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

Abstract

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".

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

“…The reinforcement inputs r left and r right are both teaching signals that control the dopamine level. In contrast to our preliminary investigations, where both reinforcement signals were constant generating currents of both SNc structures, here they were proportional to the difference between desired SC activity (generating current as in [14]).…”

“…In current investigation we simulated our model presenting as input moving dot stimuli with expansion center to the left of the screen center. As in [14] we've created training signals as generating currents I left and I right for the left and right LIP neurons respectively as follows:…”

“…These results might be explained with task-related connectivity between MST and LIP areas in our model. Since the excitatory connections are allowed only from MSTe templates to the corresponding to their focal points LIP areas and the rest of connections remain inhibitory, the reinforcement corresponding to perceptual decision increases inhibition from MSTe areas related to the wrong decision to both LIP areas while the opposite reinforcement tries to revert the strength of excitatory connections towards the one opposite to the [14] we observed that only inhibitory feedback connections to LGN from TRN structure are subject to some changes so here we monitored only their changes. In contrast however to our previous results from STDP training of perceptual part of the model reported in [14], changes in case of reinforcement training reported here became visible after single presentation of teaching signal.…”

“…• Enhanced connectivity of visual information processing layers with multiple feedback connections and spike timing dependent plasticity in its synapses reported in [14].…”

Data modelling and data processing generated by human eye movements

“…Techniques in the field of artificial intelligence are widely used [12]. They are used to model experimental processes [13]- [15], to analyze experimental results [16], and to examine a variety of indicators, such as age and/or gender [17], [18]. One important problem associated with this type of research is the processing of the data used.…”