When it comes to courtship, it is important to ensure that one is interacting with a member of the same species. Animals use multiple sensory systems to confirm that potential mates are indeed suitable, with acoustic communication playing an important role in their decision making.
Although these differences have previously been reported at the behavioral level, it is not known how the neuronal circuitry underlying this decision-making has diverged between species. Now, in a new publication in Scientific Reports, a research group at Nagoya University in Japan has investigated how the auditory processing pathway has evolved and diverged between fruit fly species.
Males of several species of Drosophila (fruit flies), which are regularly used in neuroscience research, vibrate their wings rhythmically during courtship, producing a courtship song. The temporal components of these songs differ between species, allowing female flies to distinguish between potential mates.
This article is based on accredited medical, science, and media reports. Disclaimer: I am not a scientist. I will share knowledge but will offer no personal opinion on this matter herein.
All listed theories and facts shared within this article are fully-attributed to said outlets, includingWikipedia.org, NeuroscienceNews.com, and TheDailyBrief.com.
The origins and workings of consciousness have remained among science’s greatest unanswered mysteries. How did it begin? What sparks it?
Neurologist Christopher Walsh discovered genes that direct cerebral cortex development. We asked him what they reveal about intelligence, psychiatric disorders, and the nature of being human.
This article was produced for The Kavli Prize by Scientific American Custom Media, a division separate from the magazine’s board of editors.
Keep exploring at http://brilliant.org/ArtemKirsanov/ Get 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.
OUTLINE: 00:00 Introduction. 01:42 — Perceptrons. 03:43 — Electrical excitability and action potential. 07:12 — Cable theory: passive dendrites. 09:03 — Active dendritic properties. 12:10 — Human neurons as XOR gates. 19:11 — Single neurons as deep neural networks. 22:32 — Brilliant. 23:57 — Recap and outro.
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 7621–627 (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. 28503–532 (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 503115–120 (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 31101–119 (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).
Abstract: Recent years have seen a rapid expansion of empirical and theoretical studies in connectomics – the emerging science of structural and functional brain networks. In this talk I will survey some of the recent advances and a few of the challenges for connectomics research, with an emphasis on human brain connectivity. Of particular interest are studies that employ network science methods for analyzing and modeling connectivity patterns. These studies have shown the existence of highly connected hub regions that play crucial roles in brain communication and the integration of information. Future applications of brain modeling and computation for understanding brain function and dysfunction will also be discussed. Overall, the new field of connectomics offers a unique opportunity for building a theoretical understanding of the function of the human brain.
A scan of the skull of a 319-million-year-old fossilized fish has led to the discovery of the oldest example of a well-preserved vertebrate brain, shining a new light on the early evolution of bony fish.
The fossil of the skull belonging to the extinct Coccocephalus wildi was found in a coal mine in England more than a century ago, according to researchers of the study published in the journal Nature on Wednesday.
The fossil is the only known specimen of the fish species so scientists from the University of Michigan in the US and the University of Birmingham in the UK used the nondestructive imaging technique of computed tomography (CT) scanning to look inside its skull and examine its internal bodily structure.
Sponsor: AG1, The nutritional drink I’m taking for energy and mental focus. Tap this link to get a year’s supply of immune-supporting vitamin D3-K2 & 5 travel packs FREE with your first order: https://www.athleticgreens.com/arvinash.
RECOMMENDED READING: Schwartz, “Quantum Field Theory and the Standard model” https://amzn.to/3HmWdYt.
CHAPTERS: 0:00 The most important motion in the universe. 1:08 How get energy and mental focus. 2:20 A spring: Classical simple harmonic oscillator. 4:48 QUANTUM Harmonic oscillator. 6:00 Science Asylum — what is the Schrodinger equation? 7:30 Quantum Field Theory (QFT) uses spring math! 10:00 Intuitive description of what’s going on! 12:37 What is really oscillating in QFT?
When we look at a rainbow, we see a full spectrum of light. Every colour we could imagine. Except one – magenta. Where is it? Well, officially magenta doesn’t exist. There is no wavelength of light for magenta, meaning the human brain literally makes it up, but how? Video by Archie Crofton Narrated by Lotte Rice Commissioned by Paul Ivan Harris Follow BBC Reel on Twitter, Instagram, Facebook and YouTube.
The built-in ion sensory technology mildly excites the taste buds on your tongue like they’ve never been stimulated before! The immediate results are enhanced flavor, heightened taste, and improved aftertaste. SpoonTEK science combines the power of advanced electronics with tongue sensory and the brain for an amazing eating experience. It’s not just any spoon—it’s the only spoon you will need to take your taste to the next level.
Neural decoding models attempt to identify the current mental state of an individual from recordings of their neural activity1. In recent years, neural decoders have been developed to identify numerous different types of mental activity from many neuroimaging modalities. These decoders were first developed to decode visual2,3 and semantic4,5,6,7 information from the brain, while more recent examples of neural decoders have been developed to decode a diverse set of activities, including, but not limited to, affective states8, visual imagery during sleep9, and story meaning10.
Neural decoding models have been developed that make use of many different types of neuroimaging techniques including, but not limited to, functional magnetic resonance imaging (fMRI), electrocortiography (ECoG), electroencephalogram (EEG), and functional near infrared spectroscopy (fNIRS). Depending on the type of neuroimaging technique the neural decoder uses different types of mental processes may be decoded. For example, fMRI provides a recording of activity throughout the entire brain with a very high spatial resolution, allowing a neural decoder the ability to decode mental states involving sub-cortical brain regions11. However, this comes at the cost of poor time resolution, which prevents decoding of mental activity over very short time scales.
