Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly.

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Samuel Pavio Muscinelli, Tilo Schwalger

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate neurons shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single neurons. For the case of two-dimensional rate neurons with strong adaptation, we find that the network exhibits a state of resonant chaos, characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated neurons, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single neurons, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic neural networks. Author summary Biological neural networks are formed by a large number of neurons whose interactions can be extremely complex. Such systems have been successfully studied using random network models, in which the interactions among neurons are assumed to be random. However, the dynamics of single units are usually described using over-simplified models, which might not capture several salient features of real neurons. Here, we show how accounting for richer single-neuron dynamics results in shaping the network dynamics and determines which signals are better transmitted. We focus on adaptation, an important mechanism present in biological neurons that consists in the decrease of their firing rate in response to a sustained stimulus. Our mean-field approach reveals that the presence of adaptation shifts the network into a previously unreported dynamical regime, that we term resonant chaos, in which chaotic activity has a strong oscillatory component. Moreover, we show that this regime is advantageous for the transmission of low-frequency signals. Our work bridges the microscopic dynamics (single neurons) to the macroscopic dynamics (network), and shows how the global signal-transmission properties of the network can be controlled by acting on the single-neuron dynamics. These results paves the way for further developments that include more complex neural mechanisms, and considerably advance our understanding of realistic neural networks.

2019

Neural assemblies as core elements for modeling neural networks in the brain

Hesam Setareh

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important role in information processing. Therefore, it is worthwhile to investigate modeling of neural networks. Experiments extract different kinds of datasets (ranging from pair-wise connectivity to membrane potential of individual neurons) and provide an understanding of neuronal activity. However, due to technical limitations of experiments, and complexity and variety of neural architectures, the experimental datasets do not yield a model of neural networks on their own. Roughly speaking, the experimental datasets are not enough for modeling neural networks. Therefore, in addition to these datasets, we have to utilize assumptions, hand-tuned features, parameter tuning and heuristic methods for modeling networks. In this thesis, we present different models of neural networks that are able to produce several behaviors observed in mammalian brain and cell cultures, e.g., up-state/down-state oscillations, different stimulus-evoked responses of cortical layers, activity propagation with tunable speed and several activity patterns of mice barrel cortex. An element which is embedded in all of these models is a network feature called neural assembly. A neural assembly is a group (also called population) of neurons with dense recurrent connectivity and strong internal synaptic weights. We study the dynamics of neural assemblies using analytical approaches and computer simulations. We show that network models containing assemblies exhibit dynamics similar to activity observed in the brain.

EPFL2017

Structure and dynamics of the neocortical microcircuit connectivity

Jean-Vincent Le Bé

The neocortex is the most computationally advanced portion of the brain. It is currently assumed to be composed of a large number of "cortical columns" – intricate arrangements of cortical neurons approximately 300-500 µm in diameter and 2-5 mm in height in humans – that might serve as the elementary computational unit of the neocortex. Understanding the computation performed by this microcircuit is one of the keys to our comprehension of the brain. The so-called cortical column is not a static entity, however, and it evolves throughout a lifetime and continually adapts to the information from its cortical environment. Despite the differences between cortical columns across the cortex, a number of common features have been identified: a laminar structure, the dynamics of connections between identified neurons or the mechanisms for these connections to be modified (that give its specificity to each microcircuit). This thesis presents the description of the differential connectivity and synaptic dynamics across cell populations and the long term neuronal rewiring in a particular neuronal population within the cortical column. Somatic whole cell recordings have been performed to probe the connectivity, synaptic dynamics and plasticity of the connections in the rat neocortex. Two populations of layer V pyramidal neurons were studied in particular: cortico-callosally projecting pyramidal cells (CCPs) and thick tufted pyramidal cells (TTCs). The first major results from this work revealed the degree of connectivity and the linear dynamics of the CCPs population when compared to the TTCs. CCPs have nearly 4 times fewer interconnections and subsequent post-synaptic potentials were less decreasing in amplitude along a pre-synaptic series of action potentials. Long term configuration of TTC networks was explored. These experiments show for the first time the emergence of new functional synaptic connections between TTCs within hours. Activation of the slice by glutamate greatly increases their rate of emergence and this work demonstrated that metabotropic glutamate receptor 5 (mGluR5) activation and action potential firing are required for new connections to be formed in this experimental protocol. Newly formed connections respond in a more linear fashion and have weaker post-synaptic influence than already existing connections. Pre-existing connections are also modified after stimulation, requiring mGluR5, action potentials as well as α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors activation. The activation of group III metabotropic glutamate receptors (mGluRs) however results in a decrease in the strength of connections. Finally, the influence of inhibitory interneurons on the activity and connectivity between TTCs was also investigated. The results of this study show that firing of inhibitory interneurons can be triggered by the input of only one pyramidal cell. They further show that stimulation of a single TTC can result in an hyperpolarization of the post-synaptic TTC mediated by an interneurone. When the pre-synaptic neuron is also directly connected to the post-synaptic neuron with an excitatory synapse, the indirect inhibitory connection serves to curtail the excitatory response. This work has provided a new insight into the dynamic nature of the cortical microcircuitry, showing that it evolves rapidly and can adapt, reconfigure and rewire itself in remarkably short time-spans. It also describes the variety of dynamics exhibited by the different types of pyramidal cells, due to either the projecting site specificity or to the action of an intermediate interneuron.