Synaptic connectivity in the midget-parvocellular pathway of primate central retina

2005 ◽  
Vol 494 (2) ◽  
pp. 260-274 ◽  
Author(s):  
Patricia R. Jusuf ◽  
Paul R. Martin ◽  
Ulrike Grünert
Keyword(s):  
Synaptic Connectivity ◽  
Parvocellular Pathway ◽  
Central Retina

Computer Reconstruction of the Cellular Architecture of the Daphnia Magna Optic Ganglion

1975 ◽  
Vol 33 ◽  
pp. 284-285
Author(s):  
E. R. Macagno ◽  
C. Levinthal
Keyword(s):  
Daphnia Magna ◽  
Genetic Background ◽  
Compound Eye ◽  
Synaptic Connectivity ◽  
Serial Sections ◽  
Cross Sectional ◽  
Small Crustacean ◽  
Optic Ganglion ◽  
Structural Invariance ◽  
High Degree

The optic ganglion of Daphnia Magna, a small crustacean that reproduces parthenogenetically contains about three hundred neurons: 110 neurons in the Lamina or anterior region and about 190 neurons in the Medulla or posterior region. The ganglion lies in the midplane of the organism and shows a high degree of left-right symmetry in its structures. The Lamina neurons form the first projection of the visual output from 176 retinula cells in the compound eye. In order to answer questions about structural invariance under constant genetic background, we have begun to reconstruct in detail the morphology and synaptic connectivity of various neurons in this ganglion from electron micrographs of serial sections (1). The ganglion is sectioned in a dorso-ventra1 direction so as to minimize the cross-sectional area photographed in each section. This area is about 60 μm x 120 μm, and hence most of the ganglion fit in a single 70 mm micrograph at the lowest magnification (685x) available on our Zeiss EM9-S.

Synaptic Connectivity in Engineered Neuronal Networks

2007 ◽  
pp. 165-173 ◽  
Author(s):  
Peter Molnar ◽  
Jung-Fong Kang ◽  
Neelima Bhargava ◽  
Mainak Das ◽  
James J. Hickman
Keyword(s):  
Neuronal Networks ◽  
Synaptic Connectivity

MAGNOCELLULAR PATHWAY FOR ROTATION INVARIANT NEOCOGNITRON

1993 ◽  
Vol 04 (01) ◽  
pp. 43-54 ◽  
Author(s):  
CHRISTOPHER HIAN-ANN TING
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Neural Network Model ◽  
Visual Information ◽  
Input Pattern ◽  
Arbitrary Orientation ◽  
Magnocellular Pathway ◽  
Rotation Invariant ◽  
Parvocellular Pathway ◽  
Simulation Programme

In the mammalian visual system, magnocellular pathway and parvocellular pathway cooperatively process visual information in parallel. The magnocellular pathway is more global and less particular about the details while the parvocellular pathway recognizes objects based on the local features. In many aspects, Neocognitron may be regarded as the artificial analogue of the parvocellular pathway. It is interesting then to model the magnocellular pathway. In order to achieve "rotation invariance" for Neocognitron, we propose a neural network model after the magnocellular pathway and expand its roles to include surmising the orientation of the input pattern prior to recognition. With the incorporation of the magnocellular pathway, a basic shift in the original paradigm has taken place. A pattern is now said to be recognized when and only when one of the winners of the magnocellular pathway is validified by the parvocellular pathway. We have implemented the magnocellular pathway coupled with Neocognitron parallel on transputers; our simulation programme is now able to recognize numerals in arbitrary orientation.

Simultaneous foveal and parafoveal electroretinograms in hereditary degeneration of the central retina

1989 ◽  
Vol 71 (4) ◽  
pp. 435-443 ◽  
Author(s):  
Bruno Bagolini ◽  
Vittorio Porciatti ◽  
Benedetto Falsini ◽  
Massimo Neroni ◽  
Antonello Fadda ◽  
...  
Keyword(s):  
Central Retina

scholarly journals A convolutional neural network for estimating synaptic connectivity from spike trains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

AbstractThe recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

Sign in / Sign up

Export Citation Format

Share Document