Abstract
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.
Highlights
Vision has to encode and interpret in real time the complex, ambiguous, and dynamic information from the environment in order to ensure successive interaction with it
The incoming light is initially converted in the retina into electrical signal by retinal ganglion cells (RGC), passed through the relay station—lateral geniculate nucleus (LGN) and thalamic reticular nucleus (TRN)—to the primary visual cortex (V1) where the visual information splits in two parallel pathways involved in encoding spatial layout and motion and shape information
In Koprinkova-Hristova et al (2018) we demonstrated that feedback inhibitory connections from V1 to LGN via TRN/IN modulates V1 neurons selectivity
Summary
Vision has to encode and interpret in real time the complex, ambiguous, and dynamic information from the environment in order to ensure successive interaction with it. The model contains two layers of ON and OFF RGC and their corresponding LGN and IN/TRN neurons, having identical relative to visual scene positions and opposite [“on-center off-surround” (ON) and “off-center on-surround” (OFF)] receptive fields placed in reverse order like in Kremkow et al (2016).
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