Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “Artificial neuron”
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays
<p>First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 2. Example of different axonal conduction delay between presynaptic neuron A0 and postsynaptic neuron A1 and A2
<p>Different axonal conduction delays between every two neurons are applied as follows:<br> For instance, consider presynaptic neuron A0 and postsynaptic neurons A1 and A2 in Figure 2. If neuron A0 fires spike S0 in t = 9 and S1 in t = 11 and with a time step equal to 0.5 ms, S0 arrives to A1 in t = 13 and to A2 in t = 14 where S1 arrives to A1 in t = 15 and to A2 in t = 16. This is implemented using counters assigned to each spike which increases with each time step in the time window. If counters arrive to axonal delay values, the effect of spikes from presynaptic neuron applies to the post synaptic neurons.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit
<p>In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight<br> values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons
<p>In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes.<br> The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.</p>
Hoverfly (Eristalis tenax) descending neurons respond to pursuits of artificial targets
<p><span>Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey followed by pursuit and capture. Such target detection is not only used by predators but can also play an important role in conspecific interactions. Male hoverflies (<em>Eristalis</em> <em>tenax</em>), for example, vigorously defend their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons that are found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target selective descending neurons (TSDNs) in male <em>Eristalis</em> <em>tenax</em> hoverflies. We show that the neurons have dorso-frontal receptive fields, with a preferred direction up and away from the visual midline, with a clear division into a TSDN<sub>Left</sub> and a TSDN<sub>Right</sub> cluster. We next reconstructed visual flow-fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points, but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN<sub>Left</sub> and TSDN<sub>Right</sub> populations responded to substantially larger proportions of the pursuits, but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.</span></p>
Descending neurons of the hoverfly respond to pursuits of artificial targets
Open the record for dataset details and reuse information.
Biskyrmion-based artificial neuron data
<p>Data used in the publication</p>
Data from: Artificial selection on brain size leads to matching changes in overall number of neurons
Neurons are the basic computational units of the brain, but brain size is the predominant surrogate measure of brain functional capacity in comparative and cognitive neuroscience. This approach is based on the assumption that larger brains harbour higher numbers of neurons and their connections and therefore have a higher information processing capacity. However, recent studies have shown that brain mass may be less strongly correlated with neuron counts than previously thought. Till now, no experimental test has been conducted to examine the relationship between evolutionary changes in brain size and the number of brain neurons. Here we provide such a test by comparing neuron number in artificial selection lines of female guppies (Poecilia reticulata) with >15% difference in relative brain mass and numerous previously demonstrated cognitive differences. Using the isotropic fractionator, we demonstrate that large-brained females have a higher overall number of neurons than small-brained females, but similar neuronal densities. Importantly, this difference holds also for the telencephalon, a key region for cognition. Our study provides the first direct experimental evidence that selection for brain mass leads to matching changes in number of neurons and shows that brain size evolution is intimately linked to the evolution of neuron number and cognition.
Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses
<p>This is the repository containing the datasets relative to the publication: <em>L. Benatti, T. Zanotti, D. Gandolfi, J. Mapelli, and F. M. Puglisi, “Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses,” Nano Futures, vol. 7, no. 2, p. 025003, May 2023, doi: <a href="https://doi.org/10.1088/2399-1984/accf53">10.1088/2399-1984/accf53</a>.</em></p> <p>In this folder, you will find MATLAB workspaces containing:</p> <ul> <li><strong>Sni:</strong> Tables with stimuli values (binary and decimal), noise, and surprise, divided by synapse conductance values.</li> <li><strong>MI_vs_G:</strong> Vectors containing simulated values of mutual information (MI), noise, and entropy associated with synapse conductance values, as shown in Figure 5.</li> <li><strong>SpS_vs_rank:</strong> Traces of surprise per spike (SpS) related to synapse conductance values, ordered by stimulus rank. <br> This includes the tracking of stimuli associated with lower/higher SpS as synapse conductance varies, as illustrated in Figure 6b.</li> </ul>
Data from: Artificial selection on brain size leads to matching changes in overall number of neurons
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.