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

Abstract

<jats:p> 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. </jats:p>

Synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

“…We have also shown that greater similarity (i.e. synchrony) of transmitters, such as might be generated by strong inter-connectivity, predicts greater synergy at synaptic timescales (Sherrill et al, 2019).…”

“…We quantified the amount of synergistic integration performed by the receiver based on its inputs using ‘synergy,’ a term derived from partial information decomposition. Consistent with previous work [6, 8, 10], synergy was normalized to reflect the proportion of the receiving neuron entropy for which it accounted and to control for variable entropy across triads and networks. Across triads, we asked whether normalized synergy was positively or negatively related to the number of recurrent and feedback edges in the corresponding motif.…”

“…Next, we sought to determine whether the observed relationship between normalized synergy and motif structure could be the product of systematic variance across motifs in either the strength of feedforward edge weight or the mutual information between the senders. Both are predictive of normalized synergy [6,10]. To address each potential confound, we regressed out the variance associated with each factor and asked if the residual normalized synergy was nonetheless correlated to the motif structure (Table 1).…”

“…Synergistic integration can be measured as the synergy (Williams & Beer, 2011) that emerges when a given neuron integrates input from two other neurons (Timme et al, 2016;Wibral et al, 2017). This approach has been used effectively before (Faber et al, 2019;Sherrill et al, 2019;Timme et al, 2016;Wibral et al, 2017). Here, we leveraged this approach to determine how the amount of both recurrent and feedback connections relates to synergistic integration.…”

“…Synergistic integration can be measured as the synergy [7] that emerges when a given neuron integrates input from two other neurons [8,9]. This approach has been used effectively before [6,8,10,11]. Here, we leveraged this approach to determine how recurrent and feedback information flow relate to synergistic integration.…”