Tim Herfurth

Tim Herfurth

Frankfurt, Hessen, Deutschland
1023 Follower:innen 500+ Kontakte

Info

Data Scientist and AI Consultant at Zühlke | PhD in physics/computational neuroscience. |…

Beiträge

Aktivitäten

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Berufserfahrung

  • Zühlke Group Grafik
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    Mannheim, Baden-Württemberg, Germany

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    Raunheim

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    Frankfurt Am Main Area, Germany

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    Paris Area, France

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    Certosa di Pontignano, Siena - Tuscany, Italy

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    Bilbao Area, Spain

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    Rot an der Rot, Germany

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Ausbildung

  • Max Planck Society Grafik
  • research semester: Mean-field theory for quantum spin liquids

Bescheinigungen und Zertifikate

Ehrenamt

  • CorrelAid Grafik

    Data Scientist

    CorrelAid

    –Heute 5 Jahre 6 Monate

    Social Services

    We want to use the potential of data science to help Non-Profit Organisations to increase their impact. In our local volunteer projects we want to bring together Rhein-Main’s Data Scientists with local NPOs. CorrelAid projects are usually run by a small and diverse team of Social Data Scientists who help the NPO to make better evidence-based decisions, understand their target group better, and optimize their processes.

Veröffentlichungen

  • The orbitofrontal cortex maps future navigational goals

    nature

    Accurate navigation to a desired goal requires consecutive estimates of spatial relationships between the current position and future destination throughout the journey. Although neurons in the hippocampal formation can represent the position of an animal as well as its nearby trajectories, their role in determining the destination of the animal has been questioned. It is, thus, unclear whether the brain can possess a precise estimate of target location during active environmental exploration…

    Accurate navigation to a desired goal requires consecutive estimates of spatial relationships between the current position and future destination throughout the journey. Although neurons in the hippocampal formation can represent the position of an animal as well as its nearby trajectories, their role in determining the destination of the animal has been questioned. It is, thus, unclear whether the brain can possess a precise estimate of target location during active environmental exploration. Here we describe neurons in the rat orbitofrontal cortex (OFC) that form spatial representations persistently pointing to the subsequent goal destination of an animal throughout navigation. This destination coding emerges before the onset of navigation, without direct sensory access to a distal goal, and even predicts the incorrect destination of an animal at the beginning of an error trial. Goal representations in the OFC are maintained by destination-specific neural ensemble dynamics, and their brief perturbation at the onset of a journey led to a navigational error. These findings suggest that the OFC is part of the internal goal map of the brain, enabling animals to navigate precisely to a chosen destination that is beyond the range of sensory perception.

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  • Mechanisms of signal encoding and information transmission in cortical neurons

    Universitätsbibliothek Johann Christian Senckenberg

    PhD Thesis (code: https://github.com/t8ch/dissertation-code)

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  • Quantifying encoding redundancy induced by rate correlations in Poisson neurons

    Temporal correlations in neuronal spike trains are known to introduce redundancy to stimulus encoding. However, exact methods to describe how these correlations impact neural information transmission quantitatively are lacking. Here, we provide a general measure for the information carried by correlated rate modulations only, neglecting other spike correlations, and use it to investigate the effect of rate correlations on encoding redundancy. We derive it analytically by calculating the mutual…

    Temporal correlations in neuronal spike trains are known to introduce redundancy to stimulus encoding. However, exact methods to describe how these correlations impact neural information transmission quantitatively are lacking. Here, we provide a general measure for the information carried by correlated rate modulations only, neglecting other spike correlations, and use it to investigate the effect of rate correlations on encoding redundancy. We derive it analytically by calculating the mutual information between a time-correlated, rate modulating signal and the resulting spikes of Poisson neurons. Whereas this information is determined by spike autocorrelations only, the redundancy in information encoding due to rate correlations depends on both the distribution and the autocorrelation of the rate histogram. We further demonstrate that at very small signal strengths the information carried by rate correlated spikes becomes identical to that of independent spikes, in effect measuring the signal modulation depth. In contrast, a vanishing signal correlation time maximizes information but does not generally yield the information of independent spikes. Overall, our study sheds light on the role of signal-induced temporal correlations for neural coding, by providing insight into how signal features shape redundancy and by establishing mathematical links between existing methods.

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  • Information transmission of mean and variance coding in integrate-and-fire neurons

    Neurons process information by translating continuous signals into patterns of discrete spike times. An open question is how much information these spike times contain about signals which modulate either the mean or the variance of the somatic currents in neurons, as is observed experimentally. Here we calculate the exact information contained in discrete spike times about a continuous signal in both encoding strategies. We show that the information content about mean modulating signals is…

    Neurons process information by translating continuous signals into patterns of discrete spike times. An open question is how much information these spike times contain about signals which modulate either the mean or the variance of the somatic currents in neurons, as is observed experimentally. Here we calculate the exact information contained in discrete spike times about a continuous signal in both encoding strategies. We show that the information content about mean modulating signals is generally substantially larger than about variance modulating signals for biological parameters. Our analysis further reveals that higher information transmission is associated with a larger proportion of nonlinear signal encoding. Our study measures the complete information content of mean and variance coding and provides a method to determine what fraction of the total information is linearly decodable.

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  • How linear response shaped models of neural circuits and the quest for alternatives

    Current Opinion in Neurobiology

    • We provide a unified view on linear response theory in neuroscience.
    • We review recent advances of theories combining linear and nonlinear elements.
    • We discuss challenges for establishing specific input–output mappings in neural networks.
    • We highlight the need to identify minimal computational units for network functions.

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Kurse

  • Advanced Course on Data Science & Machine Learning

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  • Advanced Course on Data Science & Machine Learning

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  • Analysis and Models in Neurophysiology, Bernstein Center Freiburg

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  • Computational Approaches to Memory and Plasticity 2015

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  • GRADE initiative "Statistical Learning and Machine Learning Applications"

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  • Moderation as Leadership Competence

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  • Okinawa Computational Neuroscience Course 2016

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  • Succesful employee management - Goethe Research Academy for Early Career Researchers

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  • Udacity online course "Introduction to Machine Learning"​

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Projekte

  • Analysis of neurophysiological data of behaving animals (incl. machine learning techniques)

    –Heute

    I am using methods of time series analysis and machine learning (Fourier analysis, PCA decomposition, clustering, visualization) to analyse and describe neurophysiological data. The data had been acquired from behaving animals in the lab of Dr. Gilles Laurent. The aim is to find neural activity patterns encoding behavioural states.

    Andere Mitarbeiter:innen
    • Gilles Laurent
  • Survival analysis on medical data

    I have been analysing real medical patient data in order to compare different therapeutics of oropharynx cancer. Survival analyses has been done with python's lifelines package.

  • Kaggle kernel - Bengali numerals classification

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    digit classification with CCN in Keras, incl. data augmentation

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  • Kaggle kernel - Correlates of Happiness

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    correlation analysis, simple linear and deep neural network regression

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  • Variational autoencoders for dimensionality reduction of neural data

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    We develop regularized, variational autoencoders and related concepts to identify the low dimensional manifolds on which the neural data recorded from behaving animals can be represented. Moreover, we develop advanced algorithms to determine the importance of single neurons in encoding of sensory or behavioral variables.

Auszeichnungen/Preise

  • M. Sc. degree with honor

    Department of Physics

Sprachen

  • English

    Fließend

  • German

    Muttersprache oder zweisprachig

Organisationen

  • Effective Altruism

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    –Heute

    local group Frankfurt

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