Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication.

Visual perception emerges as the result of neural systems actively organizing intrinsically noisy visual signals. It is commonly assumed that selection processes of competing neurons underlie this emergence of perceptual organization. While the neural competition, realized by such a “mutual inhibition” circuit has been examined in many theoretical studies, its dynamic properties have not been investigated in real neurons. We have developed a “hybrid” system where two real-life pyramidal neurons in a mouse brain slice interact through a computer simulated mutual inhibition circuit. We found that simultaneous activation of the mutually inhibiting pyramidal neurons leads to bi-stable activity. We investigated the effects of noise and the effect of changes in the activation strength on the dynamics. We observed that the circuit exhibits dynamics strikingly similar to the known properties of bi-stable visual perception.

and Alhara, Takeshi (2021) Dynamics of a mutual inhibition circuit between pyramidal neurons compared to human perceptual competition.

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