Deviance detection and regularity sensitivity in dissociated neuronal cultures.

INTRODUCTION

Understanding how neural networks process complex patterns of information is crucial for advancing both neuroscience and artificial intelligence. To investigate fundamental principles of neural computation, we examined whether dissociated neuronal cultures, one of the most primitive living neural networks, exhibit regularity sensitivity beyond mere stimulus-specific adaptation and deviance detection.

METHODS

We recorded activity to oddball electrical stimulation paradigms from dissociated rat cortical neurons cultured on high-resolution CMOS microelectrode arrays. We examined the effects of pharmacological manipulation on responses using the N-methyl-D-aspartate (NMDA) receptor antagonist. To assess regularity sensitivity, we compared neural responses between predictable periodic sequences and random sequences of stimuli.

RESULTS

In oddball electrical stimulation paradigms, we confirmed that the neuronal culture produced mismatch responses (MMRs) with true deviance detection beyond mere adaptation. These MMRs were dependent on the N-methyl-D-aspartate (NMDA) receptors, similar to mismatch negativity (MMN) in humans, which is known to have true deviance detection properties. Crucially, we also showed sensitivity to the statistical regularity of stimuli, a phenomenon previously observed only in intact brains: the MMRs in a predictable, periodic sequence were smaller than those in a commonly used sequence in which the appearance of the deviant stimulus was random and unpredictable.

DISCUSSION

These results challenge the traditional view that a hierarchically structured neural network is required to process complex temporal patterns, suggesting instead that deviant detection and regularity sensitivity are inherent properties arising from the primitive neural network. They also suggest new directions for the development of neuro-inspired artificial intelligence systems, emphasizing the importance of incorporating adaptive mechanisms and temporal dynamics in the design of neural networks.