Detection of M-sequences from spike sequence in neuronal networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Circuit Theory

Background:

• Linear feedback shift register (LFSR) circuits are known to generate pseudorandom bit sequences (PRBS), including maximal-period M-sequences.
• Understanding the principles governing neural network function is crucial for advancing neuroscience and developing new computational models.

Purpose of the Study:

• To investigate the presence of M-sequences, generated by LFSR circuits, within the time series patterns of stimulated action potentials from hippocampal neurons.
• To determine if neuronal networks can assemble LFSR-equivalent circuits.

Main Methods:

• Recorded stimulated action potentials from dissociated cultures of hippocampal neurons using a multielectrode array.
• Applied methods to detect M-sequences from a 3-stage LFSR circuit (M3) within the recorded neuronal activity.
• Compared the number of detected M-sequences in actual neuronal data against those found in random spike sequences to assess statistical significance.

Main Results:

• Identified several M-sequences corresponding to a 3-stage LFSR circuit (M3) within the neuronal firing patterns.
• Confirmed a statistically significant difference in the occurrence of M-sequences compared to random chance, with more "0-1" reversed 3-stage M-sequences detected than expected accidentally.
• The number of detected M-sequences in the observed data was significantly higher than in random spike sequences.

Conclusions:

• The findings suggest the potential for LFSR-equivalent circuits to be assembled within neuronal networks.
• The detection of M-sequences in neural activity provides evidence for underlying deterministic, pseudorandom processes in neural communication.
• This research opens avenues for exploring the computational capabilities and organizational principles of neural networks.