Laureline Logiaco

Laureline Logiaco
Columbia University | CU

About

9
Publications
1,404
Reads
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49
Citations
Introduction
I am a theoretical neuroscience post-doctoral researcher currently working at Columbia University. My research develops and tests theories of how the interactions between neurons connected in structured circuits can give rise to powerful computations, with a focus on the neural basis of flexible and complex motor behaviors.
Additional affiliations
January 2016 - April 2020
Columbia University
Position
  • PostDoc Position
Description
  • Models of action selection through the cortex – basal ganglia – thalamus circuit. Neuronal network dynamics: theory and validation.
September 2013 - December 2014
EDX / Ecole Polytechnique Fédérale de Lausanne
Position
  • Research Assistant
Description
  • Teaching assistant for the Massive Open Online Course 'Neuronal Dynamics' on EDX; professor: W. Gerstner. Response to student’s questions, participation to the design of quizzes and tests.
August 2011 - December 2015
Pierre and Marie Curie University - Paris 6 / Ecole Polytechnique Fédérale de Lausanne, Switzerland
Position
  • PhD Student
Description
  • Theroretical analysis and experimental validation for the role of fine temporal spiking patterns in the modulation of neuronal networks subserving cognitive control.
Education
September 2011 - December 2015
Pierre and Marie Curie University - Paris 6 / Ecole Polytechnique Fédérale de Lausanne
Field of study
  • Computational Neuroscience
September 2009 - June 2011
Ecole Normale Supérieure de Paris
Field of study
  • Neuroscience - Cognitive Science - Computational neuroscience
September 2008 - June 2009
Pierre and Marie Curie University - Paris 6, Ecole Normale Supérieure de Paris
Field of study
  • Life Sciences

Publications

Publications (9)
Preprint
Full-text available
The mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences...
Article
Full-text available
The neural mechanisms that generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. We model the basal ganglia inhibitory...
Article
Full-text available
Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological model of Hebbian plasticity at the location of the synapse. Our model describes the Hebbian cond...
Preprint
Full-text available
We study learning of recurrent neural networks that produce temporal sequences consisting of the concatenation of re-usable "motifs". In the context of neuroscience or robotics, these motifs would be the motor primitives from which complex behavior is generated. Given a known set of motifs, can a new motif be learned without affecting the performan...
Preprint
Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological model of Hebbian plasticity at the location of the synapse. Our computational model describes th...
Conference Paper
Full-text available
Frontal cortical areas control behavioral adaptation to environmental rules. In particular, the dorsal Anterior Cingulate Cortex (dACC) is thought to signal the worth of updating the behavioral strategy to new evidence. Downstream areas would then process and store this signal to ultimately trigger the behavioral change when the opportunity arises...
Thesis
Full-text available
We investigated the putative function of the temporal dynamics of neuronal networks for implementing cognitive processes. First, we characterized the coding properties of spike trains recorded from the dorsal Anterior Cingulate Cortex (dACC) of monkeys. dACC is thought to trigger behavioral adaptation. We found evidence for (i) high spike count va...
Article
Full-text available
The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding immine...
Article
Full-text available
While monkeys perform a task alternating between behavioral adaptation --relying on feedback monitoring and memory of previous choices-- and repetition of previous actions, firing rates in dorsal Anterior Cingulate Cortex (dACC) modulate with cognitive control levels [1]. Further, it has been hypothesized that dorsolateral Prefrontal Cortex (dlPFC)...

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Projects

Projects (2)
Project
We rigorously derive a low-dimensional system of differential equations to approximate the population dynamics of Generalized Linear Model neurons with adaptation.This type of model is able to reproduce the spike trains of pyramidal neurons in response to controlled, dynamic current injection. It includes strong adaptation mechanisms and an exponential non-linearity. We are able to describe the population response for both a stimulation defined by the mean input current in the population, and a stimulation consisting in a dynamic change of the variance of the input current in the population. We show that, under realistic parameters range, a successful theory for the population firing rate needs to account for the heterogeneity of the adaptation state among different neurons over the population. Under reasonable assumptions, we find that the distribution of the instantaneous firing probabilities in the population should be distributed log-normally, with parameters that vary over time. Such distributions were observed experimentally in vivo, suggesting that the assumptions we made to derive our equations are plausible in realistic populations of biological neurons. Finally, we show how studying the variations of the parameters of the log-normal distributions of firing probabilities in the population could uncover the relative contributions of the mean and variance of the stimulation for the downstream neuronal dynamics.
Project
We would like to understand, from a neuronal network dynamics viewpoint, how the thalamocortical architecture permits to generate motor commands. In agreement with experimental results, we assume that the cortical network dynamics produces temporal patterns that are related to the motor commands fed to the muscles. We then consider the motor cortex either taken in isolation, or embedded in a larger recurrent thalamocortical architecture. In both cases, making plausible assumptions on the single units' dynamics, we use a mathematical analysis to study the number of units necessary to generate sequences of temporally complex motor commands. We find that the inclusion of thalamocortical loops, as compared to an unstructured isolated cortical network, permits to greatly reduce the number of units necessary to generate the motor commands. We make predictions about the interactions between the population dynamics in motor cortex and motor thalamus.