The advancement of artificial intelligence (AI) and the study of neurobiological processes are deeply interlinked, as a deeper understanding of the former can yield valuable insight about the other, and vice versa. Recent neuroscience studies have found that mental state transitions, such as the transition from wakefulness to slow-wave sleep and then to rapid eye movement (REM) sleep, modulate temporary interactions in a class of neurons known as layer 5 pyramidal two-point neurons (TPNs), aligning them with a person’s mental states.

These are interactions between information originating from the external world, broadly referred to as the receptive field (RF1), and inputs emerging from internal states, referred to as the contextual field (CF2). Past findings suggest that RF1 and CF2 inputs are processed at two distinct sites within the neurons, known as the basal site and apical site, respectively.

Current AI algorithms employing attention mechanisms, such as transformers, perceiver and flamingo models, are inspired by the capabilities of the human brain. In their current form, however, they do not reliably emulate high-level perceptual processing and the imaginative states experienced by humans.

Ahsan Adeel, an Associate Professor at the University of Stirling, recently carried out a study exploring the possibility of developing AI models that can reproduce these higher mental states, which could in turn speed up their learning and reduce their computational load.

His paper, published on the arXiv preprint server, introduces Co⁴, a brain-inspired cooperative context-sensitive cognitive computation mechanism specifically designed to replicate the dual-input state-dependent mechanism uncovered in pyramidal TPNs in layer 5 of the human neocortex.

“Attending to what is relevant is fundamental to both the mammalian brain and modern machine learning models such as transformers,” wrote Adeel in his paper.

“Yet determining relevance remains a core challenge, traditionally offloaded to learning algorithms like backpropagation. Inspired by recent cellular neurobiological evidence linking neocortical pyramidal cells to distinct mental states, this work shows how models (e.g., transformers) can emulate high-level perceptual processing and awake thought (imagination) states to pre-select relevant information before applying attention.”

As part of his recent study, Adeel developed a new transformer model that can emulate human perceptual reasoning and imaginative states. This model works by pre-selecting relevant information and identifying the most salient parts of it before applying its full attention to it.

The model connects ideas following a particular reasoning pattern, which focuses on the question (i.e., what is being asked); clues (i.e., pieces of information that could help answer the question); and values or hypotheses (i.e., possible answers to the questions). This reasoning “loop” emulates the ways in which humans try to solve problems, adapting their thinking processes over time.

“Triadic neuronal-level modulation loops among questions (Q), clues (keys, K), and hypotheses (values, V) enable diverse, deep, parallel reasoning chains at the representation level and allow a rapid shift from initial biases to refined understanding,” wrote Adeel.

“This leads to orders-of-magnitude faster learning with significantly reduced computational demand (e.g., fewer heads, layers, and tokens), at an approximate cost of O(N), where N is the number of input tokens. Results span reinforcement learning (e.g., CarRacing in a high-dimensional visual setup), computer vision, and natural language question answering.”

Adeel evaluated his adapted transformer architecture in a series of learning, computer vision and language processing tasks. The results of these tests were highly promising, highlighting the promise of his newly developed mechanism for advancing the reasoning skills of AI models, potentially bringing them even closer to those observed in humans.

“The initial evidence presented here is one of many reasons to believe that emulating the cellular foundations of higher mental states, ranging from high-level perceptual processing to deep, deliberate imaginative reasoning, could be a step toward cognitively meaningful machine intelligence,” concluded Adeel.

“This approach opens the door not only to implementing large numbers of lightweight, inference-efficient AI modules, but also to moving these systems beyond mere information processing toward contextual reasoning, shifting from raw efficiency to real understanding.”

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