Search

Background: How living neural networks retain information is still incompletely understood. Two prominent ideas on this topic have developed in parallel, but have remained somewhat unconnected. The first of these, the "synaptic hypothesis," holds that information can be retained in synaptic connection strengths, or weights, between neurons. Recent work inspired by statistical mechanics has suggested that networks will retain the most information when their weights are distributed in a skewed manner, with many weak weights and only a few strong ones. The second of these ideas is that information can be represented by stable activity patterns. Multineuron recordings have shown that sequences of neural activity distributed over many neurons are repeated above chance levels when animals perform well-learned tasks. Although these two ideas are compelling, no one to our knowledge has yet linked the predicted optimum distribution of weights to stable activity patterns actually observed in living neural networks. Results: Here, we explore this link by comparing stable activity patterns from cortical slice networks recorded with multielectrode arrays to stable patterns produced by a model with a tunable weight distribution. This model was previously shown to capture central features of the dynamics in these slice networks, including neuronal avalanche cascades. We find that when the model weight distribution is appropriately skewed, it correctly matches the distribution of repeating patterns observed in the data. In addition, this same distribution of weights maximizes the capacity of the network model to retain stable activity patterns. Thus, the distribution that best fits the data is also the distribution that maximizes the number of stable patterns. Conclusions: We conclude that local cortical networks are very likely to use a highly skewed weight distribution to optimize information retention, as predicted by theory. Fixed distributions impose constraints on learning, however. The network must have mechanisms for preserving the overall weight distribution while allowing individual connection strengths to change with learning. [ABSTRACT FROM AUTHOR]

The average cortical neuron makes and receives about 1,000 - 10,000 synaptic contacts. This anatomical information suggests that local cortical networks are connected in a fairly democratic manner, with all neurons having about the same number of incoming and outgoing connections. But the physical connections found in the cortex do not necessarily reveal how information flows through cortical networks. What is the network diagram for information flow in cortical networks? To investigate this issue, we recorded spontaneous spiking activity at 20 kHz for over 1 hr from organotypic cortex cultures placed on a high-density 512 electrode array with 60 µm inter-electrode distance. The high-density array increased the chances that we would record from synaptically connected neurons and allowed us to obtain stable long-term recordings that were essential for accurate estimates of entropy rates and information flow. To measure information flow, we used a new method called transfer entropy that has been shown to accurately identify connections in model networks. Our initial "democratic" hypothesis was that network diagrams of information flow would show all neurons to have approximately equal amounts of incoming and outgoing information flow. Surprisingly, our analysis revealed wide differences in the amount of information flowing into and out of different neurons in the network, indicating that information flow is not "democratically" distributed. These data point to the existence of cells with high information flow that act as highly central hub nodes in the network. Future work combining experiments and simulations will be directed at exploring why local cortical networks assume such non-democratic information flow patterns. [ABSTRACT FROM AUTHOR]