Tesi etd-10242022-222557
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Tipo di tesi
Corso Ordinario Secondo Livello
Autore
BIASIZZO, MARCO
URN
etd-10242022-222557
Titolo
A recurrent deep neural network to simulate the early visual processing pathways
Struttura
Cl. Sc. Sperimentali - Ingegneria
Corso di studi
INGEGNERIA - INGEGNERIA
Relatori
relatore Prof. MICERA, SILVESTRO
Parole chiave
- CNN
- Conv-LSTM
- deep neural networks
- early visual pathway
- lateral geniculate nucleus
- spiking neural networks
- visual restoration
Data inizio appello
14/12/2022;
Disponibilità
completa
Riassunto analitico
We investigated the early visual pathways with a neuroscientific model representing the retina and the lateral geniculate nucleus.
We trained a recurrent, convolutional deep neural network to generate a firing rate activity consistent with the lateral geniculate nucleus behavior. The training dataset, composed by videos (inputs) and spiking times (targets), was generated through a physiological model we replicated from literature in pyNest. Then, we simulated an intraneural stimulation by imposing a custom activation to a selected central layer of the model.
Our results proved that recurrent, convolutional deep neural networks can be applied to simulate and analyze the early visual processing pathways.
We trained a recurrent, convolutional deep neural network to generate a firing rate activity consistent with the lateral geniculate nucleus behavior. The training dataset, composed by videos (inputs) and spiking times (targets), was generated through a physiological model we replicated from literature in pyNest. Then, we simulated an intraneural stimulation by imposing a custom activation to a selected central layer of the model.
Our results proved that recurrent, convolutional deep neural networks can be applied to simulate and analyze the early visual processing pathways.
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