High-Degree Neurons Feed Cortical Computations

Abstract

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network.

Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction

“…Information processing is more than simply transferring information from one time or place to another. As others have argued it also includes creating new information via synergetic interactions between separate inputs; see [26,38]. Our argument here is that in addition to "enhancing computational capabilities via synergy" information processing also includes distinguishing between currently relevant and currently irrelevant inputs.…”

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

“…Component 4, as the synergistic mutual information contributed by the storage and the transfer source, is what we consider to be the modified information (following [11]). The same underlying definition of information modification from [11] was used by Timme et al [21] in an earlier study of dynamics of spiking activity of neural cultures, yet with another PID measure and considering multiple external inputs to a neuron (see discussion for further details).…”

“…In contrast to earlier studies of synergy or information modification in neural data [21,37], we here used the definition of unique, shared and synergistic mutual information as given by Bertschinger et al [12] and by Griffith and Koch [13] (BROJA-decomposition). As initially outlined, in our view, this definition was the only published one at the time of experiment that had the properties necessary for a mapping between information modification and the synergistic part of the mutual information, and is sufficiently easy to compute because of the convexity of the underlying optimization.…”

“…Timme et al [21] studied information modification in the dynamics of spiking activity of neural cultures with a focus on the relation between information modification at a neuron and its position in the underling (effective) network structure. They report, for example, that neurons which modify "large amounts of information tended to receive connections from high out-degree neurons".…”

“…Another important difference is the use of multi unit activity in our study, while Timme et al [21] used spike sorted data that represents the activity of single neurons. However, for the data-set we used, spike extraction was relatively conservative, using a high threshold and removing events with spurious waveforms [26].…”