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- Benjaminsson, Simon, et al.
(författare)
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Visualization of Output from Large-Scale Brain Simulations
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This project concerned the development of tools for visualization of output from brain simulations performed on supercomputers. The project had two main parts: 1) creating visualizations using large-scale simulation output from existing neural simulation codes, and 2) making extensions to some of the existing codes to allow interactive runtime (in-situ) visualization. In 1) simulation data was converted to HDF5 format and split over multiple files. Visualization pipelines were created for different types of visualizations, e.g. voltage and calcium. In 2) by using the VisIt visualization application and its libsim library, simulation code was instrumented so that VisIt could access simulation data directly. The simulation code was instrumented and tested on different clusters where control of simulation was demonstrated and in-situ visualization of neural unit’s and population data was achieved.
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- Chrysanthidis, Nikolaos, et al.
(författare)
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Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model
- 2022
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Ingår i: eNeuro. - : Society for Neuroscience. - 2373-2822. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization,” which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know (R/K) behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian–Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian–Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing-dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints while also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.
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| 9. |
- Kozlov, Alexander, et al.
(författare)
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Detailed reduced models excitatory hemi-cord locomotor network lamprey
- 2003
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Rhythmic locomotor-related activity can be induced in the isolated hemi-spinal cord of lamprey during bath application of D-glutamate or NMDA (Cangiano and Grillner, 2003). This bursting activity is not dependent on glycinergic inhibition but relies on mutual glutamatergic excitation among network interneurons. The possibility of such oscillatory activity was suggested by earlier simulations (Hellgren-Kotaleski et al. 1999). Here the underlying mechanisms are further examined using both detailed and reduced mathematical models. The detailed network model comprises a population of compartmental excitatory interneurones with Na+, K+, Ca2+, KCa channels as well as two Ca-pools. The synaptic interactions are mediated by AMPA receptors and voltage-dependent NMDA receptors, as established experimentally. This model reproduces the main experimental observations on both cell and network level, including the slow (NMDA/Mg2+ dependent) and the fast rhythm. Burst frequency can be modulated by changing the AMPA and/or NMDA drive, the latter providing only a narrow dynamic range. Further, the distributed network of the entire hemi-cord has been simulated. A weakly asymmetric rostro-caudal connectivity (stronger descending) could support a uniform intersegmental phase lag along most of the spinal cord, whereas a symmetric connectivity could not. The intersegmental phase lag is effectively controlled (forward and backward direction) by adding excitation or inhibition to the most rostral segments. The detailed model was progressively reduced until only the most important (slow) currents remained. The dynamics of the reduced model followed that of the detailed model. Ca influx and activation of KCa currents was shown to play a key role in the burst generation.
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| 10. |
- Lansner, Anders, 1949-, et al.
(författare)
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A Mean Field Approximation of BCPNN
- 2005
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this report we study a mean field (MF) approximation of the Bayesian Confidence Propagating Neural Network (BCPNN) for which we derive the energy function. This MF approximation is compared with the original formulation of the network in a number of different tasks in order to establish the similarities and dissimilarities. We investigate the effect of different updating strategies on the storage capacity. Three different ways of modulating the attractor size are experimentally tested. We apply the networks to prototype extraction. Finally, we investigate how the networks cluster data. These experiments show that there are some differences between BCPNN and its MF approximation. Furthermore, the experiments provide some new knowledge on the clustering of memories in a BCPNN.
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