A new study has shown how the brain’s cognitive maps are used and updated for reasoning, allowing us to make decisions in unfamiliar situations.
Category: neuroscience – Page 458
Large brains, long life spans and elaborate problem-solving in crows makes them surprisingly similar to humans.
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This paper presents a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to achieve deeper cognitive intelligence at your fingertips and beyond. Computational information conservation theory CICT can help us to develop competitive applications and even advanced quantum cognitive computational application and systems towards deep computational cognitive intelligence. CICT new awareness of a discrete HG hyperbolic geometry subspace reciprocal space, RS of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean direct space, DS system surface representation can open the way to holographic information geometry HIG to recover lost coherence information in system description and to develop advanced quantum cognitive systems. This paper is a relevant contribution towards an effective and convenient “Science 2.0” unive.
Upon analysis, researchers found the phototherapy treatments significantly improved MMSE scores in participants with dementia.
The second focus of the study was to see how phototherapy interventions affected the behavioral and psychological symptoms of dementia (BPSDs) — such as depression and agitation — and sleep. The researchers stated there were no significant differences in BPSDs and sleep between the phototherapy and control groups.
As long as people have been alive, they’ve wanted to stay alive. But unlike finding the fountain of youth or becoming a vampire, uploading your brain to a computer or the cloud might actually be possible. Theoretically, we already know how to do it, and Elon Musk is even trying a brain implant with Neuralink. But technically, we have a long way to go. We explain the main technological advancements that we’ll need to make whole brain emulation a reality.
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What it’ll take to upload our brains to a computer.
Until now, it was unclear as to whether drugs that clear amyloid, which accumulate in the brain during aging and accompany diseases such as Alzheimer’s, have any influence over cognitive decline.
Previous studies have aimed to find this out, but results have been inconclusive due to study designs, hard-to-interpret data, and other issues that muddy the waters. March 10-14th saw the 15th International Conference on Alzheimer’s and Parkinson’s Diseases being held (virtually of course), where Dr. Mark Mintun of Eli Lilly presented data that, at least somewhat, affirmatively answers the question [1].
Alzheimer’s disease is a neurodegenerative disease that affects millions of people worldwide. It is characterized by the accumulation of amyloid plaques and disordered protein fibers called tau tangles in the brain, which lead to cognitive impairment and dementia. Scientists have long been trying to understand the underlying mechanisms behind Alzheimer’s disease and find effective treatments for the condition.
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 Introduction.
01:34 — Brief neuroscience background.
06:23 — Topology and the notion of a manifold.
11:48 — Dimension of a manifold.
15:06 — Number of holes (genus)
18:49 — Putting it all together.
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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).
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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 23260–270 (2020).
3. Bernardi, S. et al. The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex. Cell 183954–967.e21 (2020).
4. Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 22289–296 (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 18569–575 (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 102614–635 (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)
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