Model and experiments.

Model and experiments.

<p>A. Structure of the dataset: binary vectors are organized along a temporal scale. Some of the vectors repeat after time steps, others are randomly generated. Repeated vectors are considered familiar and are labeled as 1, non-repeated vectors are non-familiar and labeled as 0. <b>B<...

Disgrifiad llawn

Wedi'i Gadw mewn:

Manylion Llyfryddiaeth
Prif Awdur:	Viktoria Zemliak (19500335) (author)
Awduron Eraill:	Gordon Pipa (153913) (author), Pascal Nieters (14611325) (author)
Cyhoeddwyd:	2025
Pynciau:	Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparse coding observed previously encountered inputs network activity using lateral connectivity shaped connectivity structure supports approach outperforms lstm sparse input conditions recurrent connectivity without continual familiarity decoding recurrent connections input stimuli decoding strategy unsupervised spike spike trains spike synchrony spike count naturally encoded dependent plasticity
Tagiau:	Ychwanegu Tag Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!

Eitemau Tebyg

Jaccard similarity. Template similarity () and corresponding average pairwise Jaccard similarity computed from the generated dataset.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Stimulus sparseness. The proportion of 0s and 1s in the binary stimulus vector defines sparseness of the stimulus in a dataset.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Predicting generalizability from connectivity.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

The genetic algorithm.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Distributions of optimal parameter values across sparseness levels.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Connectivity adaptation.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

LESH performance on correlated data.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

LESH performance on data of various sparseness.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Models generalization across repeat intervals on the continual familiarity task.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Plasticity regimes weakly depend on input similarity.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Plasticity regimes do not depend on repeat intervals.
gan: Viktoria Zemliak (19500335)
Cyhoeddwyd: (2025)

Simulation parameter summary for the network model.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Comparison of the emergent properties of V1 models.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Comparison of models of V1.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Robustness of the network: (A-C) Firing rates of excitatory and inhibitory neurons are varied.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Tight balance: The cross correlation between the excitatory-only and inhibitory-only responses of the neuron in Fig 8A when input is provided by ten correlated images patches similar to that shown in Fig 8D (inset).
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Loose balance: (A) An excitatory neuron’s receptive field as measured via STA.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Decorrelation of receptive fields: (A) The range of correlations between an excitatory neuron’s receptive field (receptive field shown in inset) and the receptive fields of the 100 inhibitory neurons in the network.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Correlation of firing rates using data from 100 images: (A) Pairwise correlation coefficients of LGN neurons across 100 images, (B) excitatory population, and (C) inhibitory population.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Quantified comparison of Gabor functions with experimental data (): Distribution of receptive fields of model excitatory neurons compared with experimentally recorded receptive fields for cat ([56]) and macaque monkey ([2]).
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Receptive fields of excitatory and inhibitory neurons characterized via simulated STA: Weights calculated via STA have been normalized.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Sparse network response after training: (A) The spike rate of a subset of 30 neurons in each neural population (ON, OFF, E, and I) in response to a subset of 30 images.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Membrane voltage and rastor plot after training: (A) The membrane voltage response of an excitatory neuron (E-388) in response to a single image presented for 400 ms after learning.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

LGN model and cortical model topology: (A) LGN model.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Spike rate and sparseness: (i) Output spike rate of a LIF neuron as a function of excitatory Poisson input rate.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Network response to spontaneous input: Response of output neurons is plotted when input neurons have a constant firing rate (i) with initialized weights before learning and (ii) after learning with natural images with spiking thresholds set back to the same level as before learning.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Receptive fields of excitatory and inhibitory neurons when small weights are initialized: (i) Excitatory receptive fields of 64 randomly chosen neurons.
gan: Marko A. Ruslim (22662875)
Cyhoeddwyd: (2025)

Parameters of spiking network models. Top: excitatory-only network (Fig 8). Bottom: additional parameters for balanced network (Fig 10). Equation numbers refer to S1 Appendix.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Response times of cooperatively coding spiking neural networks.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Number of synaptic connections for the feedforward and cooperative coding architecture depending on RF size, for different sizes of feature populations.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Cooperative coding in an excitatory spiking network of leaky integrate-and-fire neurons.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

2D network schematic and response times versus RF size.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Response speed of networks with inhibition and linear MS.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Loss evolution for different strengths of EI-balance.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Loss evolution and response speed - synapse number trade-off.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Response formation and activity propagation.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Schematics of feedforward and cooperatively coding networks.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Receptive field and response of 1D network.
gan: Paul Züge (21656395)
Cyhoeddwyd: (2025)

Data Sheet 1_Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration.pdf
gan: James Humble (210462)
Cyhoeddwyd: (2025)

Data Sheet 1_Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.pdf
gan: Camille Godin (21445879)
Cyhoeddwyd: (2025)