Multilevel development of cognitive abilities in an artificial neural network

Abstract

Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.

The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet

“…Each brain functioning scores estimated by each biometric model will be agreed with each score of one or some questionnaires. This multimodality approach ( 73 ) reflects the recent advances in both quantity and quality of data available since Information & Communication Technology (ICT) and Internet of Things (IoT) brings high potential for improved accuracy at cost-efficiency ( 24 , 28 ). Machine learning and AI applied to NDDs may open valuable route to examine heterogenous symptoms in pediatric population and at individual level, by integrating multimodality dimensions in prediction models, such as social, environmental, and structural determinants ( 74–76 ).…”

About the compatibility between the perturbational complexity index and the global neuronal workspace theory of consciousness

“…This means that other stimuli cannot enter GNW. It also underlines the importance if inhibition in conscious processing ( Volzhenin, et al. 2022 ).…”

Embodied Cognitive Morphogenesis as a Route to Intelligent Systems

“…A hallmark of neural development is the composition of structural layers that can be used for diverse sets of computations. This occurs through a multilevel process that involves several distinct layers of connectionist function [86] or the combination of morphological topology and model heterogeneity [87]. In both cases, less complex behaviors get processed into more complex behaviors through the upward transfer of information.…”