Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales

• Published in: Network neuroscience (Cambridge, Mass.) Mass.), 2020, Vol.4 (3), p.678-697
• Main Authors: Sherrill, Samantha P., Timme, Nicholas M., Beggs, John M., Newman, Ehren L.
• Format: Article
• Language: English
• Subjects: Cerebral cortex; Circuits; Computation; Correlated activity; Correlation; Cortex; Cortex (somatosensory); Data processing; Effective connectivity; Firing pattern; Functional morphology; In vitro networks; Information processing; Information theory; Lattice theory; Neocortex; Network analysis; Neural information processing; Neurons; Redundancy; Synergy; Time
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00141

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05–14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission—of which synergistic processing is a component—that resulted as mutual information grew. However, at extrasynaptic windows (up to 3,000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high-correlation regime, synergistic processing decreases with greater correlation. In the present work, we address the question of whether correlated activity in functional networks of cortical circuits supports neural computation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that, at timescales most relevant to direct neuronal communication, neurons with more correlated activity predicted greater computation, suggesting that correlated activity does support computation in cortical circuits. Importantly, this result reversed at timescales less relevant to direct neuronal communication, where even greater correlated activity predicted decreased computation. Thus, the relationship between correlated activity and computation depends on the timescale and the degree of correlation in neuronal interactions.