/Twitter: https://x.com/ArtemKRSVPatreon: patreon.com/artemkirsanovOUTLINE:00:00 Introduction02:17 Protein folding paradox04:23 Energy definition08:25 Hopfield network architecture14:03 Inference18:40 Learning22:48 Limitations & Perspective24:43 Shortform25:54 OutroReferences:1) Downing, K.L., 2023. Gradient expectations: structure, origins, and synthesis of predictive neural networks. The MIT Press, Cambridge, Massachusetts.2) towardsdatascience.com/hopfield-networks-neural-memory-machines-4c94be8210733) ml-jku.github.io/hopfield-layersSpecial thanks to Crimson Ghoul for providing English subtitles!Credits:Protein folding: youtube.com/shorts/fvBO3TqJ6FE🎵 Music licensed from Lickd. The biggest mainstream and stock music platform for content creatorsViva La Vida by Coldplay, lickd.lnk.to/4aEPvoID License ID: RXj082JWjbATry Lickd FREE for 14 days for unlimited stock music and get 50% off your first mainstream track: app.lickd.co/r/47462149f85b4b6e9660bbe6d9b0f944
The Most Important Algorithm in Machine Learning Artem Kirsanov 2024-04-01 | Shortform link: shortform.com/artemIn this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.OUTLINE:00:00 Introduction01:28 Historical background02:50 Curve Fitting problem06:26 Random vs guided adjustments 09:43 Derivatives14:34 Gradient Descent16:23 Higher dimensions21:36 Chain Rule Intuition27:01 Computational Graph and Autodiff36:24 Summary38:16 Shortform39:20 OutroUSEFUL RESOURCES:Andrej Karpathy's playlist: youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9EJürgen Schmidhuber's blog on the history of backprop:https://people.idsia.ch/~juergen/who-invented-backpropagation.htmlCREDITS:Icons by freepik.com
My Strategy To Consume Information Effectively Artem Kirsanov 2023-09-07 | For a FREE trial and 20% discount to Shortform go to shortform.com/artemAnd to download the Shortform AI browser extension, visit bit.ly/45DCpuMMy name is Artem, I'm a neuroscience PhD student at NYU. In this video I talk about how I use summaries to consume knowledge from books and video lectures.Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVGithub: github.com/ArtemKirsanovOUTLINE:00:00 Introduction01:39 Why summaries are useful04:43 Shortform overview07:58 My workflow with Obsidian11:52 AI summaries of YouTube videos 16:40 Limitations
How I make science animations Artem Kirsanov 2023-08-07 | To try everything Brilliant has to offer—free—for a full 30 days, visit brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.My name is Artem, I'm a neuroscience PhD student at NYU. In this long-requested video I share the creative process behind my videos – what software I use and break down some of my previous animations. Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVGithub: github.com/ArtemKirsanovSpecial thanks to Crimson Ghoul for providing English subtitles!OUTLINE:00:00 Introduction01:09 Software: After Effects02:03 Software: Python modules05:16 Software: Blender05:55 Some helper software7:30 Mathematical animations22:54 3D neuron activity23:58 Surface slicing30:32 3D brains32:50 Branching model40:57 Brilliant42:55 OutroSome links:Manim: https://docs.manim.community/en/stable/Matplotlib: matplotlib.orgBlenderNeuron: blenderneuron.orgBlenderSpike: github.com/ArtemKirsanov/BlenderSpikeBlenderBrain: github.com/ArtemKirsanov/BlenderBrainColormaps addon: github.com/TheJeran/Blender-Colormaps---This video was sponsored by Brilliant
Building Blocks of Memory in the Brain Artem Kirsanov 2023-07-06 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.My name is Artem, I'm a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVOUTLINE:00:00 - Introduction00:39 - Historical background01:44 - Fear conditioning paradigm03:38 - Immediate-early genes as memory markers08:13 - Engrams are necessary and sufficient for recall10:16 - Excitabiliy and memory allocation16:19 - Brain-wide engrams18:12 - Linking memories together24:20 - Summary25:33 - Brilliant27:09 - OutroSpecial thanks to Crimson Ghoul for providing English subtitlesREFERENCES (in no particular order):1. Robins, S. The 21st century engram. WIRES Cognitive Science e1653 (2023) doi:10.1002/wcs.1653.2. Roy, D. S. et al. Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions. Nat Commun 13, 1799 (2022).3. Josselyn, S. A. & Tonegawa, S. Memory engrams: Recalling the past and imagining the future. Science 367, eaaw4325 (2020).4. Chen, L. et al. The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating. Neurobiology of Learning and Memory 173, 107266 (2020).5. Rao-Ruiz, P., Yu, J., Yu, J. J., Kushner, S. A. & Josselyn, S. A. Neuronal competition: microcircuit mechanisms define the sparsity of the engram. Current Opinion in Neurobiology 54, 163–170 (2019).6. Josselyn, S. A. & Frankland, P. W. Memory Allocation: Mechanisms and Function. Annu. Rev. Neurosci. 41, 389–413 (2018).7. Choi, J.-H. et al. Interregional synaptic maps among engram cells underlie memory formation. Science 360, 430–435 (2018).8. Abdou, K. et al. Synapse-specific representation of the identity of overlapping memory engrams. Science 360, 1227–1231 (2018).9. Yokose, J. et al. Overlapping memory trace indispensable for linking, but not recalling, individual memories. Science 355, 398–403 (2017).10. Rashid, A. J. et al. Competition between engrams influences fear memory formation and recall. Science 353, 383–387 (2016).11. Poo, M. et al. What is memory? The present state of the engram. BMC Biol 14, 40 (2016).12. Park, S. et al. Neuronal Allocation to a Hippocampal Engram. Neuropsychopharmacol 41, 2987–2993 (2016).13. Morrison, D. J. et al. Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiology of Learning and Memory 135, 91–99 (2016).14. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. Front. Mol. Neurosci. 8, (2016).15. Josselyn, S. A., Köhler, S. & Frankland, P. W. Finding the engram. Nat Rev Neurosci 16, 521–534 (2015).16. Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83, 722–735 (2014).17. Redondo, R. L. et al. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513, 426–430 (2014).18. Ramirez, S. et al. Creating a False Memory in the Hippocampus. Science 341, 387–391 (2013).19. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484, 381–385 (2012).20. Silva, A. J., Zhou, Y., Rogerson, T., Shobe, J. & Balaji, J. Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Science 326, 391–395 (2009).CREDITS:Icons by biorender.com and freepik.comBrain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)Rat free 3D model: skfb.ly/oEq7yThis video was sponsored by Brilliant
Can We Build an Artificial Hippocampus? Artem Kirsanov 2023-04-30 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov The first 200 of you will get 20% off Brilliant’s annual premium subscription.My name is Artem, I'm a computational neuroscience student and researcher. In this video we discuss the Tolman-Eichenbaum Machine – a computational model of a hippocampal formation, which unifies memory and spatial navigation under a common framework. Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVOUTLINE:00:00 - Introduction01:13 - Motivation: Agents, Rewards and Actions03:17 - Prediction Problem05:58 - Model architecture06:46 - Position module 07:40 - Memory module08:57 - Running TEM step-by-step11:37 - Model performance13:33 - Cellular representations17:48 - TEM predicts remapping laws19:37 - Recap and Acknowledgments20:53 - TEM as a Transformer network21:55 - Brilliant23:19 - OutroREFERENCES:1. Whittington, J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell 183, 1249-1263.e23 (2020).2. Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. Preprint at http://arxiv.org/abs/2112.04035 (2022).3. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022).CREDITS:Special thanks to Crimson Ghoul for providing English subtitles!Icons by biorender.com and freepik.comBrain 3D models were created with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)Animations were made using open-source Python packages Matplotlib and RatInABox ( github.com/TomGeorge1234/RatInABox )Rat free 3D model: skfb.ly/oEq7yThis video was sponsored by Brilliant
How Your Brain Organizes Information Artem Kirsanov 2023-03-31 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanovThe first 200 of you will get 20% off Brilliant’s annual premium subscription.My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about cognitive maps – internal models of outside world that the brain to generate flexible behavior that is generalized across contexts.Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVOUTLINE:00:00 - Introduction02:08 - Edward Tolman03:48 - Zoo of neurons in hippocampal formation06:40 - Non spatial mapping08:21 - Graph formalism12:21 - Latent spaces17:22 - Factorized representations21:51 - Summary24:47 - Brilliant26:19 - OutroREFERENCES (in no particular order):1. Behrens, T. E. J. et al. What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron 100, 490–509 (2018).2. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).3. Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722 (2017).4. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022).5. Whittington, J., Muller, T., Mark, S., Barry, C. & Behrens, T. Generalisation of structural knowledge in the hippocampal-entorhinal system.CREDITS:Special thanks to Crimson Ghoul for providing English subtitles! Icons by biorender.com and freepik.comBrain 3D models were created with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)This video was sponsored by Brilliant
Brain Criticality - Optimizing Neural Computations Artem Kirsanov 2023-03-05 | To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/. The first 200 of you will get 20% off Brilliant’s annual premium subscription.My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about the concept of critical point – how the brain might optimize information processing by hovering near a phase transition. Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVSpecial thanks to Crimson Ghoul for providing English subtitles!OUTLINE:00:00 Introduction01:11 - Phase transitions in nature05:05 - The Ising Model09:33 - Correlation length and long-range communication13:14 - Scale-free properties and power laws20:20 - Neuronal avalanches25:00 - The branching model31:05 - Optimizing information transmission 34:06 - Brilliant.org35:41 - Recap and outroThe book: https://mitpress.mit.edu/9780262544030/the-cortex-and-the-critical-point/REFERENCES (in no particular order):1. Zimmern, V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front. Neural Circuits 14, 54 (2020).2. Beggs, J. M. The criticality hypothesis: how local cortical networks might optimize information processing. Phil. Trans. R. Soc. A. 366, 329–343 (2008).3. Beggs, J. M. The cortex and the critical point: understanding the power of emergence. (The MIT Press, 2022).4. Heffern, E. F. W., Huelskamp, H., Bahar, S. & Inglis, R. F. Phase transitions in biology: from bird flocks to population dynamics. Proc. R. Soc. B. 288, 20211111 (2021).5. Beggs, J. M. & Plenz, D. Neuronal Avalanches in Neocortical Circuits. J. Neurosci. 23, 11167–11177 (2003).6. Avramiea, A.-E., Masood, A., Mansvelder, H. D. & Linkenkaer-Hansen, K. Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality. J. Neurosci. 42, 2221–2233 (2022).7. O’Byrne, J. & Jerbi, K. How critical is brain criticality? Trends in Neurosciences 45, 820–837 (2022).8. Haldeman, C. & Beggs, J. M. Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States. Phys. Rev. Lett. 94, 058101 (2005).9. Beggs, J. M. Being critical of criticality in the brain. Frontiers in Physiology.Derivation that only power laws are scale-free: youtu.be/m6FQqXAHNT8CREDITS:Icons by biorender.comBrain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)Ising model zooming animations: youtu.be/MxRddFrEnPcThis video was sponsored by Brilliant
Dendrites: Why Biological Neurons Are Deep Neural Networks Artem Kirsanov 2023-01-29 | Keep exploring at http://brilliant.org/ArtemKirsanovGet started for free, and hurry—the first 200 people get 20% off an annual premium subscription.My name is Artem, I'm a computational neuroscience student and researcher. In this video we will see why individual neurons essentially function like deep convolutional neural networks, equipped with insane information processing capabilities as well as some of the physiological mechanisms, that account for such computational complexity. Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVOUTLINE:00:00 Introduction01:42 - Perceptrons03:43 - Electrical excitability and action potential07:12 - Cable theory: passive dendrites09:03 - Active dendritic properties12:10 - Human neurons as XOR gates19:11 - Single neurons as deep neural networks22:32 - Brilliant 23:57 - Recap and outroSpecial thanks to Crimson Ghoul for providing English subtitles!REFERENCES (in no particular order):1. Bicknell, B. A., Bicknell, B. A. & Häusser, M. A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron (2021) doi:10.1016/j.neuron.2021.09.044.2. Matthew Larkum. Are dendrites conceptually useful? Neuroscience (2022) doi:10.1016/j.neuroscience.2022.03.008.3. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nature Neuroscience 7, 621–627 (2004).4. Tran-Van-Minh, A. et al. Contribution of sublinear and supralinear dendritic integration to neuronal computations. Frontiers in Cellular Neuroscience 9, 67–67 (2015).5. Gidon, A. et al. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science 367, 83–87 (2020).6. London, M. & Häusser, M. DENDRITIC COMPUTATION. Annu. Rev. Neurosci. 28, 503–532 (2005).7. Branco, T., Clark, B. A. & Häusser, M. Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons. Science 329, 1671–1675 (2010).8. Stuart, G. J. & Spruston, N. Dendritic integration: 60 years of progress. Nat Neurosci 18, 1713–1721 (2015).9. Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).10. Beniaguev, D., Segev, I. & London, M. Single cortical neurons as deep artificial neural networks. Neuron 109, (2021).11. Michalikova, M., Remme, M. W. H., Schmitz, D., Schreiber, S. & Kempter, R. Spikelets in pyramidal neurons: generating mechanisms, distinguishing properties, and functional implications. Reviews in the Neurosciences 31, 101–119 (2019).12. Larkum, M. E., Wu, J., Duverdin, S. A. & Gidon, A. The Guide to Dendritic Spikes of the Mammalian Cortex In Vitro and In Vivo. Neuroscience 489, 15–33 (2022).CREDITS:Icons by biorender.comBrain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)This video was sponsored by Brilliant
A Map of Social Space in Your Brain Artem Kirsanov 2022-12-25 | Shortform link: shortform.com/artemMy name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about how hippocampus serves a "social map", representing information about conspecific individuals at different levels of abstraction. Patreon: patreon.com/artemkirsanovTwitter: twitter.com/ArtemKRSVOUTLINE:00:00 Introduction03:30 Overview of physical place cells04:57 Social information in physical space11:28 Abstract social space15:36 Recap16:12 Shortform17:03 OutroREFERENCES:1. Omer, D. B., Maimon, S. R., Las, L. & Ulanovsky, N. Social place-cells in the bat hippocampus. Science 359, 218–224 (2018).2. Tavares, R. M. et al. A Map for Social Navigation in the Human Brain. Neuron 87, 231–243 (2015).3. Schafer, M. & Schiller, D. Navigating Social Space. Neuron 100, 476–489 (2018).4. Eichenbaum, H. The Hippocampus as a Cognitive Map … of Social Space. Neuron 87, 9–11 (2015).CREDITS:Icons by biorender.com3D model of a bat (CC license) - skfb.ly/6WEYyBrain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (brainglobe.info/atlas-api)
Theta rhythm: A Memory Clock Artem Kirsanov 2022-10-05 | Shortform link: shortform.com/artemMy name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about theta rhythm - a rhythmic pattern of brain activity (4-12 Hz), which is essential for memory encoding and retrieval. Special thanks to Crimson Ghoul for providing English subtitles!REFERENCES (in no particular order):1. Hummos A, Nair SS. An integrative model of the intrinsic hippocampal theta rhythm. Lytton WW, editor. PLoS ONE. 2017 Aug 7;12(8):e0182648. 2. Ferguson KA, Chatzikalymniou AP, Skinner FK. Combining theory, model and experiment to understand how theta rhythms are generated in the hippocampus 2017 3. Drieu C, Zugaro M. Hippocampal Sequences During Exploration: Mechanisms and Functions. Front Cell Neurosci. 2019 Jun 13;13:232. 4. Speers LJ, Cheyne KR, Cavani E, Hayward T, Schmidt R, Bilkey DK. Hippocampal Sequencing Mechanisms Are Disrupted in a Maternal Immune Activation Model of Schizophrenia Risk. J Neurosci. 2021 Aug 11;41(32):6954–65. 5. Foster DJ, Wilson MA. Hippocampal theta sequences. Hippocampus. 2007 Nov;17(11):1093–9. 6. Backus AR, Schoffelen JM, Szebényi S, Hanslmayr S, Doeller CF. Hippocampal-Prefrontal Theta Oscillations Support Memory Integration. Current Biology. 2016 Feb;26(4):450–7. 7. Pastalkova E, Itskov V, Amarasingham A, Buzsáki G. Internally Generated Cell Assembly Sequences in the Rat Hippocampus. Science. 2008 Sep 5;321(5894):1322–7. 8. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. Invariant visual representation by single neurons in the human brain. Nature. 2005 Jun;435(7045):1102–7. 9. Colgin LL. Mechanisms and Functions of Theta Rhythms. Annu Rev Neurosci. 2013 Jul 8;36(1):295–312. 10. Buzsáki G, Moser EI. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat Neurosci. 2013 Feb;16(2):130–8. 11. Buzsaki G. Rhythms of the Brain. 12. Colgin LL. Rhythms of the hippocampal network. Nat Rev Neurosci. 2016 Apr;17(4):239–49. 13. Goutagny R, Jackson J, Williams S. Self-generated theta oscillations in the hippocampus. Nat Neurosci. 2009 Dec;12(12):1491–3. 14. Buzsáki G, Tingley D. Space and Time: The Hippocampus as a Sequence Generator. Trends in Cognitive Sciences. 2018 Oct;22(10):853–69. 15. Buzsáki G. Theta Oscillations in the Hippocampus. Neuron. 2002 Jan;33(3):325–40. 16. Skaggs WE, McNaughton BL, Wilson MA, Barnes CA. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus. 1996;6(2):149–72. 17. Hasselmo ME, Stern CE. Theta rhythm and the encoding and retrieval of space and time. NeuroImage. 2014 Jan;85:656–66. 18. Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E. Theta sequences are essential for internally generated hippocampal firing fields. Nat Neurosci. 2015 Feb;18(2):282–8. 19. Reddy L, Self MW, Zoefel B, Poncet M, Possel JK, Peters JC, et al. Theta-phase dependent neuronal coding during sequence learning in human single neurons. Nat Commun. 2021 Dec;12(1):4839. OUTLINE:00:00 Introduction00:48 Brain waves01:18 Generation of theta rhythm04:37 Functions of theta wave08:13 Forming an integrated representation11:05 Sequential organization14:50 Phase precession17:49 Conclusion18:32 Sponsor message19:36 OutroCREDITS:Icons by biorender.com, flaticon.com
Wavelets: a mathematical microscope Artem Kirsanov 2022-08-15 | Wavelet transform is an invaluable tool in signal processing, which has applications in a variety of fields - from hydrodynamics to neuroscience. This revolutionary method allows us to uncover structures, which are present in the signal but are hidden behind the noise. The key feature of wavelet transform is that it performs function decomposition in both time and frequency domains. In this video we will see how to build a wavelet toolkit step by step and discuss important implications and prerequisites along the way.This is my entry for Summer of Math Exposition 2022 ( #SoME2 ).My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University. Twitter: @artemkrsvOUTLINE:00:00 Introduction01:55 Time and frequency domains03:27 Fourier Transform05:08 Limitations of Fourier08:45 Wavelets - localized functions10:34 Mathematical requirements for wavelets12:17 Real Morlet wavelet 13:02 Wavelet transform overview14:08 Mother wavelet modifications15:46 Computing local similarity18:08 Dot product of functions?21:07 Convolution24:55 Complex numbers27:56 Wavelet scalogram 30:46 Uncertainty & Heisenberg boxes33:16 Recap and conclusionCredits:Special thanks to Crimson Ghoul for providing English subtitles!Vector assets: freepik.com- Microscope vector created by freepik -freepik.com/vectors/microscope- Lab room vector created by upklyak: freepik.com/vectors/lab-room- Semaphore vector created by macrovector: freepik.com/vectors/semaphoreMathematical animations were done using manim (https://docs.manim.community/en/stable/) and matplotlib python libraries. 3D animations were done in Blender
Self-study computational neuroscience | Coding, Textbooks, Math Artem Kirsanov 2022-06-06 | Shortform link: shortform.com/artemThis video is based on the article medium.com/neurotechx/how-to-get-started-in-computational-neuroscience-dde4b1817ccdMy name is Artem, I'm a computational neuroscience student and researcher. In this video I share my experience on getting started with computational neuroscience. We will talk about programming languages, learning to code, recommended textbooks and finding project ideas to practice with.Related video links:Discovering research papers: youtu.be/Z9C4mPR0uI4How to read papers effectively: youtu.be/4yOm4kklKIADirect practice: youtu.be/NuTU9YFqZqQSpecial thanks to Crimson Ghoul for providing English subtitles!----------Coding platform: codeforces.comTextbooks:- amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174- amazon.com/Principles-Neural-Science-Fifth-Kandel/dp/0071390111- amazon.com/Brain-Inside-Out-Gy-C3-B6rgy-Buzs-C3-A1ki-dp-0190905387/dp/0190905387- amazon.com/Theoretical-Neuroscience-Computational-Mathematical-Modeling/dp/0262541858- amazon.com/Dynamical-Systems-Neuroscience-Excitability-Computational/dp/0262514206Neuroscience textbook with python exercises: https://neuronaldynamics.epfl.ch/Linear algebra:youtube.com/playlist?list=PL49CF3715CB9EF31Dyoutube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_abOpen datasets:openneuro.org/datasetsneuromorpho.org---------OUTLINE:0:00 Introduction0:46 What is computational neuroscience4:24 Necessary skills5:42 Choosing programming language6:50 Algorithmic thinking8:36 Ways to practice coding9:43 General neuroscience books11:08 Computational neuroscience books13:00 Mathematics resources & pitfalls15:40 Looking of project ideas18:59 Finding data to practice with19:29 Final adviseCREDITS:Icons by flaticon.comB-roll footage by pexels.com
Logarithmic nature of the brain 💡 Artem Kirsanov 2022-05-16 | Shortform link: shortform.com/artemMy name is Artem, I'm a computational neuroscience student and researcher. In this video we will talk about the fundamental role of lognormal distribution in neuroscience. First, we will derive it through Central Limit Theorem, and then explore how it support brain operations on many scales - from cells to perception. Special thanks to Crimson Ghoul for providing English subtitles!REFERENCES:1.Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat Rev Neurosci 15, 264–278 (2014).2.Ikegaya, Y. et al. Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity. Cerebral Cortex 23, 293–304 (2013).3.Loewenstein, Y., Kuras, A. & Rumpel, S. Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo. Journal of Neuroscience 31, 9481–9488 (2011).4.Morales-Gregorio, A., van Meegen, A. & van Albada, S. J. Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex. http://biorxiv.org/lookup/doi/10.1101/2022.03.17.480842 (2022) doi:10.1101/2022.03.17.480842.OUTLINE:00:00 Introduction01:15 What is Normal distribution03:03 Central Limit Theorem04:23 Normality in biology05:46 Derivation of lognormal distribution10:20 Division of labour in the brain12:20 Generalizer and specialist neurons13:37 How lognormality arises15:19 Conclusion16:00 Shortform: sponsor message16:54 OutroCREDITS:Icons by biorender.comMathematical animations were created using Manim CE python library - https://www.manim.community/
How to overcome study procrastination | 3 powerful tips Artem Kirsanov 2022-04-19 | Shortform link: shortform.com/artemIn this video I'll talk about 3 amazing tips, which help you to overcome study procrastination. My name is Artem, I'm a computational neuroscience student and researcher. Socials:Twitter: twitter.com/ArtemKRSVOUTLINE:00:00 Introduction00:53 Shortform message01:55 Why we procrastinate03:10 Yin Yang technique06:10 Time-based mindset08:05 Spacing out 10:38 Overview & conclusionCREDITS:Video with protein folding: youtube.com/watch?v=2dV5s6v2v8QNobel prize video: youtube.com/watch?v=amHjoeqM-1MIcons by flaticon.comB-roll footage by pexels.com
Your brain is moving along the surface of the torus 🤯 Artem Kirsanov 2022-04-05 | Shortform link: shortform.com/artemIn this video we will explore a very interesting paper published in Nature in 2022, which describes the hidden torus in the neuronal activity of cells in the entorhinal cortex, known as grid cells. Place cell video: youtube.com/watch?v=iV-EMA5g288&t=158sNeural manifolds video: youtube.com/watch?v=QHj9uVmwA_0My name is Artem, I'm a computational neuroscience student and researcher. Socials:Twitter: twitter.com/ArtemKRSVSpecial thanks to Crimson Ghoul for providing English subtitles!REFERENCES:1.Gardner, R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022).2.Pisokas, I., Heinze, S. & Webb, B. The head direction circuit of two insect species. eLife 9, e53985 (2020).3.Shilnikov, A. L. & Maurer, A. P. The Art of Grid Fields: Geometry of Neuronal Time. Front. Neural Circuits 10, (2016).4.Moser, M.-B., Rowland, D. C. & Moser, E. I. Place Cells, Grid Cells, and Memory. Cold Spring Harb Perspect Biol 7, a021808 (2015).5.Lewis, M., Purdy, S., Ahmad, S. & Hawkins, J. Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells. http://biorxiv.org/lookup/doi/10.1101/436352 (2018) doi:10.1101/436352.OUTLINE:00:00 Introduction00:48 Shortform message01:41 Disclaimer 02:32 Grid cells & modules04:48 How data was acquired05:38 Toroidal coordinates06:40 Invariance of the torus07:43 Continuous attractor network08:10 OutroCREDITS:Footage of mouse running on a torus: Helmet and the Norwegian University of Science and Technology's Kavli Institute for Systems Neuroscience eurekalert.org/multimedia/814124Thumbnail illustration: by Helmet and the Kavli Institute for Systems NeuroscienceSome video footage taken from: "Edvard and May-Britt Moser: A journey into entorhinal cortex" by NTNU University ( youtube.com/watch?v=jYCR0pQLd_U )Icons by biorender.comB-roll footage by pexels.com
My university note-taking | Zettelkasten & more Artem Kirsanov 2022-03-18 | Visit http://brilliant.org/ArtemKirsanov to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription.In this video I talk about my effective note-taking workflow I use in my university. We will talk about why "conventional" notes are ineffective and how instead you can combine Zettelkasten and flashcard note-taking techniques to achieve peak academic performance and gain deep understandingMy name is Artem, I'm a computational neuroscience student and researcher at Moscow State University.Socials:Twitter: twitter.com/ArtemKRSVStudy about intentional forgetting:Eskritt M, Ma S. Intentional forgetting: note-taking as a naturalistic example. Mem Cognit. 2014 Feb;42(2):237-46. doi: 10.3758/s13421-013-0362-1. PMID: 24014168.OUTLINE:00:00 Introduction00:51 Sponsor message02:12 Why I don't take conventional notes07:49 Fleeting notes09:03 Embedding notes10:37 Zettelkasten notesB-roll footage by pexels.comThis video was sponsored by Brilliant
How to REMEMBER what you read 🧠 Artem Kirsanov 2022-03-07 | Shortform link: shortform.com/artemHow do you remember everything you read?In this video I talk about how you can use a spaced repetition platform Anki (apps.ankiweb.net) to remember what you read, by turning books and papers into flashcardsMy name is Artem, I'm a computational neuroscience student and researcher at Moscow State University.Socials:Twitter: twitter.com/ArtemKRSVAugmenting long-term memory article:http://augmentingcognition.com/ltm.htmlOUTLINE 00:00 - Introduction01:21 - Memory decay & how to fight it03:23 - Why memorise at all? - Bloom's taxonomy04:42 - Shortform message05:51 - Creating effective flashcards 09:01 - Conclusion & outroB-roll footage by pexels.com
Memory Consolidation: Time Machine of the Brain Artem Kirsanov 2022-02-16 | Visit brilliant.org/ArtemKirsanov to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription.How does sleep help us remember things better?In this video we will explore the wonders of the hippocampus - a brain region involved in memory consolidation and spatial navigation. We will talk about the phenomenon of hippocampal replay and the Sharp Wave Ripples.My name is Artem, I'm a computational neuroscience student and researcher at Moscow State University.Socials:Twitter: twitter.com/ArtemKRSVSpecial thanks to Crimson Ghoul for providing English subtitles!OUTLINE:00:00 Introduction01:01 Sponsor message02:23 Cortex and Hippocampus04:52 Sharp Wave Ripple06:45 Fast-reverse memory replay08:55 Fast-forward replay10:03 Conclusion and OutlookREFERENCES:1.Ólafsdóttir, H. F., Bush, D. & Barry, C. The Role of Hippocampal Replay in Memory and Planning. Current Biology 28, R37–R50 (2018).2.Hannah R. Joo & Loren M. Frank. The hippocampal sharp wave-ripple in memory retrieval for immediate use and consolidation. Nature Reviews Neuroscience (2018) doi:10.1038/s41583-018-0077-1.3.Buzsáki, G. The brain from inside out. (Oxford University Press, 2019).4.Roux, L., Hu, B., Eichler, R., Stark, E. & Buzsáki, G. Sharp wave ripples during learning stabilize the hippocampal spatial map. Nat Neurosci 20, 845–853 (2017).5.Girardeau, G., Benchenane, K., Wiener, S. I., Buzsáki, G. & Zugaro, M. B. Selective suppression of hippocampal ripples impairs spatial memory. Nat Neurosci 12, 1222–1223 (2009).6.Feld, G. B. & Born, J. Sculpting memory during sleep: concurrent consolidation and forgetting. Current Opinion in Neurobiology 44, 20–27 (2017).7.Ambrose, R. E., Pfeiffer, B. E. & Foster, D. J. Reverse Replay of Hippocampal Place Cells Is Uniquely Modulated by Changing Reward. Neuron 91, 1124–1136 (2016).8.Marcelo G. Mattar & Nathaniel D. Daw. Prioritized memory access explains planning and hippocampal replay. Nature Neuroscience (2018) doi:10.1038/s41593-018-0232-z.9.Jens G. Klinzing, Niels Niethard, & Jan Born. Mechanisms of systems memory consolidation during sleep. Nature Neuroscience (2019) doi:10.1038/s41593-019-0467-3.10.Penelope A. Lewis & Daniel Bendor. How Targeted Memory Reactivation Promotes the Selective Strengthening of Memories in Sleep. Current Biology (2019) doi:10.1016/j.cub.2019.08.019.11.Wenbo Tang, Justin D. Shin, Loren M. Frank, & Shantanu P. Jadhav. Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake Compared with Sleep States. The Journal of Neuroscience (2017) doi:10.1523/jneurosci.2291-17.2017.12.Buzsáki, G. Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073–1188 (2015).13.Diba, K. & Buzsáki, G. Forward and reverse hippocampal place-cell sequences during ripples. Nat Neurosci 10, 1241–1242 (2007).14.Shin, J. D., Tang, W. & Jadhav, S. P. Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and Memory-Guided Decision Making. Neuron 104, 1110-1125.e7 (2019).15.Mehta, M. R. Cortico-hippocampal interaction during up-down states and memory consolidation. Nat Neurosci 10, 13–15 (2007).16.Aussel, A., Buhry, L., Tyvaert, L. & Ranta, R. A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations. J Comput Neurosci 45, 207–221 (2018).-------B roll footage by pexels.comIcons by biorender.com-------This video was sponsored by Brilliant
Zettelkasten workflow for research papers | Zotero & Obsidian link Artem Kirsanov 2022-01-31 | Shortform link: shortform.com/artemPlugin links:Better BibText for Zotero:https://retorque.re/zotero-better-bibtex/installation/Citations for Obsidian: github.com/hans/obsidian-citation-plugin---------My name is Artem, I’m a computational neuroscience student and researcher. In this video I talk about how I use Zotero together with Obsidian to efficiently turn research papers into Zettelkasten notes.Socials:Twitter: twitter.com/ArtemKRSV---------OUTLINE:00:00 Introduction01:14 Zotero organization05:03 Linking Zotero to Obsidian06:48 Inserting a paper link 08:11 Literature notes12:49 Final words-------B roll footage by pexels.comIcons by flaticon.com
How to Effectively Teach Yourself ANYTHING Artem Kirsanov 2022-01-06 | Shortform link: shortform.com/artem---------My name is Artem, I’m a computational neuroscience student and researcher. In this video I talk about some fundamental principles behind effective self-studying. It is based to a large extent on the book "Ultralearning" by Scott Young. I also share with your my own observations and experience OUTLINE:00:00 Introduction01:00 Shortform02:12 Definition of Ultralearning02:48 Metalearning06:16 Focus06:58 Directness11:32 Drill12:44 Conclusion-------B roll footage by pexels.comIcons by flaticon.com
Place cells: How your brain creates maps of abstract spaces Artem Kirsanov 2021-12-02 | In this video, we will explore the positional system of the brain - hippocampal place cells. We will see how it relates to contextual memory and mapping of more abstract featuresOUTLINE:00:00 Introduction00:53 Hippocampus1:27 Discovery of place cells2:56 3D navigation3:51 Role of place cells4:11 Virtual reality experiment7:47 Remapping11:17 Mapping of non-spatial dimension13:36 Conclusion _____________REFERENCES: 1) Anderson, M.I., Jeffery, K.J., 2003. Heterogeneous Modulation of Place Cell Firing by Changes in Context. J. Neurosci. 23, 8827–8835. doi.org/10.1523/JNEUROSCI.23-26-08827.20032) Aronov, D., Nevers, R., Tank, D.W., 2017. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722. doi.org/10.1038/nature216923) Bostock, E., Muller, R.U., Kubie, J.L., 1991. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1, 193–205. doi.org/10.1002/hipo.4500102074) Christopher D. Harvey, Forrest Collman, Daniel A. Dombeck, David W. Tank, 2009. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature. doi.org/10.1038/nature084995) Grieves, R.M., Jedidi-Ayoub, S., Mishchanchuk, K., Liu, A., Renaudineau, S., Jeffery, K.J., 2020. The place-cell representation of volumetric space in rats. Nat Commun 11, 789. doi.org/10.1038/s41467-020-14611-76) Jeffery, K.J., 2011. Place Cells, Grid Cells, Attractors, and Remapping. Neural Plasticity 2011, 1–11. doi.org/10.1155/2011/1826027) Latuske, P., Kornienko, O., Kohler, L., Allen, K., 2018. Hippocampal Remapping and Its Entorhinal Origin. Front. Behav. Neurosci. 11, 253. doi.org/10.3389/fnbeh.2017.002538) Laura Lee Colgin, Edvard I. Moser, May-Britt Moser, 2008. Understanding memory through hippocampal remapping. Trends in Neurosciences. doi.org/10.1016/j.tins.2008.06.0089) Meredith Minear, Tesalee K. Sensibaugh, 2020. Neuroscience of Navigation. doi.org/10.1002/9781119458555.ch5310) Moita, M.A.P., 2004. Putting Fear in Its Place: Remapping of Hippocampal Place Cells during Fear Conditioning. Journal of Neuroscience 24, 7015–7023. doi.org/10.1523/JNEUROSCI.5492-03.200411) O’Keefe, J., Burgess, N., 1996. Geometric determinants of the place fields of hippocampal neurons. Nature 381, 425–428. doi.org/10.1038/381425a012) Robinson, N.T.M., Descamps, L.A.L., Russell, L.E., Buchholz, M.O., Bicknell, B.A., Antonov, G.K., Lau, J.Y.N., Nutbrown, R., Schmidt-Hieber, C., Häusser, M., 2020. Targeted Activation of Hippocampal Place Cells Drives Memory-Guided Spatial Behavior. Cell 183, 1586-1599.e10. doi.org/10.1016/j.cell.2020.09.06113) Wohlgemuth, M.J., Yu, C., Moss, C.F., 2018. 3D Hippocampal Place Field Dynamics in Free-Flying Echolocating Bats. Front. Cell. Neurosci. 12, 270. doi.org/10.3389/fncel.2018.00270Icons by flaticon.com and biorender.com_______Socials:VK: vk.com/atpsynthase
My simple note-taking setup | Zettelkasten in Obsidian | Step-by-step guide Artem Kirsanov 2021-11-03 | In this video I show you my simple yet powerful setup in Obsidian for taking Zettelkasten notes (which plugins I recommend using, my tagging system, etc)I also show a step-by-step guide how to set it up yourselfOUTLINE:00:00 Introduction2:14 How Obsidian works and main definitions4:00 My 4-folder organisation 5:13 How to set everything up from scratch7:55 Sliding Panes & Admonition plugins8:28 Templates10:08 Creating notes12:25 Tagging & classifying notes13:16 - Maps of content (MOCs)14:40 - Final words 16:07 - OutroSocials:VK: vk.com/atpsynthase---------B-roll footage by: pexels.comMusic by: pixabay.comIconds by: flaticon.com
How to read papers effectively | Research reading technique Artem Kirsanov 2021-10-09 | In this video I talk about how to approach reading a research article, how papers are structured, in what order to read and how to highlight themOUTLINE: 00:00 Introduction1:06 Wrong approach1:41 Have a clear goal3:37 Inner filter4:54 Structure of a paper6:31 In what order to read8:56 Effective highlighting 10:31 Further processing 10:59 Outro---------Socials:VK: vk.com/atpsynthase---------B-roll footage by pexels.com
Mind mapping tutorial for students | Tips & Software Artem Kirsanov 2021-09-18 | In this video I talk about what are mind maps, why they work and how to properly create one.OUTLINE:0:00 Introduction0:47 Why mind maps work2:49 What is a mind map?3:09 Rules for creating3:15 1) Hierarchical 3:34 2) Colorful3:50 3) Use of images4:43 4) Brief and valuable5:39 5) Less than five divisions6:05 My Ipad workflow7:36 Outro Socials:VK: vk.com/atpsynthase
How to choose a note-taking app | Zettelkasten | Notion vs Roam vs Obsidian Artem Kirsanov 2021-09-10 | In this video I discuss most popular applications for Zettelkasten note-taking, what I recommend to get started and how to approach any new application that might come out in the future OUTLINE:0:00 Introduction1:55 Mindset for note-taking software5:51 Notion7:55 Roam research8:15 Remnote8:45 Obsidian9:49 Take home messageSocials:VK: vk.com/atpsynthase
Neural manifolds - The Geometry of Behaviour Artem Kirsanov 2021-08-22 | This video is my take on 3B1B's Summer of Math Exposition (SoME) competition It explains in pretty intuitive terms how ideas from topology (or "rubber geometry") can be used in neuroscience, to help us understand the way information is embedded in high-dimensional representations inside neural circuits OUTLINE:00:00 Introduction01:34 - Brief neuroscience background06:23 - Topology and the notion of a manifold11:48 - Dimension of a manifold15:06 - Number of holes (genus)18:49 - Putting it all togetherSpecial thanks to Crimson Ghoul for providing English subtitles!____________Main paper: Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A. & Fiete, I. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat Neurosci 22, 1512–1520 (2019)._________________________Other relevant references:1.Jazayeri, M. & Ostojic, S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. arXiv:2107.04084 [q-bio] (2021).2.Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat Neurosci 23, 260–270 (2020).3.Bernardi, S. et al. The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex. Cell 183, 954-967.e21 (2020).4.Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 22, 289–296 (2019).5.Remington, E. D., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5 (2018).6.Low, R. J., Lewallen, S., Aronov, D., Nevers, R. & Tank, D. W. Probing variability in a cognitive map using manifold inference from neural dynamics. http://biorxiv.org/lookup/doi/10.1101/418939 (2018) doi:10.1101/418939.7.Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat Commun 7, 13239 (2016).8.Peyrache, A., Lacroix, M. M., Petersen, P. C. & Buzsáki, G. Internally organized mechanisms of the head direction sense. Nat Neurosci 18, 569–575 (2015).9.Dabaghian, Y., Mémoli, F., Frank, L. & Carlsson, G. A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology. PLoS Comput Biol 8, e1002581 (2012).10.Yu, B. M. et al. Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity. Journal of Neurophysiology 102, 614–635 (2009).11.Singh, G. et al. Topological analysis of population activity in visual cortex. Journal of Vision 8, 11–11 (2008).The majority of animations in this video were made using Manim - an open source python library (github.com/ManimCommunity/manim) and brainrender (github.com/brainglobe/brainrender)Icons by flaticon.com and biorender.comRelevant resources:Definition of a hole:mathworld.wolfram.com/Hole.htmlCup-doughnut transformation: youtube.com/watch?v=4iHjt2OvqagVoltage traces and spike trains were obtained from atrificially simulated neurons using BRIAN2 python package:Stimberg, M, Brette, R, Goodman, DFM. “Brian 2, an Intuitive and Efficient Neural Simulator.” eLife 8 (2019): e47314. doi: 10.7554/eLife.47314.Generated spike trains were later analysed to obtain rate curves with the help of ELEPHANT:Denker M, Yegenoglu A, Grün S (2018) Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 2018, P19. doi: 10.12751/incf.ni2018.0019________Socials:VK: vk.com/atpsynthase#SoME1
How to focus when studying from home Artem Kirsanov 2021-07-27 | In this video I'll tell you practical tips that I use on a daily basis to stay focused during my study sessions.OUTLINE:0:00 - Introduction1:00 - Part 1: Things to do before1:20 - Bring back the spark2:03 - Gradually increase the focus3:58 - Dedicate a time frame 4:36 - Productive environment 5:45 - Behavioural and environmental hooks8:08 - Part 2: Maintain the focus9:36 - Eliminate distractions: Phone11:06 - Eliminate distractions: Computer15:32 - Part 3: After the session16:06 - Short breaks16:51 - Big block of active rest17:39 - Flowchart & conclusions Socials: VK: vk.com/atpsynthaseEmail: ArtemKirsanov2606@gmail.com
Your brain CANT Multitask - Heres why Artem Kirsanov 2021-07-16 | This video explores what attention really is, what role it plays in learning and why people can't multitask - the issue of attention residue OUTLINE:0:00 - Sneak peek0:20 - Introduction0:57 - Why we need attention1:46 - Thalamus as attentional filter3:06 - Higher attentional systems3:40 - Role of attention in learning4:42 - Attention residue6:00 - Conclusions and referencesSocials:VK: vk.com/atpsynthase
Interleaving vs Spaced repetition | Study hacks Artem Kirsanov 2021-07-02 | In this video I'll tell you about interleaving - a very easy and simple trick, which can immensely boost your learning of any subjectOUTLINE:0:00 - Introduction0:55 - What is interleaving?2:02 - Spaced repetition vs Interleaving3:04 - Strategy choice5:06 - Difference detection and abstraction6:35 - How to implement7:46 - Conclusion and referencesSocials:VK: vk.com/atpsynthase
Understanding note-taking | Zettelkasten Artem Kirsanov 2021-06-23 | In this video I'll talk about a Zettelkasten note-taking system, invented by Niklas Luhmann and why it works, so that you can take inspiration from these principles and incorporate them into your own workflow. OUTLINE:0:00 - Introduction2:13 - What is Zettelkasten?4:01 - Emergent5:55 - Dynamic6:23 - Personalized7:12 - Interlinking8:29 - Externalization8:55 - Clarity10:40 - Investment in future11:52 - ConclusionSocials:VK: vk.com/atpsynthase
Biological Clock - Breakthrough junior challenge 2018 Artem Kirsanov 2018-07-01 | This is my #breakthroughjuniorchallenge video about Biological ClockP.S. Physiology or Medicine Nobel Prize 2017 was awarded for research in this topic c:
