Refractory density model of cortical direction selectivity: Lagged-nonlagged, transient-sustained, and On-Off thalamic neuron-based mechanisms and intracortical amplification

Chizhov, Anton V.; Merkulyeva, Natalia

doi:10.1371/journal.pcbi.1008333

Cited by 9 publications

(14 citation statements)

References 98 publications

(182 reference statements)

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“…The biologically-detailed CBRD (conductance-based refractory density) model describes excitatory and inhibitory neuronal populations of the primary visual cortex (V1) receiving retinotopically-organized input from the lateral geniculate nucleus of the thalamus (LGN), which in turn receives input from the retina [ 22 , 24 ]. Activity of LGN neurons is calculated using a filter-based model of receptive fields and firing at the retinal inputs.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, we describe the role of the recurrent connectivity and its strength, synaptic depression, synaptic currents with slow and fast kinetics, and of the propagation of activity through the multiple layers of multi-layer model networks. Next, we validate results obtained with the ring-structured models and extend the analysis of response retention using a complex biologically detailed model with retinotopic representation of space, the conductance-based refractory density (CBRD) model [ 22 ]. Finally, we demonstrate that CBRD model reproducing the response retention can also produce smooth spatio-temporal patterns of activity in response to apparent-motion stimuli, as reported in experimental studies.…”

Section: Introductionmentioning

confidence: 94%

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Response retention and apparent motion effect in visual cortex models

Tiselko,

Volgushev,

Jancke

et al. 2023

PLoS ONE

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Apparent motion is a visual illusion in which stationary stimuli, flashing in distinct spatial locations at certain time intervals, are perceived as one stimulus moving between these locations. In the primary visual cortex, apparent-motion stimuli produce smooth spatio-temporal patterns of activity similar to those produced by continuously moving stimuli. An important prerequisite for producing such activity patterns is prolongation of responses to brief stimuli. Indeed, a brief stimulus can evoke in the visual cortex a long response, outlasting the stimulus by hundreds of milliseconds. Here we use firing-rate based models with simple ring structure, and biologically-detailed conductance-based refractory density (CBRD) model with retinotopic space representation to analyze the response retention and the origin of smooth profiles of activity in response to apparent-motion stimuli. We show that the strength of recurrent connectivity is the major factor that endorses neuronal networks with the ability for response retention. The same strengths of recurrent connections mediate the appearance of bump attractor in the ring models. Factors such as synaptic depression, NMDA receptor mediated currents, and conductances regulating spike adaptation influence response retention, but cannot substitute for the weakness of recurrent connections to reproduce response retention in models with weak connectivity. However, the weakness of lateral recurrent connections can be compensated by layering: in multi-layer models even with weaker connections the activity retains due to its feedforward propagation from layer to layer. Using CBRD model with retinotopic space representation we further show that smooth spatio-temporal profiles of activity in response to apparent-motion stimuli are produced in the models expressing response retention, but not in the models that fail to produce response retention. Together, these results demonstrate a link between response retention and the ability of neuronal networks to generate spatio-temporal patterns of activity, which are compatible with perception of apparent motion.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Response retention and apparent motion effect in visual cortex models

Tiselko,

Volgushev,

Jancke

et al. 2023

PLoS ONE

Self Cite

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“…Currently, the modeling of neural computations can be categorized into first order integrate and fire (I&F) dynamic models (20)(21)(22)(23)(24)and renewal theory inspired refractory density models (25,26) (RDMs). The I&F models work under the assumption of homogeneous neural populations and conceive the neuronal dynamics as a summation (hence, the integrate terminology) of nonlinearly filtered action potentials (above a critical voltage threshold).…”

Section: Mainmentioning

confidence: 99%

Neuron particles capture network topology and behavior from single units

Gupta

Rhodes

Kiani

et al. 2021

Preprint

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While networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.

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“…For visual cortex there is a wealth of experimental data including various neuron types and their intrinsic biophysics, the functional architecture of thalamic input to the system, the kinetics of the synaptic conductances, and the schematic of the intra-cortical synaptic connections. Numerical simulations of such models, for example [7][8][9][10], have provided realistic reproductions of experimental data, and can provide explicit predictions for subsequent experimental studies. In general, a complex model can incorporate the available data at will, motivated by the fact that a priori we do not know which details are fundamental for function, e.g.…”

Section: Introductionmentioning

confidence: 99%

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

Chizhov

Graham

2021

PLoS Comput Biol

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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 as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.

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Refractory density model of cortical direction selectivity: Lagged-nonlagged, transient-sustained, and On-Off thalamic neuron-based mechanisms and intracortical amplification

Cited by 9 publications

References 98 publications

Response retention and apparent motion effect in visual cortex models

Response retention and apparent motion effect in visual cortex models

Neuron particles capture network topology and behavior from single units

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

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