A spiking neural network model of spatial and visual mental imagery

Abstract

Mental imagery has long been of interest to the cognitive and neurosciences, but how it manifests itself in the mind and brain still remains unresolved. In pursuit of this, we built a spiking neural model that can perform mental rotation and mental map scanning using strategies informed by the psychology and neuroscience literature. Results: When performing mental map scanning, reaction times (RTs) for our model closely match behavioural studies (approx. 50 ms/cm), and replicate the cognitive penetrability of the task. When performing mental rotation, our model's RTs once again closely match behavioural studies (model: 55-65°/s; studies: 60°/s), and performed the task using the same task strategy (whole unit rotation of simple and familiar objects through intermediary points). Overall, our model suggests: (1) vector-based approaches to neuro-cognitive modelling are well equipped to re-produce behavioural findings, and (2) the cognitive (in)penetrability of imagery tasks may depend on whether or not the task makes use of (non)symbolic processing.

A new discovery on visual information dynamic changes from V1 to V2: corner encoding

“…Due to the intricate connections between neurons, the characteristic of connections in different RFs is closely related to spike timing-dependent plasticity (STDP) [6,24,44], which is also known as pulse-timedependent plasticity. The connection characteristics are also tightly linked with the orientation selectivity of RFs [7].…”

“…Considered as the perfect image information processing system, visual system of human beings can quickly recognize such objective information as position, size, shape, color, and orientation with substantial advantages in stability, robustness, efficiency, and simplicity [56]. For that reason, scholars from fields of cognitive neurobiology, computational neuroscience, and CV have shown growing interest in examining the neural information processing mechanisms of the visual nervous system [13], [23]; [38,39,41,44].…”

A New Discovery on Visual Information Dynamic Changes from Retina to V2

“…Considered as the perfect image information processing system, visual system of human beings can quickly recognize such objective information as position, size, shape, color, orientation with substantial advantages in stability, robustness, efficiency and simplicity (Zhao, Zou, Jin, Yao, & Li, 2010). For that reason, scholars from fields of cognitive neurobiology, computational neuroscience, and CV have shown growing interest in examining the neural information processing mechanisms of the visual nervous system (Khan, Meyer, Konik, & Bouakaz, 2012;Oprea, Pack, & Khadra, 2020;Panetta, Gao, & Agaian, 2015;Raichle, 2010;Riley & Davies, 2020). Indeed, research on visual information processing mechanisms has kept accelerating as biological techniques continue to evolve over the past few decades.…”

A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network

“…Therefore, in recent years, spiking WTA networks have been highly regarded due to their vital role in cognitive functions such as action selection, attention, decision-making, and working biological memory. 32,33 The main purpose of this research is to design and investigate a scalable and modular hardware accelerator to simulate the behavior of spiking WTA networks in real time. The architecture is fully coded and synthesized in Verilog HDL and is also implemented on Xilinx Artix-7 XC7A200T FPGA as a digital hardware platform.…”

“…According to the observation of the superficial layer of the cortex, there are so many neural microcircuits that compete through positive feedback of excitatory and inhibitory neurons. Therefore, in recent years, spiking WTA networks have been highly regarded due to their vital role in cognitive functions such as action selection, attention, decision‐making, and working biological memory 32,33 …”