Are there organisms with fewer than 1000 neurons?

Are there organisms with fewer than 1000 neurons?

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I'm developing neural networks comprised of just 3 to 10 layers of virtual neurons and I'm curious to know if there are any insect brains out there with fewer than a thousand neurons?

  • Are there any tiny creatures with small numbers of neurons?
  • Do neuronal maps exist for those simple nervous systems?

Short answer
As far as I know, a complete neural map (a connectome) is only available for the roundworm C. elegens, a nematode with only 302 neurons (fig. 1).

Fig. 1. C. elegans (left, size: ~1 mm) and connectome of C. elegans (right).
sources: Utrecht University & Farber (2012)

Looking at the least complex of animals will be your best bet and nematodes (roundworms) like Caenorhabditis elegans are definitely a good option. C. elegans has some 300 neurons. Below is a schematic of phyla in Fig.2.

You mention insects; these critters are much more complex than roundworms. The total number of neurons varies with each insect, but for comparison: one of the lesser complex insects like the fruit fly Drosophila already has around 100k neurons, while a regular honey bee has about one million (source: Bio Teaching).

Complexity of the organism is indeed an indicator of the number of neurons to be expected. Sponges, for instance (Fig. 1) have no neurons at all, so the least complex of animals won't help you. the next in line are the Cnidaria (Fig. 2). The Cnidaria include the jelly fish, and for example Hydra vulgaris has 5.6k neurons.

So why do jelly fish feature more neurons? Because size also matters. Hydra vulgaris can grow up 15 mm, while C. elegans grows only up to 1 mm. See the wikipedia page for an informative list of #neurons in a host of species.

A decent neuronal connectivity map (a connectome) only exists for C. elegans (Fig. 1) as far as I know, although other maps (Drosophila (Meinertzhagen, 2016) and human) are underway.

- Farber, Sci Am February 2012
- Meinertzhagen, J Neurogenet (2016); 30(2): 62-8

Fig. 2. Phyla within the kingdom of animalia. source: Southwest Tennessee University College

The organism you are looking for is the nematode C. elegans, which always has the same number of neurons, 302, and has been fully mapped, see WormWeb or you can chase original publications from there. C. elegans is particularly suited for this kind of work because it has a constant number of cells which divide in an entirely predictable order and its neurons forms predictable connections. Larger organisms, such as flies, have a variable number of cells and their neurons do not form precisely predictable connections. The immense amount of knowledge about C. elegans, advanced genetic manipulation techniques, and a transparent body also helps.

I am not aware of any insects with such small brains, even a fruitfly has several orders of magnitude more.

I believe there are types of water snail with 8 distinct neurons in a ganglia, there's a bit of information here: The cell bodies of the neurons are massive, visible under a standard dissecting microscope, so they were popular among early electrophysiologists. I guess there are probably more neurons around the snail, but it's certainly one of the simplest "brains" around…

Re: insect brain size

Following article has a good summary - in short insects' nervous systems range from 7400 to 850000 neurons:

There may be some hope for parasitic insects, e.g. Dicopomorpha echmepterygis where male has neither wings nor eyes, it is not inconceivable for their brains to be simpler.

Brain Neurons and Synapses

If you need to perform at your best, need to focus, problem-solve or maintain a calm and clear mindset, you will get a huge benefit from taking Mind Lab Pro.


  • Better focus
  • Calm mindset
  • 55+ memory and mood
  • Performance focused athletes
  • Student learning

Concept in Action

Watch this video of biologist Mark Kirschner discussing the “flipping” phenomenon of vertebrate evolution.

The nervous system is made up of neurons, specialized cells that can receive and transmit chemical or electrical signals, and glia, cells that provide support functions for the neurons by playing an information processing role that is complementary to neurons. A neuron can be compared to an electrical wire—it transmits a signal from one place to another. Glia can be compared to the workers at the electric company who make sure wires go to the right places, maintain the wires, and take down wires that are broken. Although glia have been compared to workers, recent evidence suggests that also usurp some of the signaling functions of neurons.

There is great diversity in the types of neurons and glia that are present in different parts of the nervous system. There are four major types of neurons, and they share several important cellular components.


Neurons are the cells that transmit information in an animal's nervous system so that it can sense stimuli from its environment and behave accordingly. Not all animals have neurons Trichoplax and sponges lack nerve cells altogether.

Neurons may be packed to form structures such as the brain of vertebrates or the neural ganglions of insects.

The number of neurons and their relative abundance in different parts of the brain is a determinant of neural function and, consequently, of behavior.

All numbers for neurons (except Caenorhabditis and Ciona), and all numbers for synapses (except Ciona) are estimations.

The cerebral cortex is a structure of particular interest at the intersection between comparative neuroanatomy and comparative cognitive psychology. Historically, it had been assumed that since only mammals have a cerebral cortex, only they benefit from the information processing functions associated with it, notably awareness and thought. [57] It is now known that non-avian reptiles also have a cerebral cortex and that birds have a functional equivalent called the dorsal ventricular ridge (DVR), which in fact appears to be a modification subsequent to the reptilian cortex. A modern understanding of comparative neuroanatomy now suggests that for all vertebrates, the pallium roughly corresponds to this general sensory-associative structure. [58] It is also a widely accepted view that arthropods and closely related worms have an equivalent structure, the corpora pedunculata, more commonly known as mushroom bodies. In fact this structure in invertebrates and the pallium in vertebrates may have a common evolutionary origin from a common ancestor. [59]

Given the apparent function of the sensory-associative structure, it has been suggested that the total number of neurons in the pallium or its equivalents may be the best predictor of intelligence when comparing species, being more representative than total brain mass or volume, brain-to-body mass ratio, or encephalization quotient (EQ). [1] It may thus be reasonably assumed that the total number of neurons in an animal's corresponding sensory-associative structure strongly relates to its degree of awareness, breadth and variety of subjective experiences, and intelligence. [1]

The methods used to arrive at the numbers in this list include neuron count by isotropic fractionator, optical fractionator or estimation based on correlations observed between number of cortical neurons and brain mass within closely related taxa. Isotropic fractionation is often considered more straightforward and reliable than optical fractionation which may yield both overestimates and underestimates. [60] Estimation based on brain mass and taxon is to be considered the least reliable method.


We used synergistic miRNA discovery to analyze 94 human sRNA-seq datasets, yielding 2,469 novel miRNA candidates. These were each supported by a typical RNA hairpin structure and an approximately 22-nucleotide sRNA mapping to the hairpin in accordance with Dicer processing and detected in at least two sequencing experiments. In addition, we characterized the novel miRNA candidates in more detail in two cell systems. In a neuronal cell system, we found that our candidates responded similarly to known miRNAs when components of the biogenesis pathways were knocked down or when the cells were induced to differentiate.

We used public data from a human kidney cell line to show that comparable numbers of novel and known miRNAs interact with key proteins DGCR8, Ago1 and Ago2, in hairpin positions that conform with miRNA biogenesis. The abundance of novel sequences bound to Ago1 and Ago2 suggests that they did not just undergo chance interactions with the biogenesis machinery, but were indeed incorporated by the effector proteins. Last, evidence from CLASH data showed that novel miRNAs had canonical binding to target mRNAs. The interaction strength and seed recognition resembled those of known miRNAs but not random sequences, as would be expected if Argonaute incorporation was spurious. Several of the novel miRNAs interacted with multiple mRNAs in the kidney cells, and appeared to form part of regulatory networks.

While these particular two cell systems do not give saturated coverage of all novel miRNA candidates, we have no reason to doubt that experiments in other cell systems would yield similar positive results. In sum, we have provided additional evidence for the biogenesis of 1,098 novel miRNA candidates (Additional file 4: Figure S6). We have thus presented compelling evidence that the number of human miRNA genes is larger than anticipated at over three thousand genes.

When enriching our novel miRNA candidates with the first described custom miRNA capture system, we showed that they responded similarly to known miRNAs, but not tRNAs and rRNAs, during induced cell differentiation. This suggests that the novel miRNAs were not the results of leaky transcription, but were closely linked to regulatory processes. Further, the SureSelect capture system shows great promise: it strongly enriched for target sRNAs while being fully quantitative. At low-pass sequencing, it improved detection of targets (Figure 6a,b) and at saturated sequencing it improved the profiling depth of targets (Figure 6c-f). With some maturation, a custom miRNA capture system could be used to profile dozens of miRNA samples on an Illumina miSeq instrument in less than one day. This clearly has potential clinical applications with rapid processing of patient sample sets.

Overall, our novel candidates have features similar to known miRNAs, in particular we note that they interacted with Argonaute effector proteins and displayed typical targeting sequence characteristics. The specific and low expression levels of the novel candidates were expected, because there is a strong discovery bias favoring abundant transcripts. The apparent low expression in tissues does not exclude the possibility that some of the novel miRNAs may be highly expressed and have important functions in specific cell types [21]. This is an appealing hypothesis because the novel candidate miRNAs are overrepresented in human brain, which is known to harbor a vast diversity of neuronal cell types. Thus our catalog may provide a valuable resource as the small RNA field enters the single-cell era, facilitating the evaluation of specific physiological conditions of gene expression at the cellular level, which is tightly regulated by miRNAs.

Last, in this study we have presented evidence of the biogenesis of our novel human miRNAs. However, biogenesis does not necessarily imply biological function that confers an adaptive advantage. It is conceivable that hairpins may enter the miRNA biogenesis pathways but have insubstantial impact on the transcriptome because they are lowly expressed or do not recruit the necessary co-factors [43]. In fact, our population genetic studies suggest that many, but likely not all, of our novel human miRNAs are under selection pressure. In general, it is not is easy to discern if a given miRNA has a function. miRNA biochemical function can be validated using reporter assays that express transcripts at physiological levels, but this is extremely time consuming. Deeply conserved miRNAs are likely to be functional, but the reverse does not necessarily hold, as there are examples of species-specific miRNAs with well-defined functions [44]. We think that it is important that miRNA annotations are saturated to ensure that future studies will pick up sequences which change expression patterns during development or in disease, in tissues or in single cells. These miRNAs can then be subjected to careful functional assays. Saturating the miRNA annotations risks diluting out the deeply conserved and well-studied sequences deposited there, but this can easily be avoided by stratifying the sequences according to confidence. miRBase has already curated a ‘core annotation’ of miRNAs with compelling evidence for biogenesis [45], and a recent study has identified a subset of sequences supported by functional evidence [46]. Similarly, we have stratified our novel miRNA candidates into five confidence levels based on the evidence presented in our study (Additional file 3: Table S2), enabling researchers to decide their own levels of stringency.

To investigate if other species harbor large numbers of undiscovered miRNAs, we repeated the prediction in mouse, using public sequencing data of a comparable volume to the data used in human, compiled from 11 distinct studies. This yielded 1,520 novel mouse miRNA candidates (unpublished results). Interestingly, this is one-third fewer than the number reported in human, although the mouse data has excellent coverage of tissues, including samples from brain and from several developmental time points [41]. Revisiting the human data with simulations, we found that the number of reported candidates scale almost linearly with the amount of data analyzed (Figure 7), suggesting that human miRNA discovery has not yet reached saturation, even with our added set. This shows that many more miRNAs remain to be discovered, both in well-studied model organisms and in human.

Saturation of novel miRNA prediction. To assess the influence of data magnitude on the analyses, saturation curves of the 94 datasets were performed. (a) Saturation curve of sequencing depth, from 10% to 100% of reads retained. For each dataset this percentage of (randomly chosen) reads were retained and subsequently the miRNA prediction analysis was repeated. The total number of reported novel miRNAs (brown) or high-confidence novel miRNAs (orange) is shown. The number of known miRNAs that are detected by simple sequence matches is shown in green. (b) As before, except that entire datasets rather than individual reads were discarded or retained.

Learning and Memory

The information below was adapted from OpenStax Biology 35.2

One of the key functions that the brain performs is the process of learning and memory. Learning is the ability to acquire new knowledge, and memory is the ability to recall it later. Learning and memory involve both specific brain structures as well as certain neuronal processes. The current hypothesis states that specific neurons in the cerebral cortex are responsible for physically storing memories, and that learning and memory are mediated by both chemical and structural changes in the synapses of these neurons.

Short-term memories are thought to be stored in the prefrontal cortex (part of the frontal lobe). The hippocampus in the temporal lobe is essential for consolidating these short-term memories into long-term memories, but the memories are not actually stored in the hippocampus. The precise location of memory storage is unknown, but it is though that different components of memories may be stored in different locations within the cerebral cortex, and that retrieval of long-term memories may involved the prefrontal cortex.

Storage and access are only half of the story for learning and memory the other half is chemical and structural changes in synapses, or neural plasticity: formation of new and loss of existing neural connections. By the end of embryogenesis in humans, half of all embryonic neurons undergo programmed cell death, and half of the initial synapses are lost. This basic neural architecture is then continually remodeled during the individual’s lifetime. How does neural plasticity relate to learning and memory? Chemical and structural changes in synapses (synaptic plasticity, synaptic pruning, synaptogenesis) mediate access to and strength of these memories as follows:

  • Neurogenesis, or the growth of new neurons. At one time, scientists believed that people were born with all the neurons they would ever have, but research performed during the last few decades indicates that neurogenesis, the birth of new neurons, continues into adulthood. Neurogenesis was first discovered in songbirds that produce new neurons while learning songs. For mammals, new neurons also play an important role in learning: about 1000 new neurons develop in the hippocampus (a brain structure involved in learning and memory) each day. While most of the new neurons will die, researchers found that an increase in the number of surviving new neurons in the hippocampus correlated with how well rats learned a new task. Interestingly, both exercise and some antidepressant medications also promote neurogenesis in the hippocampus. Stress has the opposite effect.
  • Synaptogenesis, or the growth of new synapses between two existing neurons, and synaptic pruning, or the destruction of existing synapses between two neurons.
  • Synaptic plasticity, or the strengthening or weakening of existing synaptic connections. Two processes in particular, long-term potentiation (LTP) and long-term depression (LTD) are important forms of synaptic plasticity that occur in synapses in the hippocampus, a brain region that is involved in storing memories.
    • Long-term potentiation (LTP) is the long-term strengthening of a synaptic connection. LTP is based on the idea that “cells that fire together wire together.” There are various mechanisms underlying synaptic strengthening seen with LTP, including an increase in the amount of neurotransmitter released by the presynaptic neuron, and an increased response to the same amount of neurotransmitter by the postsynaptic neuron. LTP can result in sensitization, where there is an increased response to the same external stimulus.
    • Long-term depression (LTD) is essentially the reverse of LTP: it is a long-term weakening of a synaptic connection. While it may seem counterintuitive, LTD may be just as important for learning and memory as LTP: the weakening unused synapses allows for unimportant connections to be lost and makes the synapses that have undergone LTP that much stronger by comparison. LTD can result in habituation, where there is a decreased response to the same external stimulus.

    Long term potentiation can occur after repeated stimulation at a synaptic terminal (panel 1) via several mechanisms, including production of more neurotransmitter receptors on the postsynaptic neuron (panel 2) and production of more neurotransmitter molecules by the presynaptic neuron (panel 3). A stronger connection between the neurons (panel 4) will occur as a result of either of these changes. Image credit: modification of work by Tomwsulcer – Own work, CC0,

    This video provides a simplified overview of learning and memory in a commonly used model organism for studying these processes:

    And finally, this video provides a succinct overview of two of the common results of learning, sensitization or habituation:


    Electronic supplementary material is available online at

    Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.


    . 2010 Two views of brain function . Trends Cogn. Sci. 14, 180-190. (doi:10.1016/j.tics.2010.01.008) Crossref, PubMed, ISI, Google Scholar

    Friston KJ, Frith CD, Dolan RJ, Price CJ, Zeki S, Ashburner JT, Penny W.

    2004 Human brain function. Oxford, UK: Elsevier. Google Scholar

    . 1989 Structure and function of the normal testis and epididymis . J. Am. Coll. Toxicol. 8, 457-471. (doi:10.3109/10915818909014532) Crossref, Google Scholar

    Nieschlag E, Behre HM, Nieschlag S

    . 2010 Physiology of testicular function. In Andrology: male reproductive health and dysfunction (eds GF Weinbaver, CM Luetjens, M Simoni, E Nieschlag), pp. 1-629. Berlin, Germany: Springer. Crossref, Google Scholar

    Guo J, Zhu P, Wu C, Yu L, Zhao S, Gu X

    . 2003 In silico analysis indicates a similar gene expression pattern between human brain and testis . Cytogenet. Genome Res. 103, 58-62. (doi:10.1159/000076290) Crossref, PubMed, ISI, Google Scholar

    Guo JH, Huang Q, Studholme DJ, Wu CQ, Zhao SY

    . 2005 Transcriptomic analyses support the similarity of gene expression between brain and testis in human as well as mouse . Cytogenet. Genome Res. 111, 107-109. (doi:10.1159/000086378) Crossref, PubMed, ISI, Google Scholar

    Arden R, Gottfredson LS, Miller G, Pierce A

    . 2009 Intelligence and semen quality are positively correlated . Intelligence 37, 277-282. (doi:10.1016/j.intell.2008.11.001) Crossref, ISI, Google Scholar

    2017 Male factor infertility and risk of multiple sclerosis: a register-based cohort study . Mult. Scler. J. 24, 1835-1842. (doi:10.1177/1352458517734069) Crossref, PubMed, Google Scholar

    Fode M, Krogh-jespersen S, Brackett NL, Ohl DA, Lynne CM, Sønksen J

    . 2012 Male sexual dysfunction and infertility associated with neurological disorders . Asian J. Androl. 14, 61-68. (doi:10.1038/aja.2011.70) Crossref, PubMed, ISI, Google Scholar

    . 2019 Basic neural units of the brain: neurons, synapses and action potential. arXiv. ( Google Scholar

    . 2017 Glial cells and their function in the adult brain: a journey through the history of their ablation . Front. Cell Neurosci. 11, 1-17. (doi:10.3389/fncel.2017.00024) Crossref, PubMed, ISI, Google Scholar

    . 1984 Organization and morphogenesis of the human seminiferous epithelium . Cell Tissue Res. 237, 395-407. (doi:10.1007/BF00228424) Crossref, PubMed, ISI, Google Scholar

    Svechnikov K, Landreh L, Weisser J, Izzo G, Colón E, Svechnikov I, Söder O

    et al. 2010 Origin, development and regulation of human leydig cells . Horm. Res. Paediatr. 73, 93-101. (doi:10.1159/000277141) Crossref, PubMed, ISI, Google Scholar

    Kıray H, Lindsay SL, Hosseinzadeh S, Barnett SC

    . 2016 The multifaceted role of astrocytes in regulating myelination . Exp. Neurol. 283, 541-549. (doi:10.1016/j.expneurol.2016.03.009) Crossref, PubMed, ISI, Google Scholar

    Fu C, Rojas T, Chin AC, Cheng W, Bernstein IA, Albacarys LK, Wright WW, Snyder SH

    . 2018 Multiple aspects of male germ cell development and interactions with Sertoli cells require inositol hexakisphosphate kinase-1 . Sci. Rep. 8, 1-13. (doi:10.1038/s41598-018-25468-8) Crossref, PubMed, ISI, Google Scholar

    . 2018 Tissue-specific profiling of oxidative stress-associated transcriptome in a healthy mouse model . Int. J. Mol. Sci. 19, 3174. (doi:10.3390/ijms19103174) Crossref, ISI, Google Scholar

    Falkowska A, Gutowska I, Goschorska M, Nowacki P

    . 2015 Energy metabolism of the brain, including the cooperation between astrocytes and neurons, especially in the context of glycogen metabolism . Int. J. Mol. Sci. 16, 25 959-25 981. (doi:10.3390/ijms161125939) Crossref, ISI, Google Scholar

    . 2004 Lactate and energy metabolism in male germ cells . Trends Endocrinol. Metab. 15, 345-350. (doi:10.1016/j.tem.2004.07.003) Crossref, PubMed, ISI, Google Scholar

    Rato L, Alves MG, Socorro S, Duarte AI, Cavaco JE, Oliveira PF

    . 2012 Metabolic regulation is important for spermatogenesis . Nat. Rev. Urol. 9, 330-338. (doi:10.1038/nrurol.2012.77) Crossref, PubMed, ISI, Google Scholar

    Pitts MW, Kremer PM, Hashimoto AC, Torres DJ, Byrns CN, Williams CS, Berry MJ

    . 2015 Competition between the brain and testes under selenium-compromised conditions: insight into sex differences in selenium metabolism and risk of neurodevelopmental disease . J. Neurosci. 35, 15 326-15 338. (doi:10.1523/JNEUROSCI.2724-15.2015) Crossref, ISI, Google Scholar

    Kabuto H, Amakawa M, Shishibori T

    . 2004 Exposure to bisphenol A during embryonic/fetal life and infancy increases oxidative injury and causes underdevelopment of the brain and testis in mice . Life Sci. 74, 2931-2940. (doi:10.1016/j.lfs.2003.07.060) Crossref, PubMed, ISI, Google Scholar

    Zhao Z, Nelson AR, Betsholtz C, Zlokovic BV

    . 2015 Establishment and dysfunction of the blood-brain barrier . Cell 163, 1064-1078. (doi:10.1016/j.cell.2015.10.067) Crossref, PubMed, ISI, Google Scholar

    Mital P, Hinton BT, Dufour JM

    . 2011 The blood-testis and blood-epididymis barriers are more than just their tight junctions1 . Biol. Reprod. 84, 851-858. (doi:10.1095/biolreprod.110.087452) Crossref, PubMed, ISI, Google Scholar

    Crawford MA, Broadhurst CL, Ghebremeskel K, Sinclair AJ, Saugstad LF, Schmidt WF, Sinclair AJ, Cunnane SC

    . 2008 The role of docosahexaenoic and arachidonic acids as determinants of evolution and hominid brain development . In Fish Glob Welf Environ 5th World Fish Congr , pp. 57-76. Tokyo, Japan: JSFS. Google Scholar

    Lenzi A, Gandini L, Maresca V, Rago R, Sgrò P, Dondero F, Picardo M

    . 2000 Fatty acid composition of spermatozoa and immature germ cells . Mol. Hum. Reprod. 6, 226-231. (doi:10.1093/molehr/6.3.226) Crossref, PubMed, ISI, Google Scholar

    Davidoff MS, Middendorff R, Köfüncü E, Müller D, Ježek D, Holstein AF

    . 2002 Leydig cells of the human testis possess astrocyte and oligodendrocyte marker molecules . Acta Histochem. 104, 39-49. (doi:10.1078/0065-1281-00630) Crossref, PubMed, ISI, Google Scholar

    Schulze W, Davidoff MS, Holstein AF

    . 1987 Are Leydig cells of neural origin? Substance P-like immunoreactivity in human testicular tissue . Acta Endocrinol (Copenh). 115, 373-377. (doi:10.1530/acta.0.1150373) Crossref, PubMed, Google Scholar

    Davidoff MS, Schulze W, Middendorff R, Holstein AF

    . 1993 The Leydig cell of the human testis: a new member of the diffuse neuroendocrine system . Cell Tissue Res. 271, 429-439. (doi:10.1007/BF02913725) Crossref, PubMed, ISI, Google Scholar

    Davidoff MS, Middendorff R, Pusch W, Müller D, Wichers S, Holstein AF

    . 1999 Sertoli and Leydig cells of the human testis express neurofilament triplet proteins . Histochem. Cell Biol. 111, 173-187. (doi:10.1007/s004180050347) Crossref, PubMed, ISI, Google Scholar

    . 2016 Cytoskeleton molecular motors: structures and their functions in neuron . Int. J. Biol. Sci. 12, 1083-1092. (doi:10.7150/ijbs.15633) Crossref, PubMed, ISI, Google Scholar

    . 2017 Kinesins in spermatogenesis† . Biol. Reprod. 96, 267-276. (doi:10.1095/biolreprod.116.144113) Crossref, PubMed, ISI, Google Scholar

    Liu XA, Rizzo V, Puthanveettil SV

    . 2012 Pathologies of axonal transport in neurodegenerative diseases . Transl. Neurosci. 3, 355-372. Crossref, PubMed, ISI, Google Scholar

    Zhang Y, Ou Y, Cheng M, Shojaei Saadi H, Thundathil JC, van der Hoorn FA

    . 2012 KLC3 is involved in sperm tail midpiece formation and sperm function . Dev. Biol. 366, 101-110. (doi:10.1016/j.ydbio.2012.04.026) Crossref, PubMed, ISI, Google Scholar

    Brands A, Münzel PA, Bock KW

    . 2000 In situ hybridization studies of UDP-glucuronosyltransferase UGT1A6 expression in rat testis and brain . Biochem. Pharmacol. 59, 1441-1444. (doi:10.1016/S0006-2952(00)00274-4) Crossref, PubMed, ISI, Google Scholar

    . 1995 Testis-brain RNA-binding protein, a testicular translational regulatory RNA-binding protein, is present in the brain and binds to the 3′ untranslated regions of transported brain mRNAs1 . Biol. Reprod. 53, 707-717. (doi:10.1095/biolreprod53.3.707) Crossref, PubMed, ISI, Google Scholar

    Ibberson M, Riederer BM, Uldry M, Guhl B, Roth J, Thorens B

    . 2002 Immunolocalization of GLUTX1 in the testis and to specific brain areas and vasopressin-containing neurons . Endocrinology 143, 276-284. (doi:10.1210/endo.143.1.8587) Crossref, PubMed, ISI, Google Scholar

    Maeda K, Inui S, Tanaka H, Sakaguchi N

    . 1999 A new member of the α4-related molecule (α4-b) that binds to the protein phosphatase 2A is expressed selectively in the brain and testis . Eur. J. Biochem. 264, 702-706. (doi:10.1046/j.1432-1327.1999.00571.x) Crossref, PubMed, Google Scholar

    Marazziti D, Gallo A, Golini E, Matteoni R, Tocchini-Valentini GP

    . 1998 Molecular cloning and chromosomal localization of the mouse Gpr37 gene encoding an orphan G-protein-coupled peptide receptor expressed in brain and testis . Genomics 53, 315-324. (doi:10.1006/geno.1998.5433) Crossref, PubMed, ISI, Google Scholar

    Mayer H, Bauer H, Breuss J, Ziegler S, Prohaska R

    . 2001 Characterization of rat LANCL1, a novel member of the lanthionine synthetase C-like protein family, highly expressed in testis and brain . Gene 269, 73-80. (doi:10.1016/S0378-1119(01)00463-2) Crossref, PubMed, ISI, Google Scholar

    Tanja O, Facchinetti P, Rose C, Bonhomme MC, Gros C, Schwartz JC

    . 2000 Neprilysin II: a putative novel metalloprotease and its isoforms in CNS and testis . Biochem. Biophys. Res. Commun. 271, 565-570. (doi:10.1006/bbrc.2000.2664) Crossref, PubMed, ISI, Google Scholar

    Yamamoto H, Ochiya T, Takahama Y, Ishii Y, Osumi N, Sakamoto H, Terada M

    . 2000 Detection of spatial localization of Hst-1/Fgf-4 gene expression in brain and testis from adult mice . Oncogene 19, 3805-3810. (doi:10.1038/sj.onc.1203752) Crossref, PubMed, ISI, Google Scholar

    Danielsson A, Djureinovic D, Fagerberg L, Hallstro B, Ponte F, Lindskog C, Uhlén M, Pontén F

    . 2014 The human testis-specific proteome defined by transcriptomics and antibody-based profiling . Mol. Hum. Reprod. 20, 476-488. (doi:10.1093/molehr/gau018) Crossref, PubMed, ISI, Google Scholar

    Liu T-Y, Huang HH, Wheeler D, Xu Y, Wells JA, Song YS, Wiita AP

    . 2017 Time-resolved proteomics extends ribosome profiling-based measurements of protein synthesis dynamics . Cell Syst. 4, 636-644. e9. (doi:10.1016/j.cels.2017.05.001) Crossref, PubMed, ISI, Google Scholar

    Wilda M, Bächner D, Zechner U, Kehrer-Sawatzki H, Vogel W, Hameister H

    . 2000 Do the constraints of human speciation cause expression of the same set of genes in brain, testis, and placenta? Cytogenet. Cell Genet. 91, 300-302. (doi:10.1159/000056861) Crossref, PubMed, Google Scholar

    Khaitovich P, Enard W, Lachmann M, Pääbo S

    . 2006 Evolution of primate gene expression . Nat. Rev. Genet. 7, 693-702. (doi:10.1038/nrg1940) Crossref, PubMed, ISI, Google Scholar

    . 2019 A hotspot for new genes . Elife 8, 8-10. Crossref, ISI, Google Scholar

    . 2013 Failed sperm development as a reproductive isolating barrier between species . Evol. Dev. 15, 458-465. (doi:10.1111/ede.12054) Crossref, PubMed, ISI, Google Scholar

    . 2011 De novo origin of human protein-coding genes . PLoS Genet. 7, 11. Crossref, ISI, Google Scholar

    2018 Human-specific NOTCH2NL genes expand cortical neurogenesis through delta/notch regulation . Cell 173, 1370-1384.e16. (doi:10.1016/j.cell.2018.03.067) Crossref, PubMed, ISI, Google Scholar

    . 2005 Comparing the human and chimpanzee genomes: searching for needles in a haystack . Genome Res. 15, 1746-1758. (doi:10.1101/gr.3737405) Crossref, PubMed, ISI, Google Scholar

    . 1971 Testicular changes in association with malformation of TFIE central nervous system and mental retardation . Acta Pathol. Microbiol. Scand. Pathol. 79A, 249-256. (doi:10.1111/j.1699-0463.1971.tb01816.x) Google Scholar

    2002 Mutation of ARX causes abnormal development of forebrain and testes in mice and X-linked lissencephaly with abnormal genitalia in humans . Nat. Genet. 32, 359-369. (doi:10.1038/ng1009) Crossref, PubMed, ISI, Google Scholar

    Dragatsis I, Levine MS, Zeitlin S

    . 2000 Inactivation of Hdh in the brain and testis results in progressive neurodegeneration and sterility in mice . Nat. Genet. 26, 300-306. (doi:10.1038/81593) Crossref, PubMed, ISI, Google Scholar

    Mascaro JS, Hackett PD, Rilling JK

    . 2013 Testicular volume is inversely correlated with nurturing-related brain activity in human fathers . Proc. Natl Acad.Sci. USA 110, 15 746-15 751. (doi:10.1073/pnas.1305579110) Crossref, ISI, Google Scholar

    . 2004 The sperm, a neuron with a tail: ‘neuronal’ receptors in mammalian sperm . Biol. Rev. Camb. Philos. Soc. 79, 713-732. (doi:10.1017/S1464793103006407) Crossref, PubMed, ISI, Google Scholar

    Ren X, Chen X, Wang Z, Wang D

    . 2017 Is transcription in sperm stationary or dynamic? J. Reprod. Dev. 63, 439-443. (doi:10.1262/jrd.2016-093) Crossref, PubMed, ISI, Google Scholar

    . 2013 Neuronal gap junctions: making and breaking connections during development and injury . Trends Neurosci. 36, 227-236. (doi:10.1016/j.tins.2012.11.001) Crossref, PubMed, ISI, Google Scholar

    . 2002 Snares and munc18 in synaptic vesicle fusion . Nat. Rev. Neurosci. 3, 641-653. (doi:10.1038/nrn898) Crossref, PubMed, ISI, Google Scholar

    Michaut M, De Blas G, Tomes CN, Yunes R, Fukuda M, Mayorga LS

    . 2001 Synaptotagmin VI participates in the acrosome reaction of human spermatozoa . Dev. Biol. 235, 521-529. (doi:10.1006/dbio.2001.0316) Crossref, PubMed, ISI, Google Scholar

    Tomes CN, Michaut M, De BG, Visconti P, Matti U, Mayorga LS

    . 2002 SNARE complex assembly is required for human sperm acrosome reaction . Dev. Biol. 243, 326-338. (doi:10.1006/dbio.2002.0567) Crossref, PubMed, ISI, Google Scholar

    Hutt DM, Cardullo RA, Baltz JM, Ngsee JK

    . 2002 Synaptotagmin VIII is localized to the mouse sperm head and may function in acrosomal exocytosis1 . Biol. Reprod. 66, 50-56. (doi:10.1095/biolreprod66.1.50) Crossref, PubMed, ISI, Google Scholar

    Pierce A, Miller G, Arden R, Gottfredson LS

    . 2009 Why is intelligence correlated with semen quality? Commun. Integr. Biol. 2, 1-3. (doi:10.4161/cib.2.5.8716) PubMed, Google Scholar

    Harper CV, Cummerson JA, White MRH, Publicover SJ, Johnson PM

    . 2008 Dynamic resolution of acrosomal exocytosis in human sperm . J. Cell Sci. 121, 2130-2135. (doi:10.1242/jcs.030379) Crossref, PubMed, ISI, Google Scholar

    Ritta MN, Calamera JC, Bas DE

    . 1998 Occurrence of GABA and GABA receptors in human spermatozoa . Mol. Hum. Reprod. 4, 769-773. (doi:10.1093/molehr/4.8.769) Crossref, PubMed, ISI, Google Scholar

    Bray C, Son J-H, Kumar P, Harris JD, Meizel S

    . 2002 A role for the human sperm glycine receptor/Cl − channel in the acrosome reaction initiated by recombinant ZP31 . Biol. Reprod. 66, 91-97. (doi:10.1095/biolreprod66.1.91) Crossref, PubMed, ISI, Google Scholar

    Baccetti B, Burrini AG, Collodel GC, Falugi C, Moretti E, Piomboni P

    . 1995 Localisation of two classes of acetylcholine receptor-like molecules in sperms of different animal species . Zygote 3, 207-217. (doi:10.1017/S0967199400002604) Crossref, PubMed, ISI, Google Scholar

    Ramírez-Reveco A, Villarroel-Espíndola F, Rodríguez-Gil JE, Concha II

    . 2017 Neuronal signaling repertoire in the mammalian sperm functionality . Biol. Reprod. 96, 505-524. (doi:10.1095/biolreprod.116.144154) Crossref, PubMed, ISI, Google Scholar

    Schulz DJ, Baines RA, Hempel CM, Li L, Liss B, Misonou H

    . 2006 Cellular excitability and the regulation of functional neuronal identity: from gene expression to neuromodulation . J. Neurosci. 26, 10 362-10 367. (doi:10.1523/JNEUROSCI.3194-06.2006) Crossref, ISI, Google Scholar

    Jagannathan S, Publicover SJ, Barratt CLR

    . 2002 Voltage-operated calcium channels in male germ cells . Reproduction 123, 203-215. (doi:10.1530/rep.0.1230203) Crossref, PubMed, ISI, Google Scholar

    Darszon A, Labarca P, Nishigaki T, Espinosa F

    . 1999 Ion channels in sperm physiology . Physiol. Rev. 79, 481-510. (doi:10.1152/physrev.1999.79.2.481) Crossref, PubMed, ISI, Google Scholar

    Darszon A, Nishigaki T, Beltran C, Treviño CL

    . 2011 Calcium channels in the development, maturation, and function of spermatozoa . Physiol. Rev. 91, 1305-1355. (doi:10.1152/physrev.00028.2010) Crossref, PubMed, ISI, Google Scholar

    . 2007 Key role of calcium signaling in synaptic transmission . Neurophysiology 39, 248-250. (doi:10.1007/s11062-007-0034-5) Crossref, ISI, Google Scholar

    Brini M, Calì T, Ottolini D, Carafoli E

    . 2014 Neuronal calcium signaling: function and dysfunction . Cell. Mol. Life Sci. 71, 2787-2814. (doi:10.1007/s00018-013-1550-7) Crossref, PubMed, ISI, Google Scholar

    . 2009 Egg coat proteins activate calcium entry into mouse sperm via CATSPER channels1 . Biol. Reprod. 80, 1092-1098. (doi:10.1095/biolreprod.108.074039) Crossref, PubMed, ISI, Google Scholar

    . 2018 CatSper: a unique calcium channel of the sperm flagellum . Curr. Opin. Physiol. 2, 109-113. (doi:10.1016/j.cophys.2018.02.004) Crossref, PubMed, ISI, Google Scholar

    Publicover S, Harper CV, Barratt C

    . 2007 [Ca 2+ ]i signalling in sperm: making the most of what you've got . Nat. Cell. Biol. 9, 235-242. (doi:10.1038/ncb0307-235) Crossref, PubMed, ISI, Google Scholar

    Amoako AA, Marczylo TH, Marczylo EL, Elson J, Willets JM, Taylor AH, Konje JC

    . 2013 Anandamide modulates human sperm motility: implications for men with asthenozoospermia and oligoasthenoteratozoospermia . Hum. Reprod. 28, 2058-2066. (doi:10.1093/humrep/det232) Crossref, PubMed, ISI, Google Scholar

    Castillo P, Younts T, Chávez A, Hashimotodani Y

    . 2013 Endocannabinoid signaling and synaptic function . Neuron 76, 70-81. (doi:10.1016/j.neuron.2012.09.020) Crossref, ISI, Google Scholar

    Koch S, Acebron SP, Koch S, Acebron SP, Herbst J, Hatiboglu G, Niehrs C

    . 2015 Post-transcriptional Wnt signaling governs epididymal sperm maturation post-transcriptional Wnt signaling . Cell 163, 1225-1236. (doi:10.1016/j.cell.2015.10.029) Crossref, PubMed, ISI, Google Scholar

    . 2013 WNT signaling in neuronal maturation and synaptogenesis . Front. Cell. Neurosci. 7, 1-11. (doi:10.3389/fncel.2013.00103) Crossref, PubMed, ISI, Google Scholar

    Silva JV, Cabral M, Correia R, Carvalho P, Sousa M, Oliveira PF, Fardilha M

    . 2019 mTOR signaling pathway regulates sperm quality in older men . Cell 8, 1-13. (doi:10.3390/cells8060629) ISI, Google Scholar

    . 2014 mTOR signaling and its roles in normal and abnormal brain development . Front. Mol. Neurosci. 7, 1-12. (doi:10.3389/fnmol.2014.00028) Crossref, PubMed, ISI, Google Scholar

    Santiago J, Vieira Silva J, Fardilha M

    . 2019 First insights on the presence of the unfolded protein response in human spermatozoa . Int. J. Mol. Sci. 20, 1-16. (doi:10.3390/ijms20215518) Crossref, ISI, Google Scholar

    Chaerkady R, Kerr CL, Marimuthu A, Kelkar DS, Kashyap MK, Gucek M, Gearhart JD, Pandey A

    . 2009 Temporal analysis of neural differentiation using quantitative proteomics . J. Proteome Res. 8, 1315-1326. (doi:10.1021/pr8006667) Crossref, PubMed, ISI, Google Scholar

    Dammer EB, Duong DM, Diner I, Gearing M, Feng Y, Lah JJ, Levey AI, Seyfried NT

    . 2013 Neuron enriched nuclear proteome isolated from human brain . J. Proteome Res. 12, 3193-3206. (doi:10.1021/pr400246t) Crossref, PubMed, ISI, Google Scholar

    Djuric U, Rodrigues DC, Batruch I, Ellis J, Shannon P, Diamandis P

    . 2017 Spatiotemporal proteomic profiling of human cerebral development . Mol. Cell. Proteom. 16, 1558-1562. (doi:10.1074/mcp.M116.066274) Crossref, ISI, Google Scholar

    Drummond ES, Nayak S, Ueberheide B, Wisniewski T

    . 2015 Proteomic analysis of neurons microdissected from formalin-fixed, paraffin-embedded Alzheimer's disease brain tissue . Sci. Rep. 5, 1-8. (doi:10.1038/srep15456) Crossref, ISI, Google Scholar

    Fathi A, Hatami M, Vakilian H, Han CL, Chen YJ, Baharvand H, Salekdeh GH

    . 2014 Quantitative proteomics analysis highlights the role of redox hemostasis and energy metabolism in human embryonic stem cell differentiation to neural cells . J. Proteomics 101, 1-16. (doi:10.1016/j.jprot.2014.02.002) Crossref, PubMed, ISI, Google Scholar

    Ramachandran U, Manavalan A, Sundaramurthi H, Sze SK, Feng ZW, Hu JM, Heese K

    . 2012 Tianma modulates proteins with various neuro-regenerative modalities in differentiated human neuronal SH-SY5Y cells . Neurochem. Int. 60, 827-836. (doi:10.1016/j.neuint.2012.03.012) Crossref, PubMed, ISI, Google Scholar

    Villeneuve L, Tiede LM, Morsey B, Fox HS

    . 2013 Quantitative proteomics reveals oxygen-dependent changes in neuronal mitochondria affecting function and sensitivity to rotenone . J. Proteome Res. 12, 4599-4606. (doi:10.1021/pr400758d) Crossref, PubMed, ISI, Google Scholar

    Elephants Have The Most Neurons. Why Aren't They The Smartest Animals?

    Why aren't elephants the smartest animals since they have the most neurons? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

    Answer by Fabian van den Berg, Neuropsychologist, on Quora:

    Why aren't elephants the smartest animals since they have the most neurons?

    We often hear 'bigger is better' which might be true for pay-checks but not for other things. I’m of course talking about brains, what else? Nature has an astounding diversity of life, each with a unique brain. Some of those brains grow to be massive organs, like that of the African Elephant with a 5kg brain (11lbs) and 257 billion neurons. Some brains stay tiny, like that of roundworms which comes in at only a fraction of a gram with about 300 neurons in total. Humans rank in between, with a 1.4kg (3lbs) brain and give or take 86 billion neurons.

    That begs the question, if humans are outranked by animals such as elephants, why are we the self-proclaimed smartest creature on earth? How is it that an elephant with almost 3 times the number of neurons isn’t laughing at our struggle with quantum mechanics?

    Like a late night news-report, the reason might surprise you. To put it bluntly, humans aren’t all that special. Like mentioned above, we don’t have the biggest brain with the most neurons. Nor do we have the brain with the biggest surface area dolphins beat us there with their amazingly complex brain folds. We get a bit closer if we take body size into account, but we’d lose from a marmoset (a sort of small monkey which honestly isn’t all that bright). A new measure was developed called the ‘encephalisation quotient’ (EQ), which takes into account that the relationship between brain and body size isn’t linear. It’s a whole formula, but it gave us what we needed for our ego, we were on top! Based on our size we have a brain that is 7 times larger than it should be. Sounds great for us, but the measure failed a bit for other animals. The rhesus monkey should be smarter than a gorilla if we were to believe their EQ, which isn’t the case. That puts us back to square one.

    Humans don’t stand out that much in general, except when it comes to intelligence. Absolute brain size isn’t what makes us smart, neither is surface area, EQ, or neuron density. Then why is it that an elephant, with a huge brain and more neurons, isn’t as smart or even smarter than a human? This is where neuroscience and biology get a bit tricky, an example might help.

    Consider the fastest supercomputer in the world. At the time of writing, that is the Summit made by IBM. It has an impressive 9.216 CPUs, 27.648 GPUs and can make 200 quadrillion calculations per second. For comparison, it would take every person on earth working together, doing 1 calculation per second for almost a year to do what this machine can do in 1 second. It is set to model the universe, explore cancer, and figure out genetics on a scale we cannot imagine. But can it run Minecraft? No it cannot. Yet my old i7 quad-core laptop can run Minecraft just fine. Weird isn’t it, an immense computer with more memory and processing power than fits in my apartment can’t run a simple game that my rickety laptop can? So much for “super” computers.

    The truth is, the thing isn’t designed to run Minecraft. It’s made to run those complex astronomical and biological models, while my laptop is designed to run games and various other tasks useful to me. I’m sure with some fiddling you can get any game running on those systems, but you’d definitely get in trouble for that. When comparing brains, the absolute neuron count isn’t the only thing we need to look at. Just like absolute processing power isn’t the only thing you look for when you need to play Minecraft. What’s in a machine, how it’s connected, how it interfaces, all change depending on a computer’s purpose.

    Human brains and Elephant brain are different in more ways than one. Different parts have different concentrations of neurons for example. Despite having three times as many neurons, elephants only have a third as many neurons in their cerebral cortex. The cortex just so happens to be the part of the brain we associate with a lot of “higher cognitive functions” and intelligence. All those elephant brain cells are concentrated in other areas, like the cerebellum which is used for movements (that trunk does look very capable).

    The way the brain is put together is another factor. We estimate that Neanderthals had bigger brains than us they had the capacity for a 1600cm3 brain. When researchers recently grew some Neanderthal brain-matter, we saw that they were very different from our own. Human mini-brains were nice, smooth spheres, whereas Neanderthal brains were more like popcorn. The consequences are still not clear, but it does bring us to this point: brains are complicated. Brains aren’t homogenous masses of neurons and support cells. Brains have structure to them, neurons form columns and layers, have specific pathways to send and receive specific information. The way neurons are structured and connected affects what and how they process information. Different animals have different needs, different senses, and different bodies. Brains are formed to deal with all of that. An elephant needs to control its trunk to get food, not solve math problems to get good grades.

    As mentioned in the beginning, nature has an astounding diversity of life and brains. Those brains have been sculpted by evolution over millions of years, and evolution doesn’t care about intelligence as much as we do. Evolution is a process without goals instead it takes more of a “good enough” approach. An organism has to function within its environment. For our elephant, an elephant brain is absolutely perfect for doing elephant things, it’s the pinnacle of elephantness.

    Humans had different survival tactics and evolutionary challenges. We didn’t have claws and weren’t very big and strong, instead we were smart and social. In evolutionary terms we bet everything on our brain, which is reflected by our cerebral cortex. Unlike other measurements, our cerebral cortex usually comes out on top compared to other animals. Even when compared to other primates, our cortex is astounding (more so in organization than size). It does require a lot of fuel, making it very reasonable to assume we beat other primates in the intelligence game because we started cooking. But that’s a story for another day.

    Intelligence is an elusive concept we don’t really know for sure what makes one species smarter than another. It’ll be a while before we have definitive answers, but we do know it has to do with a lot of factors. Brain size, number of neurons, number of connections, different structures, densities, how they are connected, they all play a role. No single measure can explain why some animals are smarter than others, let alone why some humans are smarter than others.

    An elephant is not as intelligent as a human, because an elephant brain is formed and wired to do elephant things. Just like a supercomputer isn’t made to play Minecraft, but rather focuses on simulating supernovae. Human brains do human things instead of elephant things in fact we make terrible elephants.

    It’s not the size of the brain that matters it’s how you use it.

    This question originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google+. More questions:


    The roundworm Caenorhabditis elegans is a free-living, transparent nematode, about 1 mm in length, [6] that lives in temperate soil environments. It is the type species of its genus. [7]

    C. elegans has one of the simplest nervous systems of any organism, with its hermaphrodite type having only 302 neurons. Furthermore, the structural connectome of these neurons is fully worked out. There are fewer than one thousand cells in the whole body of a C. elegans worm, and because C. Elegans is a model organism, each has a unique identifier and comprehensive supporting literature. Being a model organism, the genome is fully known, along with many well characterized mutants readily available, a comprehensive literature of behavioural studies, etc. With so few neurons and new calcium 2 photon microscopy techniques it should soon be possible to record the complete neural activity of a living organism. By manipulating the neurons through optogenetic techniques, combined with the above recording capacities the project is in an unprecedented position to be able to fully characterize the neural dynamics of an entire organism.

    In the process of trying to build an "in silico" model of a relatively simple organism like C. elegans, new tools are being developed which will make it easier to model progressively more complex organisms.

    Although the ultimate goal is to simulate all features of C. elegans' behaviour, the project is new and the first behaviour the OpenWorm community decided to simulate is a simple motor response: teaching the worm to crawl. To do so, the virtual worm must be placed in a virtual environment. A full feedback loop must be established: Environmental Stimulus > Sensory Transduction > Interneuron Firing > Motor Neuron Firing > Motor Output > Environmental Change > Sensory Transduction.

    There are two main technical challenges here: modelling the neural/electrical properties of the brain as it processes the information, and modelling the mechanical properties of the body as it moves. The neural properties are being modeled by a Hodgkin-Huxley model, and the mechanical properties are being modeled by a Smoothed Particle Hydrodynamic algorithm.

    The OpenWorm team built an engine called Geppetto which could integrate these algorithms and due to its modularity will be able to model other biological systems (like digestion) which the team will tackle at a later time.

    The team also built an environment called NeuroConstruct which is able to output neural structures in NeuroML. Using NeuroConstruct the team reconstructed the full connectome of C. elegans.

    Using NeuroML the team has also built a model of a muscle cell. Note that these models currently only model the relevant properties for the simple motor response: the neural/electrical and the mechanical properties discussed above.

    The next step is to connect this muscle cell to the six neurons which synapse on it and approximate their effect.

    The rough plan is to then both:

    • Approximate the synapses which synapse on those neurons
    • Repeat the process for other muscle cells

    Progress Edit

    As of January 2015 [update] , the project is still awaiting peer review, and researchers involved in the project are reluctant to make bold claims about its current resemblance to biological behavior project coordinator Stephen Larson estimates that they are "only 20 to 30 percent of the way towards where we need to get". [8]

    In 1998 Japanese researchers announced the Perfect C. elegans Project. A proposal was submitted, but the project appears to have been abandoned. [9] [10]

    In 2004 a group from Hiroshima began the Virtual C. elegans Project. They released two papers which showed how their simulation would retract from virtual prodding. [11] [12]

    In 2005 a Texas researcher described a simplified C. elegans simulator based on a 1-wire network incorporating a digital Parallax Basic Stamp processor, sensory inputs and motor outputs. Inputs employed 16-bit A/D converters attached to operational amplifier simulated neurons and a 1-wire temperature sensor. Motor outputs were controlled by 256-position digital potentiometers and 8-bit digital ports. Artificial muscle action was based on Nitinol actuators. It used a "sense-process-react" operating loop which recreated several instinctual behaviors. [13]

    These early attempts of simulation have been criticized for not being biologically realistic. Although we have the complete structural connectome, we do not know the synaptic weights at each of the known synapses. We do not even know whether the synapses are inhibitory or excitatory. To compensate for this the Hiroshima group used machine learning to find some weights of the synapses which would generate the desired behaviour. It is therefore no surprise that the model displayed the behaviour, and it may not represent true understanding of the system.

    The Open Worm community is committed to the ideals of open science. Generally this means that the team will try to publish in open access journals and include all data gathered (to avoid the file drawer problem). Indeed, all the biological data the team has gathered is publicly available, and the five publications the group has made so far are available for free on their website. All the software that OpenWorm has produced is completely free and open source.

    Open Worm is also trying a radically open model of scientific collaboration. The team consists of anyone who wishes to be a part of it. There are over one hundred "members" who are signed up for the high volume technical mailing list. Of the most active members who are named on a publication there are collaborators from Russia, Brazil, England, Scotland, Ireland and the United States. To coordinate this international effort, the team uses "virtual lab meetings" and other online tools that are detailed in the resources section.

    The Secret To Chimp Strength

    February's brutal chimpanzee attack, during which a pet chimp inflicted devastating injuries on a Connecticut woman, was a stark reminder that chimps are much stronger than humans&mdashas much as four-times stronger, some researchers believe. But what is it that makes our closest primate cousins so much stronger than we are? One possible explanation is that great apes simply have more powerful muscles.

    Indeed, biologists have uncovered differences in muscle architecture between chimpanzees and humans. But evolutionary biologist Alan Walker, a professor at Penn State University, thinks muscles may only be part of the story.

    In an article published in the April issue of Current Anthropology, Walker argues that humans may lack the strength of chimps because our nervous systems exert more control over our muscles. Our fine motor control prevents great feats of strength, but allows us to perform delicate and uniquely human tasks.

    Walker's hypothesis stems partly from a finding by primatologist Ann MacLarnon. MacLarnon showed that, relative to body mass, chimps have much less grey matter in their spinal cords than humans have. Spinal grey matter contains large numbers of motor neurons&mdashnerves cells that connect to muscle fibers and regulate muscle movement.

    More grey matter in humans means more motor neurons, Walker proposes. And having more motor neurons means more muscle control.

    Our surplus motor neurons allow us to engage smaller portions of our muscles at any given time. We can engage just a few muscle fibers for delicate tasks like threading a needle, and progressively more for tasks that require more force. Conversely, since chimps have fewer motor neurons, each neuron triggers a higher number of muscle fibers. So using a muscle becomes more of an all-or-nothing proposition for chimps. As a result, chimps often end up using more muscle than they need.

    "[A]nd that is the reason apes seem so strong relative to humans," Walker writes.

    Our finely-tuned motor system makes a wide variety of human tasks possible. Without it we couldn't manipulate small objects, make complex tools or throw accurately. And because we can conserve energy by using muscle gradually, we have more physical endurance&mdashmaking us great distance runners.

    Great apes, with their all-or-nothing muscle usage, are explosive sprinters, climbers and fighters, but not nearly as good at complex motor tasks. In other words, chimps make lousy guests in china shops.

    In addition to fine motor control, Walker suspects that humans also may have a neural limit to how much muscle we use at one time. Only under very rare circumstances are these limits bypassed&mdashas in the anecdotal reports of people able to lift cars to free trapped crash victims.

    "Add to this the effect of severe electric shock, where people are often thrown violently by their own extreme muscle contraction, and it is clear that we do not contract all our muscle fibers at once," Walker writes. "So there might be a degree of cerebral inhibition in people that prevents them from damaging their muscular system that is not present, or not present to the same degree, in great apes."

    Walker says that testing his hypothesis that humans have more motor neurons would be fairly straightforward. However, he concedes that testing whether humans have increased muscle inhibition could be a bit more problematic.

    The Human Brain is a Linearly Scaled-Up Primate Brain in its Number of Neurons. What Now?

    Cognitive abilities, brain size and number of neurons

    To conclude that the human brain is a linearly scaled-up primate brain, with just the expected number of neurons for a primate brain of its size, is not to state that it is unremarkable in its capabilities. However, as studies on the cognitive abilities of non-human primates and other large-brained animals progress, it becomes increasingly likely that humans do not have truly unique cognitive abilities, and hence must differ from these animals not qualitatively, but rather in the combination and extent of abilities such as theory of mind, imitation and social cognition (Marino et al., 2009). Quantitative changes in the neuronal composition of the brain could therefore be a main driving force that, through the exponential combination of processing units, and therefore of computational abilities, leads to events that may look like “jumps” in the evolution of brains and intelligence (Roth and Dicke, 2005). Such quantitative changes are likely to be warranted by increases in the absolute (rather than relative) numbers of neurons in relevant cortical areas and, coordinately, in the cerebellar circuits that interact with them (Ramnani, 2006). Moreover, viewing the human brain as a linearly scaled-up primate brain in its cellular composition does not diminish the role that particular neuroanatomical arrangements, such as changes in the relative size of functional cortical areas (for instance, Semendeferi et al., 2001 Rilling and Seligman, 2002), in the volume of prefrontal white matter (Schoenemann et al., 2005) or in the size of specific portions of the cerebellum (Ramnani, 2006) may play in human cognition. Rather, such arrangements should contribute to brain function in combination with the large number of neurons in the human brain. Our analysis of numbers of neurons has so far been restricted to large brain divisions, such as the entire cerebral cortex and the ensemble of brainstem, diencephalon and basal ganglia, but an analysis of the cellular scaling of separate functional cortical areas and the related subcortical structures is underway. Such data should allow us to address important issues such as mosaic evolution through concerted changes in the functionally related components of distributed systems, and the presumed increase in relative number of neurons in systems that increase in importance (Barton and Harvey, 2000 Barton, 2006).

    If cognitive abilities among non-human primates scale with absolute brain size (Deaner et al., 2007) and brain size scales linearly across primates with its number of neurons (Herculano-Houzel et al., 2007), it is tempting to infer that the cognitive abilities of a primate, and of other mammals for that matter, are directly related to the number of neurons in its brain. In this sense, it is interesting to realize that, if the same linear scaling rules are considered to apply to great apes as to other primates, then similar three-fold differences in brain size and in brain neurons alike apply to humans compared to gorillas, and to gorillas compared to baboons. This, however, is not to say that any cognitive advantages that the human brain may have over the gorilla and that the gorilla may have over the baboon are equally three-fold – although these differences are difficult to quantify. Since neurons interact combinatorially through the synapses they establish with one another, and further so as they interact in networks, the increase in cognitive abilities afforded by increasing the number of neurons in the brain can be expected to increase exponentially with absolute number of neurons, and might even be subject to a thresholding effect once critical points of information processing are reached. In this way, the effects of a three-fold increase in numbers of neurons may be much more remarkable when comparing already large brains, such as those of humans and gorillas, than when comparing small brains, such as those of squirrel monkeys and galagos.

    Intraspecific variability in size, numbers and abilities

    One final caveat to keep in mind when studying scaling of numbers of brain neurons, particularly in regard to cognition, is that relationships observed across species need not apply to comparisons across individuals of the same species. Not only the extent of intraspecific variation is much smaller (on the order of 10�%) than interspecific variation (which spans five orders of magnitude within mammals Tower, 1954 Stolzenburg et al., 1989), but also the mechanisms underlying interspecific and intraspecific variation are also likely to differ. Our own preliminary data suggest that, indeed, variations in brain size across rats of the same age are not correlated with variations in numbers of neurons (Morterá and Herculano-Houzel, unpublished observations). There is no justification, therefore, to extend the linear correlation between brain size and number of neurons across primates to a putative correlation across persons of different brain sizes (which might be used, inappropriately, as grounds for claims that larger-brained individuals have more neurons, and are therefore “smarter”, than smaller-brained persons). In fact, although men have been reported to have more neurons in the cerebral cortex than women (Pakkenberg and Gundersen, 1997 Pelvig et al., 2008), there is no significant correlation between brain size and general cognitive ability within families (Schoenemann et al., 2000). Across these individuals, other factors such as variations in number and identity of synaptic connections within and across structures, building on a statistically normal, albeit variable, number of neurons, and depending on genetics and life experiences such as learning, are more likely to be determinant of the individual cognitive abilities (see, for instance, Mollgaard et al., 1971 Black et al., 1990 Irwin et al., 2000 Draganski et al., 2004).

    Concluding remarks: our place in nature

    Novel quantitative data on the cellular composition of the human brain and its comparison to other primate brains strongly indicate that we need to rethink our notions about the place that the human brain holds in nature and evolution, and rewrite some of the basic concepts that are taught in textbooks. Accumulating evidence (Deacon, 1997 Roth and Dicke, 2005 Deaner et al., 2007) indicates that an alternative view of the source of variations in cognitive abilities across species merits investigation: one that disregards body and brain size and examines absolute numbers of neurons as a more relevant parameter instead. Now that these numbers can be determined in various brains and their structures, direct comparisons can be made across species and orders, with no assumptions about body𠄻rain size relationships required. Complementarily, however, it now becomes possible to examine how numbers of neurons in the brain, rather than brain size, relate to body mass and surface as well as metabolism, parameters that have been considered relevant in comparative studies (Martin, 1981 Fox and Wilczynski, 1986 MacLarnon, 1996 Schoenemann, 2004), in order to establish what mechanisms underlie the loosely correlated scaling of body and brain.

    According to this now possible neuron-centered view, rather than to the body-centered view that dominates the literature (see Gazzaniga, 2008, for a comprehensive review), the human brain has the number of neurons that is expected of a primate brain of its size a cerebral cortex that is exactly as large as expected for a primate brain of 1.5 kg just as many neurons as expected in the cerebral cortex for the size of this structure and, despite having a relatively large cerebral cortex (which, however, a rodent brain of 1.5 kg would also be predicted to have), this enlarged cortex holds just the same proportion of brain neurons in humans as do other primate cortices (and rodent cortices, for that matter). This final observation calls for a reappraisal of the view of brain evolution that concentrates on the expansion of the cerebral cortex, and its replacement with a more integrated view of coordinate evolution of cellular composition, neuroanatomical structure, and function of cerebral cortex and cerebellum (Whiting and Barton, 2003).

    Other �ts” that deserve updating are the ubiquitous quote of 100 billion neurons (a value that lies outside of the margin of variation found so far in human brains Azevedo et al., 2009), and, more strikingly, the widespread remark that there are 10× more glial cells than neurons in the human brain. As we have shown, glial cells in the human brain are at most 50% of all brain cells, which is an important finding since it is one more brain characteristic that we share with other primates (Azevedo et al., 2009).

    Finally, if being considered the bearer of a linearly scaled-up primate brain does not sound worthy enough for the animal that considers himself the most cognitively able on Earth, one can note that there are, indeed, two advantages to the human brain when compared to others – even if it is not an outlier, nor unique in any remarkable way. First, the human brain scales as a primate brain: this economical property of scaling alone, compared to rodents, assures that the human brain has many more neurons than would fit into a rodent brain of similar size, and possibly into any other similar-sized brain. And second, our standing among primates as the proud owners of the largest living brain assures that, at least among primates, we enjoy the largest number of neurons from which to derive cognition and behavior as a whole. It will now be interesting to determine whether humans, indeed, have the largest number of neurons in the brain among mammals as a whole.


  1. Hipolit

    What remarkable words

  2. Anna

    This message is incomparable))), it is very interesting to me :)

  3. Stanley

    Bravo, what suitable words ..., excellent thought

  4. Arwood

    Brilliant idea and it is timely

  5. Toktilar

    Just what you need! :)

  6. Sullimn

    Granted, your idea is simply perfect

Write a message