Frédéric Alexandre

Subba Reddy Oota, Zijiao Chen, Manish Gupta et al.

Can artificial intelligence unlock the secrets of the human brain? How do the inner mechanisms of deep learning models relate to our neural circuits? Is it possible to enhance AI by tapping into the power of brain recordings? These captivating questions lie at the heart of an emerging field at the intersection of neuroscience and artificial intelligence. Our survey dives into this exciting domain, focusing on human brain recording studies and cutting-edge cognitive neuroscience datasets that capture brain activity during natural language processing, visual perception, and auditory experiences. We explore two fundamental approaches: encoding models, which attempt to generate brain activity patterns from sensory inputs; and decoding models, which aim to reconstruct our thoughts and perceptions from neural signals. These techniques not only promise breakthroughs in neurological diagnostics and brain-computer interfaces but also offer a window into the very nature of cognition. In this survey, we first discuss popular representations of language, vision, and speech stimuli, and present a summary of neuroscience datasets. We then review how the recent advances in deep learning transformed this field, by investigating the popular deep learning based encoding and decoding architectures, noting their benefits and limitations across different sensory modalities. From text to images, speech to videos, we investigate how these models capture the brain's response to our complex, multimodal world. While our primary focus is on human studies, we also highlight the crucial role of animal models in advancing our understanding of neural mechanisms. Throughout, we mention the ethical implications of these powerful technologies, addressing concerns about privacy and cognitive liberty. We conclude with a summary and discussion of future trends in this rapidly evolving field.

Lucie Fontaine, Frédéric Alexandre

Complementary Learning Systems theory holds that intelligent agents need two learning systems. Semantic memory is encoded in the neocortex with dense, overlapping representations and acquires structured knowledge. Episodic memory is encoded in the hippocampus with sparse, pattern-separated representations and quickly learns the specifics of individual experiences. Recently, this duality between semantic and episodic memories has been challenged by predictive coding, a biologically plausible neural network model of the neocortex which was shown to have hippocampus-like abilities on auto-associative memory tasks. These results raise the question of the episodic capabilities of the neocortex and their relation to semantic memory. In this paper, we present such a predictive coding model of the neocortex and explore its episodic capabilities. We show that this kind of model can indeed recall the specifics of individual examples but only if it is trained on a small number of examples. The model is overfitted to these exemples and does not generalize well, suggesting that episodic memory can arise from semantic learning. Indeed, a model trained with many more examples loses its recall capabilities. This work suggests that individual examples can be encoded gradually in the neocortex using dense, overlapping representations but only in a limited number, motivating the need for sparse, pattern-separated representations as found in the hippocampus.

Guangfu Hao, Frederic Alexandre, Shan Yu

Cognitive flexibility has been extensively studied in human cognition but remains relatively unexplored in the context of Visual Large Language Models (VLLMs). This study assesses the cognitive flexibility of state-of-the-art VLLMs (GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet) using the Wisconsin Card Sorting Test (WCST), a classic measure of set-shifting ability. Our results reveal that VLLMs achieve or surpass human-level set-shifting capabilities under chain-of-thought prompting with text-based inputs. However, their abilities are highly influenced by both input modality and prompting strategy. In addition, we find that through role-playing, VLLMs can simulate various functional deficits aligned with patients having impairments in cognitive flexibility, suggesting that VLLMs may possess a cognitive architecture, at least regarding the ability of set-shifting, similar to the brain. This study reveals the fact that VLLMs have already approached the human level on a key component underlying our higher cognition, and highlights the potential to use them to emulate complex brain processes.

Ikram Chraibi Kaadoud, Nicolas P. Rougier, Frédéric Alexandre

We introduce a general method to extract knowledge from a recurrent neural network (Long Short Term Memory) that has learnt to detect if a given input sequence is valid or not, according to an unknown generative automaton. Based on the clustering of the hidden states, we explain how to build and validate an automaton that corresponds to the underlying (unknown) automaton, and allows to predict if a given sequence is valid or not. The method is illustrated on artificial grammars (Reber's grammar variations) as well as on a real use-case whose underlying grammar is unknown.