Octopus 3d Model Free Download
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Comparative analysis of brain function in invertebrates with sophisticated behaviors, such as the octopus, may advance our understanding of the evolution of the neural processes that mediate complex behaviors. Until the last few years, this approach was infeasible due to the lack of neurophysiological tools for testing the neural circuits mediating learning and memory in the brains of octopus and other cephalopods. Now, for the first time, the adaptation of modern neurophysiological methods to the study of the central nervous system of the octopus allows this avenue of research. The emerging results suggest that a convergent evolutionary process has led to the selection of vertebrate-like neural organization and activity-dependent long-term synaptic plasticity. As octopuses and vertebrates are very remote phylogenetically, this convergence suggests the importance of the shared properties for the mediation of learning and memory.
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The researchers ultimately want to use their model to understand how decisions made locally in the arms fit into the context of complex behaviors like hunting, which also require direction from the brain.
Long an inspiration for science-fictional, tentacled aliens from outer space, the octopus may be as alien an intelligence as we can meet on Earth, Sivitilli said. He believes understanding how the octopus perceives its world is as close as we can come to preparing to meet intelligent life beyond our planet.
The octopus exhibits many similar behaviors to vertebrates, like humans, but its nervous system architecture is fundamentally different, because it evolved after vertebrates and invertebrates parted evolutionary ways, more than 500 million years ago.
Sivitilli employed a camera and a computer program to observe the octopus as it explored objects in its tank and looked for food. The program quantifies movements of the arms, tracking how the arms work together in synchrony, suggesting direction from the brain, or asynchronously, suggesting independent decision-making in each appendage.
The octopus is a soft-bodied, eight-limbed mollusc; around 300 species are recognised. Like other cephalopods, the octopus is bilaterally symmetric with two eyes and a beak, with its mouth at the center point of the eight limbs. The soft body can rapidly alter its shape, enabling octopuses to squeeze through small gaps. They trail their eight appendages behind them as they swim.
I was able to get the toggle switch to operate the servo last night by putting two wires from outside poles on the Octopus to poles 1 and 2 on the DPDT. (I am counting poles vertically 1,2,3 down then 4 is back at the top). When I wire center wire from octopus to center pole (2) the toggle will turn off the Octopus when the toggle is in the downward (ON) position I have my LEDs wired according to the diagram, but they are not working. Clearly I am missing something coming from the octopus to the toggle or I need to cross wire poles.
Success. You have to basicaly combine his two diagrams. Create a pigtail off each the wires coming from inner and outer pin. One goes to toggle other goes to LED. One goes to toggle one goes to leg with resistors. Middle wire from octopus goes to 3rd leg of LED configuration.
We'd like to shard the association model Listing such that all users stay on database "master" shard. Users will get a shard_id column and listings will be split into different databases "shard1", "shard2".
The eight legs of the octopus are very special, and the suction cups on each leg make its legs unique. Its legs not only have a unique shape, but also have a rich "connotation". There are nearly 500 million neurons distributed on the eight legs. The neurons on the legs can understand the instructions issued by the octopus brain and perform different actions at the same time.
Scientific illustrator James H. Emerton created the lifelike specimen at Yale University under the guidance of zoologist Addison E. Verrill, Harvard Class of 1862. Earlier this month, the Enteroctopus dofleini was removed from its longtime digs at the Harvard Museum of Natural History, where it lived under less-than-ideal viewing conditions, hanging from a classroom ceiling.
Octopus cells in the posteroventral cochlear nucleus exhibit characteristic onset responses to broad band transients but are little investigated in response to more complex sound stimuli. In this paper, we propose a phenomenological, but biophysically motivated, modeling approach that allows to simulate responses of large populations of octopus cells to arbitrary sound pressure waves. The model depends on only few parameters and reproduces basic physiological characteristics like onset firing and phase locking to amplitude modulations. Simulated responses to speech stimuli suggest that octopus cells are particularly sensitive to high-frequency transients in natural sounds and their sustained firing to phonemes provides a population code for sound level.
Octopus cell spikes only occur at the onset of broad band transients [3,4,5,6, 15, 21] but phase-lock persistently to amplitude modulations in a specific AM frequency band. Mechanistically, this firing behavior is thought to arise from integrating across auditory nerve fibers (ANFs) [10, 13, 16] with a broad range of characteristic frequencies [4, 20, 24]. This suggests that the main computation underlying AM extraction is most likely based on the tonotopic pattern of afferent arborization. In addition, octopus cells have remarkably low input resistances of only few Mega Ohms [5, 15] leading to fast enough membrane time constants for processing of fast transient as well as slow amplitude fluctuations. The short membrane time constants are generated by a high density of low-threshold potassium channels [23], which in addition to reducing integration time also endow the neurons with differentiation properties [1, 22] that further facilitate AM locking.
Computational theories of octopus cell function thus require to analyze the interplay between cellular biophysical properties and the circuit parameters describing ANF population inputs. Here, we propose an efficient phenomenological model for octopus cell spiking with only few parameters that are either constrained by direct physiological measurements or functional properties. We find that octopus cell spiking over a wide range of best frequencies can be robustly explained by only small changes in these parameters. Our model thus provides a computationally efficient and robust tool to simulate octopus cell spike responses to any kind of sound stimulus. The model can therefore be used to emulate population inputs to downstream structures in the auditory pathway, the ventral nucleus of the lateral lemniscus and the inferior colliculus.
The general structure of the proposed effective model is outlined in Fig. 1. In short, the sound stimulus is translated to simulated ANF firing rates \(r_i(t)\), where _ labels the respective frequency channel. The ANF rates are then translated into the octopus cell input by a weighted sum over frequency channels with weight factors \(g_i\). The cellular membrane potential is derived from these inputs by a combination of differentiation and low-pass filtering. Finally, the output rate 1_(2_) of the octopus cell is obtained by a sigmoidal transformation of the pseudo potential 3_(4_). Spike trains can subsequently be obtained by using 5_(6_) as the density of an inhomogeneous Poisson process. All individual transformations will be explained in detail in the following paragraphs.
For high frequency cells it is necessary to introduce an additional frequency shift \(f_{0}\) to properly fit the observed characteristic frequency, compensating for the overlap of peripheral filters. The parameter \(\varDelta \) describes the width (in octaves) of the arborization and will be the essential fit parameter to model the afferent arborization.
Receptive fields of model octopus cells using the plain periphery model either showed unphysiologically strong low-frequency tails [compared to data from [24]], even for cells with high characteristic frequencies, or amplitude modulation locking of the model was distorted by low frequency components from the tails of the ANF receptive fields. For simplicity, we removed these low-frequency components by applying an additional high pass filter
to the sound pressure wave before the periphery model, with \(f_\mathrm{hp}= 450\) Hz, the Heaviside step function \(\varTheta (t)\), and \(\varDelta t=1/(100\,\mathrm{kHz})\). This filter suppresses most of the low-frequency-tail of the receptive fields, while still preserving the general response patterns of the model octopus cells (see Discussion for biological feasibility). For numerical convolution, we restricted the kernel \(k_\mathrm{hp}\) to a duration of \(3/f_\mathrm{hp}\).
The octopus cells respond to rising envelopes of the sound stimuli, which, following [1], we model via a differentiation. The kinetics of the membrane potential response is accounted for by an additional second-order low pass filter
that is supposed to reflect the combination of synaptic and potassium channel kinetics as well as membrane filtering. The frequency \(f_\mathrm{lp}\) is the second fit parameter of the model. This leads to the pseudo potential 5376163bf9
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