Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally. We show that, when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overexpression of specific 2- and 3-neuron motifs, distributions of connection numbers in clusters of 3-8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. In addition, memories loaded into the network can be retrieved, even in the presence of noise that is comparable with the baseline variations in the postsynaptic potential. The confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning. We predict that overexpression of excitatory-excitatory bidirectional connections observed in many cortical systems must be accompanied with underexpression of bidirectionally connected inhibitory-excitatory neuron pairs. Many structural and dynamical properties of local cortical networks are ubiquitously present across areas and species. Because synaptic connectivity is shaped by experience, we wondered whether continual learning, rather than genetic control, is responsible for producing such features. To answer this question, we developed a biologically constrained recurrent network of excitatory and inhibitory neurons capable of learning predefined sequences of network states. Embedding such associative memories into the network revealed that, when individual neurons are robustly loaded with a near-maximum amount of memories they can support, the network develops many properties that are consistent with experimental observations. Our findings suggest that basic structural and dynamical properties of local networks in the brain are simply a byproduct of learning and memory storage.