Optimal degrees of synaptic connectivity
WebMar 1, 2024 · A point in this space (i.e., synaptic state, w) defines the entire connectivity structure of the network. In the activity space ... Optimal synaptic dynamics for memory maintenance in the presence of noise ... Google Scholar. 57. A. Litwin-Kumar, et al. Optimal degrees of synaptic connectivity. Neuron, 93 (2024), pp. 1153-1164.e7. View PDF View ... WebSparse synaptic wiring can optimize a neural representation for associative learning • Maximizing dimension predicts the degree of connectivity for cerebellum-like circuits • Supervised plasticity of input connections is needed to exploit dense wiring • Performance of a Hebbian readout neuron is formally related to dimension
Optimal degrees of synaptic connectivity
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WebMar 1, 2024 · A point in this space (i.e., synaptic state, w) defines the entire connectivity structure of the network. In the activity space ... Optimal synaptic dynamics for memory … WebOur theory predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that …
WebOptimal Degrees of Synaptic Connectivity Ashok Litwin-Kumar Columbia University April 14, 2024 - 9:30am 427 Thackeray Hall Abstract: Synaptic connectivity varies widely across neuronal types. In the cerebellar cortex, granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate. WebJan 23, 2014 · In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world. Author Summary
WebMar 11, 2024 · Optimal degrees of synaptic connectivity ( Litwin-Kumar et al., 2024) could be then associated with dendritic length and branching pattern, spine features, and the neuronal impedance, conductance, and voltage modulatory properties ( Papoutsi et … WebOur theory predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that …
Webrespectively, implying access to synaptic connectivity both in forward (pre-to-post) and reverse (post-to-pre) directions. Here we study the impact of different arrangements of synaptic connectivity tables on weight storage and STDP updates for large-scale neuromorphic systems. We analyze the memory efficiency for varying degrees of density
WebDec 8, 2024 · Abstract. Studies of brain network connectivity improved understanding on brain changes and adaptation in response to different pathologies. Synaptic plasticity, the ability of neurons to modify their connections, is involved in brain network remodeling following different types of brain damage (e.g., vascular, neurodegenerative, inflammatory). cty physicsWebOptimal degrees of synaptic connectivity. Neuron 93 (5), 1153–1164. A. Litwin-Kumar, R. Rosenbaum & B. Doiron (2016). Inhibitory stabilization and visual coding in cortical … cty productWebOptimal degrees of synaptic connectivity. A Litwin-Kumar, KD Harris, R Axel, H Sompolinsky, LF Abbott. Neuron 93 (5), 1153-1164, 2024. 242: 2024: Formation and maintenance of … easing stressWebThe theory I will describe predicts optimal values for the number of inputs to cerebellar granule cells and Kenyon cells of the Drosophila mushroom body, and it also provides a … cty prettlWebVan Vreeswijk, C. and Abbott, L.F. (1993) The Effect of Synaptic Time Constants on Firing Patterns in Populations of Spiking Neurons. In Gielen, S. and Kappen, H. eds. ICANN'93: Proceedings of the International Conference on Artificial Neural Networks (Springer-Verlag, London) pp. 666-669. easing sydney\u0027s congestionWebApr 11, 2016 · This suggests synaptic connectivity in cortex is optimized to store a large number of attractor states in a robust fashion. Main Local networks of pyramidal cells in … easing styles in sfmWebSome of these synaptic mechanisms can occur at different time scales. For instance, in a time scale longer than the second (say days or years), synaptic intensities can be modified as a consequence of learning. This has been widely theoretically studied within a general theory of learning in attractors neural networks [2]. cty pouyuen