A null model of the mouse whole-neocortex micro-connectome

In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.

Sensory memory in a model of a cortical column

Julien Mayor

Sensory memory is the first step of a complex memorization process. Sensory information is buffered in a "sensory store" for a short period. Then part of it is transferred to working memory, where information can be actively processed and, eventually, through its rehearsal, can be memorized and stored in long-term memory. Sensory memory is characterized by a high capacity and a short temporal extension. In this work we show that such a transient buffering of information is well captured in models of cortical microcircuits. Sensory information stays for a short time in the perturbations it induced to the neural activity. Two fundamental characteristics of cortical columns appear to be useful to this purpose. The first one is an approximate balance of neuronal excitation and inhibition. The second one is the sparseness of cells recurrent connections. Together, these two discriminants ensure a broad set of complex dynamical behaviours of the neural network along with the presence of a phase of sustained non-oscillatory activity with very irregular spike trains in individual neurons. We show that at the vicinity of a phase transition from a quiescent state to a phase of chaotic spiking activity, global stimuli can be buffered in the "macroscopic" neuronal activity. However if the sensory input only excites a subpart of the network, efficient information buffering is made using the detailed spiking activity of every neuron in the network. The "microscopic" structure of the network is therefore needed in order to buffer sensory information. In most situations, addition of noise enhances the buffering performance and the computational power of the network. This effect is known in the physical literature as "stochastic resonance". If the network has to achieve complex transformation of its inputs, stochastic resonance is shown to be an emergent property of the population of neurons. Both the very irregular spiking activity present in vivo and the balanced excitation and inhibition measured in cortical columns have a new functional relevance in being the neuronal substrate of an efficient sensory memory .

EPFL2005

Analysis of information processing in the olfactory bulb by in vivo experiments and theoretical modelling

Brice Bathellier

Much information is conveyed to animals by the diverse molecules propagated in their environment. This information is transmitted and treated within the olfactory system to be utilized by the rest of the brain. In this process, the sensory flow arriving from the nose first encounters the olfactory bulb. This thesis studies different aspects of the encoding and treatment of olfactory information in the bulb by a combination of experimental and theoretical approaches. The olfactory bulb consists of two functional stages: an input stage where the nerve terminals from the sensory neurons of the nose are spatially arranged in a precise manner, and an output stage corresponding to the neurons which propagate the processed information towards the more central brain areas. The first part of this thesis is focused on the development of an image processing technique to precisely map the input stage. In a second part, output neurons activity in responsive areas of the bulb was recorded in anaesthetized mice. The data obtained indicate complex dynamics in neural activity on several time scales. We show that the combination of mean firing rates from a large enough ensemble of neurons allows to accurately predict the odor presented to the animal. We also show that temporal variables related to the dynamics give very little information complementary to the ensemble rate code, suggesting that these variables are not read by brain structures downstream to the bulb. In a third part, we developed a numerical model of the olfactory bulb to understand its fast oscillatory dynamics. The model proposes a mechanism based on the negative feedback from the interneurons that regulate output neurons. Some predictions of the model are checked experimentally. An analytical interpretation of the model is also proposed and its generalization to the analysis of fast oscillation in other parts of the brain is discussed.

EPFL2007

Statistical models of effective connectivity in neural microcircuits

Joao Emanuel Felipe Gerhard

To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured. I describe a statistical framework to estimate circuit connectivity directly from measured activity patterns. Because we usually only have access to a small subset of neurons of a circuit, the estimated connectivity reflects an effective coupling, that is, how spiking activity in one neuron effectively modulates the activity of other neurons. For small circuits, like the nervous system of the crab that controls gut muscle activity, we could show that it is possible to derive the actual physiological connectivity from observing neural activity alone. This was achieved with a regression model adapted to the spike train structure of the data (Generalized Linear Model, GLM). This is the first successful demonstration of a network inference algorithm on a physiological circuit for which the connections are known. For larger networks, like cortical networks, the concept of effective connectivity - though not equivalent to structural connectivity - is useful to characterize the functional properties of the network. For example, we may assess whether networks have small-world or scale-free properties that are important for information processing. We find that cortical networks show a small, but significant small-world structure by applying our estimation framework on multi-electrode recordings from the visual system of the awake monkey. Finally, we study how well spike dynamics and network topology can be inferred from noisy calcium imaging data. We applied our framework on simulated data to explore how uncertainties in spike inference due to experimental parameters affect estimates of network connectivity and their topological features. We find that considerable information about the connectivity can be extracted from the neural activity, but only if spikes are reconstructed with high temporal precision. We then study how errors in the network reconstruction affect the estimation of a number of graph-theoretic measures. Our findings provide a benchmark for future experiments that aim to reliably infer neuronal network properties.

EPFL2014