Investigation of Feedback Connections Effect of a Spike Timing Neural Network Model of Early Visual System

Front. Comput. Neurosci.

Nedelcheva

et al. 2019

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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.

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Section: Simulation Resultsmentioning

confidence: 67%

Spike Timing Neural Model of Motion Perception and Decision Making

Front. Comput. Neurosci.

Nedelcheva

et al. 2019

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“…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%

“…For simplicity, in our model the feedback connectivity from V1 was the same as the feedforward connectivity from LGN to V1. In [16] the presence and strength of such feedback connectivity on the spiking activity of the primary visual cortex was investigated by simulations. It was demonstrated that it has modulatory effect on the selectivity of V1 neurons.…”

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

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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.

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Brain-Inspired Spike Timing Model of Dynamic Visual Information Perception and Decision Making with STDP and Reinforcement Learning

Lecture Notes in Computer Science

2020