Neurons preferentially connect to spatially clustered neurons
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Available evidence, including neural network simulations and neurophysiological studies, demonstrates that neurons exhibit spatial clustering and that connections can be organized on a cluster-specific basis.
Abstract
Rapid categorization of vocalizations enables adaptive behavior across species. While categorical perception is thought to arise in the neocortex, humans and other animals could benefit from functional organization of ethologically-relevant sounds at earlier stages in the auditory hierarchy. Here, we developed two-photon calcium imaging in the awake echolocating bat (
Eptesicus fuscus)
to study encoding of sound meaning in the Inferior Colliculus, which is as few as two synapses from the inner ear. Echolocating bats produce and interpret frequency sweep-based vocalizations for social communication and navigation. Auditory playback experiments demonstrated that individual neurons responded selectively to social or navigation calls, enabling robust population-level decoding across categories. Strikingly, category-selective neurons formed spatial clusters, independent of tonotopy within the IC. These findings support a revised view of categorical processing in which specified channels for ethologically-relevant sounds are spatially segregated early in the auditory hierarchy, enabling rapid subcortical organization of call meaning.
2023-12-08. Conway BR, Tsao DY (October 2009). "Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior
Color (or colour in Commonwealth English) is the visual perception produced by the activation of the different types of cone cells in the eye caused by light. Though color is not an inherent property of matter, color perception is related to an object's light absorption, emission, reflection and transmission. For most humans, visible wavelengths of light are the ones perceived in the visible light
From the V1 blobs, color information is sent to cells in the second visual area, V2. The cells in V2 that are most strongly color tuned are clustered in the "thin stripes" that, like the blobs in V1, stain for the enzyme cytochrome oxidase (separating the thin stripes are interstripes and thick stripes, which seem to be concerned with other visual information like motion and high-resolution form). Neurons in V2 then synapse onto cells in the extended V4. This area includes not only V4, but two other areas in the posterior inferior temporal cortex, anterior to area V3, the dorsal posterior inferior temporal cortex, and posterior TEO. Area V4 was initially suggested by Semir Zeki to be exclusively dedicated to color, and he later showed that V4 can be subdivided into subregions with very high concentrations of color cells separated from each other by zones with lower concentration of such cells though even the latter cells respond better to some wavelengths than to others, a finding confirmed by subsequent studies. The presence in V4 of orientation-selective cells led to the view that V4 is involved in processing both color and form associated with color but it is worth noting that the orientation selective cells within V4 are more broadly tuned than their counterparts in V1, V2, and V3. Color processing in the extended V4 occurs in millimeter-sized color modules called globs. This is the part of the brain in which color is first processed into the full range of hues found in color space.
S2CID 11724926. Conway BR, Tsao DY (October 2009). "Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior
Color vision (CV), a feature of visual perception, is an ability to perceive differences between light composed of different frequencies independently of light intensity.
Color perception is a part of the larger visual system and is mediated by a complex process between neurons that begins with differential stimulation of different types of photoreceptors by light entering the eye. Those photorece
C…
From the V1 blobs, color information is sent to cells in the second visual area, V2. The cells in V2 that are most…
constant
δ SRA
3 pS
Spike-rate adaptation strength
Open in a new tab Network structure
We simulated networks of n = 500 neurons, of which 75 % were excitatory. Excitatory neurons were randomly, independently assigned membership to each of n c clusters in the network. First, each neuron was randomly assigned membership to one of the clusters. Then, each cluster was assigned a number - n E μ c - 1 / n c rounded to the nearest integer-of additional randomly selected neurons such that each cluster had identical numbers of neurons, n E , clust = n E μ c / n c , and mean cluster participation, μ c , reached its goal value.
E-to-E recurrent connections were randomly assigned on a cluster-wise basis, where only neurons that shared membership in a cluster could be connected. The within-cluster connection probability was configured such that the network exhibited a desired global Eto-E connection probability p c . Given the total number of possible connections between excitatory neurons is C t o t = n E n E - 1 and the total number of possible connections between excitatory neurons within all clusters is C clust = n E , clust n E , clust - 1 n c , we calculated the within-cluster connection probability as p c C tot / C clust . That is, given the absence of connections between clusters (clusters were coupled by the overlap of cells) the within-cluster connection probability was greater than p c so as to generate the desired total number of connections equal to p c C t o t .
All E-to-I and I-to-E connections were independent of cluster membership and existed with a probability p c I . There were no I-to-I connections. p c , n c , and μ c were varied for some simulations. Except where specified otherwise, all parameters took the fiducial value shown in the table below.
The network visualization in Figure 1c was plotted based on the first 2 dimensions of a tdistributed stochastic neighbor embedding of the connectivity between excitatory cells using the MATLAB function tsne. The f
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