Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation of the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.

Online processing of multiple inputs in a sparsely-connected recurrent neural network

Wulfram Gerstner, Julien Mayor

The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is spread throughout the network. We show that simple output structures are powerful enough to extract the diffuse information from the network. The dimensional blow up, which is crucial in kernel methods, is efficiently achieved by the dynamics of the network itself.

Springer2003

Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses

Michele Giugliano, Eleni Vasilaki

Recent experimental data from the rodent cerebral cortex and olfactory bulb indicate that specific connectivity motifs are correlated with short-term dynamics of excitatory synaptic transmission. It was observed that neurons with short-term facilitating synapses form predominantly reciprocal pairwise connections, while neurons with short-term depressing synapses form predominantly unidirectional pairwise connections. The cause of these structural differences in excitatory synaptic microcircuits is unknown. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). While the impact of STDP on SD was shown in simultaneous neuronal pair recordings in vitro, the mutual interactions between STDP and SD in large networks are still the subject of intense research. Our approach combines an SD phenomenological model with an STDP model that faithfully captures long-term plasticity dependence on both spike times and frequency. As a proof of concept, we first simulate and analyze recurrent networks of spiking neurons with random initial connection efficacies and where synapses are either all short-term facilitating or all depressing. For identical external inputs to the network, and as a direct consequence of internally generated activity, we find that networks with depressing synapses evolve unidirectional connectivity motifs, while networks with facilitating synapses evolve reciprocal connectivity motifs. We then show that the same results hold for heterogeneous networks, including both facilitating and depressing synapses. This does not contradict a recent theory that proposes that motifs are shaped by external inputs, but rather complements it by examining the role of both the external inputs and the internally generated network activity. Our study highlights the conditions under which SD-STDP might explain the correlation between facilitation and reciprocal connectivity motifs, as well as between depression and unidirectional motifs.

Public Library of Science2014

Continuous attractor working memory and provenance of channel models

Alexander Kevin Seeholzer

The brain is a complex biological system composed of a multitude of microscopic processes, which together give rise to computational abilities observed in everyday behavior. Neuronal modeling, consisting of models of single neurons and neuronal networks at varying levels of biological detail, can synthesize the gaps currently hard to constrain in experiments and provide mechanistic explanations of how these computations might arise. In this thesis, I present two parallel lines of research on neuronal modeling, situated at varying levels of biological detail. First, I assess the provenance of voltage-gated ion channel models in an integrative meta-analysis that investigates a backlog of nearly 50 years of published research. To cope with the ever-increasing volume of research produced in the field of neuroscience, we need to develop methods for the systematic assessment and comparison of published work. As we demonstrate, neuronal models offer the intriguing possibility of performing automated quantitative analyses across studies, by standardized simulated experiments. We developed protocols for the quantitative comparison of voltage-gated ion channels, and applied them to a large body of published models, allowing us to assess the variety and temporal development of different models for the same ion channels over the time scale of years of research. Beyond a systematic classification of the existing body of research made available in an online platform, we show that our approach extends to large-scale comparisons of ion channel models to experimental data, thereby facilitating field-wide standardization of experimentally-constrained modeling. Second, I investigate neuronal models of working memory (WM). How can cortical networks bridge the short time scales of their microscopic components, which operate on the order of milliseconds, to the behaviorally relevant time scales of seconds observed in WM experiments? I consider here a candidate model: continuous attractor networks. These can implement WM for a continuum of possible spatial locations over several seconds and have been proposed for the organization of prefrontal cortical networks. I first present a novel method for the efficient prediction of the network-wide steady states from the underlying microscopic network properties. The method can be applied to predict and tune the "bump" shapes of continuous attractors implemented in networks of spiking neuron models connected by nonlinear synapses, which we demonstrate for saturating synapses involving NMDA receptors. In a second part, I investigate the computational role of short-term synaptic plasticity as a synaptic nonlinearity. Continuous attractor models are sensitive to the inevitable variability of biological neurons: variable neuronal firing and heterogeneous networks decrease the time that memories are accurately retained, eventually leading to a loss of memory functionality on behaviorally relevant time scales. In theory and simulations, I show that short-term plasticity can control the time scale of memory retention, with facilitation and depression playing antagonistic roles in controlling the drift and diffusion of locations in memory. Finally, we place quantitative constraints on the combination of synaptic and network parameters under which continuous attractors networks can implement reliable WM in cortical settings.