Orientation Selectivity Tuning of a Spike Timing Neural Network Model of the First Layer of the Human Visual Cortex

Front. Comput. Neurosci.

Nedelcheva

et al. 2019

Self Cite

The paper presents a hierarchical spike timing neural network model developed in NEST simulator aimed to reproduce human decision making in simplified simulated visual navigation tasks. It includes multiple layers starting from retina photoreceptors and retinal ganglion cells (RGC) via thalamic relay including lateral geniculate nucleus (LGN), thalamic reticular nucleus (TRN), and interneurons (IN) mediating connections to the higher brain areas—visual cortex (V1), middle temporal (MT), and medial superior temporal (MTS) areas, involved in dorsal pathway processing of spatial and dynamic visual information. The last layer—lateral intraparietal cortex (LIP)—is responsible for decision making and organization of the subsequent motor response (saccade generation). We simulated two possible decision options having LIP layer with two sub-regions with mutual inhibitory connections whose increased firing rate corresponds to the perceptual decision about motor response—left or right saccade. Each stage of the model was tested by appropriately chosen stimuli corresponding to its selectivity to specific stimulus characteristics (orientation for V1, direction for MT, and expansion/contraction movement templates for MST, respectively). The overall model performance was tested with stimuli simulating optic flow patterns of forward self-motion on a linear trajectory to the left or to the right from straight ahead with a gaze in the direction of heading.

Section: Simulation Resultsmentioning

confidence: 99%

Section: Model Structurementioning

confidence: 99%

Section: Model Structurementioning

confidence: 99%

See 1 more Smart Citation

Spike Timing Neural Model of Motion Perception and Decision Making

Front. Comput. Neurosci.

Nedelcheva

et al. 2019

Self Cite

“…1. It is based on structure developed first in [11] based on literature information about each layer neurons' functionality, structure and connectivity according to [7,8,15,17,18,23,27]. The difference is in additional feedback connections from MST to MT as well as STDP connections within thalamic relay (to and from LGN to TRN and interneurons IN) and from MST to LIP area.…”

Section: Model Structurementioning

confidence: 99%

STDP Plasticity in TRN Within Hierarchical Spike Timing Model of Visual Information Processing

IFIP Advances in Information and Communication Technology

Nedelcheva

et al. 2020

Self Cite

We investigated age related synaptic plasticity in thalamic reticular nucleus (TRN) as a part of visual information processing system in the brain. Simulation experiments were performed using a hierarchical spike timing neural network model in NEST simulator. The model consists of multiple layers starting with retinal photoreceptors through thalamic relay, primary visual cortex layers up to the lateral intraparietal cortex (LIP) responsible for decision making and preparation of motor response. All synaptic inter-and intra-layer connections of our model are structured according to the literature information. The present work extends the model with spike timing dependent plastic (STDP) synapses within TRN as well as from visual cortex to LIP area. Synaptic strength changes were forced by teaching signal typical for three different age groups (young, middle and elderly) determined experimentally from eye movement data collected by eye tracking device from human subjects preforming a simplified simulated visual navigation task. Keywords: Spike timing neural model • Spike timing dependent plasticity • Visual system • Decision making • Saccade generation This work was financially supported by the Bulgarian Science Fund, grant No DN02-3-2016 "Modeling of voluntary saccadic eye movements during decision making".

“…The structure of perceptual layers up to LIP area involved in the sensory-based decision making reported in [15], [16], [17] is shown in Fig. 1 Following the commonly accepted models from [18], [19], the reaction of retinal ganglion cells to luminosity changes was simulated by a spatiotemporal filter whose spatial component has circular shape modelled by a difference of two Gaussians and the temporal component has a bi-phasic profile determined by difference of two Gamma functions.…”

Section: A Visual Information Perception and Sensory-based Decisionmentioning

confidence: 99%

Age-related Spike Timing Dependent Plasticity of Brain-inspired Model of Visual Information Processing with Reinforcement Learning

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

2020

Self Cite

The paper summarizes our efforts to develop a spike timing neural network model of dynamic visual information processing and decision making inspired by the available knowledge about how the human brain performs this complicated task. It consists of multiple layers with functionality corresponding to the main visual information processing structures starting from the early level of the visual system up to the areas responsible for decision making based on accumulated sensory evidence as well as the basal ganglia modulation due to the feedback from the environment. In the present work, we investigated age-related changes in the spike timing dependent plastic synapses of the model as a result of reinforcement learning.