Network stability

Neuronal activity in the brain exhibits a synchronized pattern during restful states and a desynchronized pattern during active behaviors. Synaptic plasticity during the synchronized states is critical for knowledge reorganization and abstraction. However, the mechanisms underlying the influence of synchronized neuronal activity on neural circuits through plasticity remain incompletely understood. The transition between synchronized and desynchronized states involves substantial modulation of inhibition, which significantly alters the excitation-inhibition (EI) balance in synaptic inputs. The

How changes in synaptic connections underlie learning and memory is a central question in Neuroscience. Previous modeling efforts focused on biologically realistic learning rules and dynamics, but these rules usually produce bimodal weight distributions that are not aligned with experimentally observed lognormal distributions of synaptic weights. How biological learning rules preserve such unimodal distributions and at the same time retain information, i.e., learn, remains unknown. Here, we take a game-theoretical approach, asking whether learning is possible under the constraint of retaining

…information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional featu

We study randomly connected neural networks to understand the effects of network architectures on network dynamics. Specifically, we examine the impact of sparse connectivity combined with Dale’s law on network stability by analysing the eigenspectrum of the networked Jacobian. Previous work has examined the effects of Dale’s law for fully connected random matrices that are balanced (zero mean) [3] or unbalanced [1] and sparse matrices with constant weights for each population [2]. Our work significantly extends these results by integrating both Dale’s law and sparsity into both balanced and u

Recurrent neural networks have been trained to perform a wide variety of experimental tasks in order to understand the underlying dynamics employed by the brain to solve those tasks. However, some of the most commonly trained network architectures have not been biologically realistic, typically limiting their analogy with the brain to the level of abstract dynamical motifs. For example, they had noiseless dynamics or no separation of excitatory and inhibitory cells. The stochastic stabilized supralinear network (SSN) incorporates these features, as well as a rectified-superlinear activation f