Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition

The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding.

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

Developmental and spatial expression pattern of syntaxin 13 in the mouse central nervous system

Harald Hirling, Juan-Carlos Sarria

Vesicular transport involves SNARE (soluble- N-ethylmaleimide-sensitive-factor-attachment-protein-receptor) proteins on transport vesicles and on target membranes. Syntaxin 13 is a SNARE enriched in brain, associated with recycling endosomes; its overexpression in PC12 cells promotes neurite outgrowth. This suggests an important role for receptor recycling during neuronal differentiation. Here we describe the spatiotemporal pattern of syntaxin 13 expression during mouse brain development. During early embryogenesis (E12-E15), it was found in the forebrain ventricular zone and in primary motor and sensory neurons in the brainstem, spinal cord and sensory ganglia. In the forebrain at E15, syntaxin 13 was not detected in neuroblasts in the intermediate zone of the embryonic hemispheric wall, while there was labeling in cortical neurons in deeper layers starting at E15-18, and progressively in later-generated neurons up to layer II around P6. Syntaxin 13 reached maximal expression in all brain divisions at about P7, followed by a decrease, with heterogeneous neuron populations displaying various staining intensities in adult brain. While usually restricted to the soma of neurons, we transiently detected syntaxin 13 in dendrites of pyramidal neurons during the first postnatal week. In conclusion, the developmentally regulated syntaxin 13 expression in various neuronal populations is consistent with its involvement in endocytic trafficking and neurite outgrowth.

2002

Population response in autaptic and disynaptic inhibitory cortical microcircuitry: a computational study

Vincent Delattre

The dynamic of the neuronal response is highly influenced by the nature and the amount of noise that a neuron experienced, this noise being either external and internal to the neuron. The 10 000 synaptic afferents may not fire synchronously, hence, cause the synaptic noise, and ion channels are intrinsically noisy. Recent discoveries at the cellular- and microcircuit-levels describing several stereotypical pathways were reported, such as target-specific homosynaptic plasticity, disynaptic inhibition between pyramidal neurons, and differential self-inhibition of interneurons. This study proposes a large exploration of neuronal dynamical response properties, and describes how they are affected by these features. In particular, the introduction of heterogeneity in the dynamic of sodium channels affects the filtering behavior of the neuronal response, whereas inhibitory autapses seem to have less impact on the dynamic. However, autapses may have an important role for enhancing the spike time reliability by reducing the intrinsic channel noise, allowing the timing precision relevant to several behaviors, as suggest simulations of simple microcircuitry architectures and neocortical slice recordings

2008