Robust associative learning is sufficient to explain structural and dynamical properties of local cortical circuits

Abstract

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 memory sequences of varying lengths and compared network properties to 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, overrepresentations of specific 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. What is more, memories loaded into the network can be retrieved even in the presence of noise comparable to the baseline variations in the postsynaptic potential. Confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning.

Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model

“…On one hand, (a) there exist independent empirical evidences that excitatory to pairs is overexpressedcompared to random network -in rat's visual (Song et al, 2005) and somatosensory (Perin et al, 2011) cortex. In a recent theoretical study this overexpression is explained as a consequence of maximizing capacity of the associative memory (Zhang et al, 2019); these suggest that the emergence of excitatory to pairs is not an accidental observation. On the other hand, (b) we have shown that whenever the background network is sparse, strong negative triple-wise is the signature of excitatory to pairs; and even simultaneous presence of excitation and inhibition does not disturb this conclusion.…”

Noise in neurons and synapses enables reliable associative memory storage in local cortical circuits

“…Second, as synaptic connectivity of neurons changes during learning (3), it is not unreasonable to expect that the requirement of reliable memory retrieval is reflected in the properties of network connectivity and, consequently, the activity of neurons in the brain. Interestingly, local neural networks in the mammalian cortical areas have many common features of connectivity and network activity (18). We show that these network properties in the model emerge all at once during reliable memory storage.…”

“…The replica method (32,33) provides an analytical solution in the N → ∞ limit. Though neuron networks in the brain are finite, they are thought to be large enough to have many properties that are well described by this limit (18). More importantly, the analytical solution of the replica method reveals the dependence of the results on combinations of network parameters that can be explored with other methods.…”

“…McCulloch and Pitts neurons (17) loaded to capacity with associative memory sequences (18) in the presence of errors and noise. First, we show that there is a tradeoff between the capacity and reliability of stored sequences which is controlled by the levels of errors and noise present during learning.…”

“…Rubin et al (10) considered presynaptic and intrinsic noise and showed that the balance of inhibitory and excitatory currents emerges at capacity. Zhang et al (18) showed that many structural and dynamical properties of local cortical networks emerge in associative networks robustly loaded to capacity. These studies significantly differ from the present work both in terms of the models and the results.…”