Large-Scale Computational Model of Cat Primary Visual Cortex (original) (raw)
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Large-Scale Computational Modeling of the Primary Visual Cortex
Coherent Behavior in Neuronal Networks, 2009
This chapter reviews our approach to large-scale computational modeling of the primary visual cortex (V1). The main objectives of our modeling are to (i) capture groups of experimentally observed phenomena in a single theoretical model of cortical circuitry, and (ii) identify the physiological mechanisms underlying the model dynamics. We have achieved these goals by building parsimonious models based on minimal, yet sufficient, sets of anatomical and physiological assumptions. We have also verified the structural robustness of the proposed network mechanisms. During the modeling process, we have identified a particular operating state of our model cortex from which we believe that V1 responds to changes in visual stimulation. This state is characterized by (i) high total conductance, (ii) strong inhibition, (iii) large synaptic fluctuations, (iv) an important role of NMDA conductance in the orientation-specific, long-range interactions, and (v) a high degree of correlation between the neuronal membrane potentials, NMDA-type conductances, and firing rates. Tuning our model to this operating state in the absence of stimuli, we have used to identify and investigated model neuronal network mechanisms underlying cortical phenomena such including (i) spatiotemporal patterns of spontaneous cortical activity, (ii) cortical activity patterns induced by the Hikosaka line-motion
Chapter 1 Large-Scale Computational Modeling of the Primary Visual Cortex
2008
This chapter reviews our approach to large-scale computati on l modeling of the primary visual cortex (V1). The main objectives of our modeling are to (i) capture groups of experimentally observed phenomena in a si ngle theoretical model of cortical circuitry, and (ii) identify the physiological mechanisms underlying the model dynamics. We have achieved these goals by building par simonious models based on minimal, yet sufficient, sets of anatomical and phys iological assumptions. We have also verified the structural robustness of the propos ed network mechanisms. During the modeling process, we have identified a part icular operating state of our model cortex from which we believe that V1 responds to c hanges in visual stimulation. This state is characterized by (i) high total c onductance, (ii) strong inhibition, (iii) large synaptic fluctuations, (iv) an importan t role of NMDA conductance in the orientation-specific, long-range interactions, and (v) a high degree of correlation ...
Journal of Physiology-Paris, 2003
A large-scale computational model of a local patch of input layer 4Ca of the primary visual cortex (V1) of the macaque monkey, together with a coarse-grained reduction of the model, are used to understand potential effects of cortical architecture upon neuronal performance. Both the large-scale point neuron model and its asymptotic reduction are described. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Emphasis is given to the role of cortical architecture (the geometry of synaptic connectivity, of the ordered and disordered structure of input feature maps, and of their interplay) as mechanisms underlying cortical responses within the model. Specifically: (i) Distinct characteristics of model neuronal responses (firing rates and orientation selectivity) as they depend upon the neuron's location within the cortical layer relative to the pinwheel centers of the map of orientation preference; (ii) A time independent (DC) elevation in cortico-cortical conductances within the model, in contrast to a ''push-pull'' antagonism between excitation and inhibition; (iii) The use of asymptotic analysis to unveil mechanisms which underly these performances of the model; (iv) A discussion of emerging experimental data. The work illustrates that large-scale scientific computation-coupled together with analytical reduction, mathematical analysis, and experimental data, can provide significant understanding and intuition about the possible mechanisms of cortical response. It also illustrates that the idealization which is a necessary part of theoretical modeling can outline in sharp relief the consequences of differing alternative interpretations and mechanisms-with final arbiter being a body of experimental evidence whose measurements address the consequences of these analyses.
Multiscale modeling of the primary visual cortex
IEEE Engineering in Medicine and Biology Magazine, 2000
T he extraordinary power of the brain is apparent from the vast complexity of its behaviors and the ease with which it performs them. These behaviors are accomplished by a complex system of excitatory and inhibitory neurons of different types, operating with large intrinsic fluctuations, through extensive feedback, and often with competition between many scales in space and time. The behavior of such large-scale neuronal systems is simply not understood; however, today, a combination of modern experiments, large-scale scientific computation, and mathematical modeling and analysis begins to offer us a glimpse of the inner workings of some parts of this fascinatingly complex system.
A computational model of vertical signal propagation in the primary visual cortex
Biological Cybernetics, 1992
A computational model of the flow of activity in a vertically organized slab of cat primary visual cortex (area 17) has been developed. The membrane potential of each cell in the model, as a function of time, is given by the solution of a system of first order, coupled, non-linear differential equations. When firing threshold is exceeded, an action potential waveform is "pasted" in. The behavior of the model following a brief simulated stimulus to afferents from the dorsal lateral geniculate nucleus (dLGN) is explored. Excitatory and inhibitory post-synaptic potential (E and IPSP) latencies, as a function of cortical depth, were generated by the model. These data were compared with the experimental literature. In general, good agreement was found for EPSPs. Many disynaptic inhibitory inputs were found to be "masked" by the firing of action potentials in the model. To our knowledge this phenomenon has not been reported in the experimental literature. The model demonstrates that whether a cell exhibits disynaptic or polysynaptic PSP latencies is not a fixed consequence of anatomical connectivity, but rather, can be influenced by connection strengths, and may be influenced by the ongoing pattern of activity in the cortex.
The dynamics of visual responses in the primary visual cortex
Progress in brain research, 2007
There is a transformation in behavior in the visual system of cats and primates, from neurons in the Lateral Geniculate Nucleus (LGN) that are not tuned for orientation to orientation-tuned cells in primary visual cortex (V1). The visual stimuli that excite V1 can be well controlled, and the thalamic inputs to V1 from the LGN have been measured precisely. Much has been learned about basic principles of cortical neurophysiology on account of the intense investigation of the transformation between LGN and V1. Here we present a discussion of different models for visual cortex and orientation selectivity, and then discuss our own experimental findings about the dynamics of orientation selectivity. We consider what these theoretical analyses and experimental results imply about cerebral cortical function. The conclusion is that there is a very important role for intracortical interactions, especially cortico-cortical inhibition, in producing neurons in the visual cortex highly selective ...
bioRxiv, 2021
A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular ...
Large Scalable Simulations of Mammalian Visual Cortex
2005
Large artificial neural networks are examined. Structures discussed in this article simulate the cortex of mammalian visual system and its dynamics. Simulations of thousands of Hodgkin-Huxley neurons always require high computational power. Discussion of such networks parallelisation is presented in some detail. Analysis of simulation time, algorithm’s speedup as a function of processors’ number and density of connections is discussed as well.
A comprehensive data-driven model of cat primary visual cortex
2018
Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward ...
A Quantitative Map of the Circuit of Cat Primary Visual Cortex
Journal of Neuroscience, 2004
We developed a quantitative description of the circuits formed in cat area 17 by estimating the "weight" of the projections between different neuronal types. To achieve this, we made three-dimensional reconstructions of 39 single neurons and thalamic afferents labeled with horseradish peroxidase during intracellular recordings in vivo. These neurons served as representatives of the different types and provided the morphometrical data about the laminar distribution of the dendritic trees and synaptic boutons and the number of synapses formed by a given type of neuron. Extensive searches of the literature provided the estimates of numbers of the different neuronal types and their distribution across the cortical layers. Applying the simplification that synapses between different cell types are made in proportion to the boutons and dendrites that those cell types contribute to the neuropil in a given layer, we were able to estimate the probable source and number of synapses made between neurons in the six layers. The predicted synaptic maps were quantitatively close to the estimates derived from the experimentalelectronmicroscopicstudiesforthecaseofthemainsourcesofexcitatoryandinhibitoryinputtothespinystellatecells,whichform a major target of layer 4 afferents. The map of the whole cortical circuit shows that there are very few "strong" but many "weak" excitatory projections, each of which may involve only a few percentage of the total complement of excitatory synapses of a single neuron.