CNS 2013 Paris: Tutorials Program

List of tutorials

T1: Neural mass and neural field models

Time: 9:00–12:20, 13:30–16:50
Space: Curie

Axel Hutt (INRIA Nancy, France)
Viktor Jirsa (Institut de Neuroscience de Systemes, France)
John Terry (University Exeter, UK)
Wolfram Erlhagen (University Minho, Portugal)

T2: Theory of correlation transfer and correlation structure in recurrent networks

Time: 9:00–12:20, 13:30–16:50
Space: Curie

Ruben Moreno-Bote (Foundation Sant Joan de Déu, Barcelona, Spain)
Moritz Helias (Research Center Jülich, Germany)

T3: Modeling and interpretation of extracellular potentials

Time: 9:00–12:20, 13:30–16:50
Space: Grignard

Gaute T. Einevoll (Norwegian University of Life Sciences, Ås, Norway)
Szymon Łęski  (Nencki Institute of Experimental Biology, Warsaw, Poland)
Espen Hagen (Norwegian University of Life Sciences, Ås, Norway)

T4: Probabilistic inference as a neural-computing paradigm

Time: 13:30–16:50
Space: Grignard

Dejan Pecevski (Graz University of Technology, Graz, Austria)

T5: Brain Activity at Rest: Dynamics and Structure of the Brain in Health and Disease

Time: 9:00–12:20
Space: Grignard

Gustavo Deco (Universitat Pompeu Fabra, Spain)

T6: Developing neuron and synapse models for NEST

Time: 9:00–12:20, 13:30–16:50
Space: Grignard

Abigail Morrison (Research Center Jülich, Germany)
Jochen Eppler (Research Center Jülich, Germany)

T7: Advanced modelling of spiking neural networks with BRIAN

Time: 9:00–12:20, 13:30–16:50
Space: Curie

Romain Brette (École Normale Supérieure, Paris, France)
Marcel Stimberg (École Normale Supérieure, Paris, France)
Victor Benichoux (École Normale Supérieure, Paris, France)
Cyrille Rossant (University College London, UK)
Nelson Cortés Hernández (École Normale Supérieure, Paris, France)
Dan Goodman (Massachusetts Eye and Ear Infirmary, Boston, USA)
Bertrand Fontaine (Albert Einstein College of Medicine, New York, USA)

(see schedule)

T8: Managing complex workflows in neural simulation and data analysis

Time: 9:00–12:20, 13:30–16:50
Space: Curie

Andrew P. Davison (UNIC, CNRS, Gif sur Yvette)
Sonja Grün (Research Center Jülich, Germany)
Michael Denker (Research Center Jülich, Germany)

T9: Massively Parallel Time Encoding and Channel Identification Machines

Time: 9:00–12:20, 13:30–16:50
Space: Grignard

Aurel A. Lazar (Columbia University, New York, US)

Tutorial abstracts

T1: Neural mass and neural field models (pdfs are here)

Axel Hutt (INRIA Nancy, France)
Viktor Jirsa (Institut de Neuroscience de Systemes, France)
John Terry (University Exeter, UK)
Wolfram Erlhagen (University Minho, Portugal)

The brain exhibits dynamical processes on different spatial and temporal scales. Single neurons have a size of tens of micrometers and fire during few milliseconds, whereas macroscopic brain activity, such as encephalographic data or the BOLD response in functional Magnetic Resonance Imaging, evolve on a millimeter or centimeter scale during tens of milliseconds. To understand the relation between the two dynamical scales, the mesoscopic scale of neural populations between these scales is helpful. Moreover, it has been found experimentally that neural populations encode and decode cognitive functions. The tutorial presents a specific type of rate-coding models which is both mathematically tractable and verifiable experimentally. It starts with a physiological motivation of the model, followed by mathematical analysis techniques applied to explain experimental data and applications to epilepsy and robotics.

In detail:

Fundamentals of Neural Mass and Neural Field Models: physiological motivation and mathematical descriptions (9:00 - 10:30 , Axel Hutt)

The talk will motivate physiologically the mathematical description of neural populations based on microscopic neural properties. Several different mathematical models will be discussed, explained and compared. The major aim of this presentation is to explain the standard models found in the literature in terms of mathematical elements and physiological assumptions.

Pattern formation in neural network systems and applications to movement dynamics (10:45 - 12:20 , Viktor Jirsa)

We will introduce and systematically evaluate the mechanisms underlying pattern formation in neuronal networks. A particular focus will be given to the contribution of network connectivity. The conditions will be derived aiding in the emergence of low-dimensional sub-spaces, in which nonlinear dynamic flows are constrained to manifolds. Applications of these concepts and experimental examples will be provided from the field of human movement sciences.

Brain Networks in Epilepsy: Fusing models and Clinical Data (13:30 - 15:00 , John Terry)

We will explore how both physiological and phenomenological mathematical models can be developed to understand the mechanisms that underpin transitions in clinically recorded EEG between activity corresponding to normal function and the pathological activity associated with epilepsy. We present evidence that the bifurcation sequences of physiologically inspired models give rise to dynamical sequences that precisely map those observed in both focal and generalised seizures. We further demonstrate that seizure frequency can be understood through the relationship between the dynamics of brain regions and the network structures that connect them. Our mathematical models help to explain previously counterintuitive experimental studies demonstrating loss of connectivity paradoxically makes generalised seizures more likely.

The dynamic neural field approach to cognitive robotics (15:15 - 16:45 , Wolfram Erlhagen)

In recent years, there has been an increased interest by part of the robotics community in using the theoretical framework of dynamic neural fields to develop neuro-inspired control architectures for autonomous agents. The formation of self-sustained activity patterns in neural populations explained by the theory offers a systematic way to endow robots with cognitive functions such as working memory, decision making, prediction and anticipation. In the tutorial, I will present a Dynamic Neural Field-architecture for natural human-robot collaboration that is heavily inspired by neuro-cognitive mechanisms supporting joint action in humans and other primates. The model is formalized as a large-scale network of reciprocally connected neural populations, each governed by a classical field dynamics of Amari type.

T2: Theory of correlation transfer and correlation structure in recurrent networks

Ruben Moreno-Bote (Foundation Sant Joan de Déu, Barcelona, Spain)
Moritz Helias (Research Center Jülich, Germany)

In the first part, we will study correlations arising from pairs of neurons sharing common fluctuations and/or inputs. Using integrate-and-fire neurons, we will show how to compute the firing rate, auto-correlation and cross-correlation functions of the output spike trains. The transfer function of the output correlations given the inputs correlations will be discussed. We will show that the output correlations are generally weaker than the input correlations [Moreno-Bote and Parga, 2006], that the shape of the cross-correlation functions depends on the working regime of the neuron [Ostojic et al., 2009; Helias et al., 2013], and that the output correlations strongly depend on the output firing rate of the neurons [de la Rocha et al, 2007]. We will study generalizations of these results when the pair of neurons is reciprocally connected.

In the second part, we will consider correlations in recurrent random networks. Using a binary neuron model [Ginzburg & Sompolinsky, 1994], we explain how mean-field theory determines the stationary state and how network-generated noise linearizes the single neuron response. The resulting linear equation for the fluctuations in recurrent networks is then solved to obtain the correlation structure in balanced random networks. We discuss two different points of view of the recently reported active suppression of correlations in balanced networks by fast tracking [Renart et al., 2010] and by negative feedback [Tetzlaff  et al., 2012].  Finally, we consider extensions of the theory of correlations of linear Poisson spiking models [Hawkes, 1971] to the leaky integrate-and-fire model and present a unifying view of linearized theories of correlations [Helias et al, 2011].

At last, we will revisit the important question of how correlations affect information and vice-versa [Zohary et al, 1994] in neuronal circuits, showing novel results about information content in recurrent networks of integrate-and-fire neurons [Moreno-Bote and Pouget, Cosyne abstracts, 2011].

References:

• de la Rocha et al. (2007), Correlation between neural spike trains increases with firing rate, Nature 448:802-6
• Ginzburg & Sompolinsky (1994), Theory of correlations in stochastic neural networks, PRE 50:3171-3190
• Hawkes (1971), Point Spectra of Some Mutually Exciting Point Processes, Journal of the Royal Statistical Society Series B 33(3):438-443
• Helias et al. (2011), Towards a unified theory of correlations in recurrent neural networks, BMC Neuroscience 12(Suppl 1):P73
• Helias et al. (2013), Echoes in correlated neural systems, New Journal of Physics 15(2):023002
• Moreno-Bote & Parga (2006), Auto- and crosscorrelograms for the spike response of leaky integrate-and-fire neurons with slow synapses, PRL 96:028101

• Ostojic et al. (2009), How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains, J Neurosci 29(33):10234-10253
• Renart et al. (2010), The Asynchronous State in Cortical Circuits, Science 327(5965):587-590
• Shadlen & Newsome (1998), The variable discharge of cortical neurons: implications for connectivity, computation, and information coding, J Neurosci 18:3870-96

• Tetzlaff et al. (2012), Decorrelation of neural-network activity by inhibitory feedback, PLoS Comp Biol 8(8):e1002596, doi:10.1371/journal.pcbi.1002596

• Zohary et al. (1994), Correlated Neuronal Discharge Rate and Its Implications for Psychophysical Performance, Nature 370:140-14

T3: Modeling and interpretation of extracellular potentials

Gaute T. Einevoll (Norwegian University of Life Sciences, Ås)
Szymon Łęski (Nencki Institute of Experimental Biology, Warsaw)
Espen Hagen (Norwegian University of Life Sciences, Ås)

While extracellular electrical recordings have been the workhorse in electrophysiology, the interpretation of such recordings is not trivial. The recorded extracellular potentials in general stem from a complicated sum of contributions from all transmembrane currents of the neurons in the vicinity of the electrode contact. The duration of spikes, the extracellular signatures of neuronal action potentials, is so short that the high-frequency part of the recorded signal, the multi-unit activity (MUA), often can be sorted into spiking contributions from the individual neurons surrounding the electrode. However, no such simplifying feature aids us in the interpretation of the low-frequency part, the local field potential (LFP). To take a full advantage of the new generation of silicon-based multielectrodes recording from tens, hundreds or thousands of positions simultaneously, we thus need to develop new data analysis methods grounded in the underlying biophysics.  This is the topic of the present tutorial.

In the first part of this tutorial we will go through

• the biophysics of extracellular recordings in the brain,

• a scheme for biophysically detailed modeling of extracellular potentials and the application to modeling single spikes [1-3], MUA [4] and LFP, both from single neurons [5] and populations of neurons [4,6], and
• methods for

• estimation of current source density [7] from LFP data, such as the iCSD  [8-10] and kCSD methods [11], and

• decomposition of recorded signals in cortex into contributions from various laminar populations, i.e., (i) laminar population analysis (LPA) [12] based on joint modeling of LFP and MUA, and (ii) a novel scheme using LFP and known constraints on the synaptic connections [13]

In the second part the participants will get demonstrations and hands-on experience with

• LFPy (compneuro.umb.no/LFPy), a versatile tool based on Python and the simulation program NEURON [14] (www.neuron.yale.edu) for calculation of extracellular potentials around neurons, and
• tools for iCSD analysis, in particular,
• CSDplotter (for linear multielectrodes [8]) (software.incf.org/software/csdplotter)
• iCSD 2D (for 2D multishank electrodes [10]) (software.incf.org/software/icsd-2d)

References:

• [1] G Holt, C Koch, J Comp Neurosci 6:169 (1999)

• [2] J Gold et al, J Neurophysiol 95:3113 (2006)

• [3] KH Pettersen and GT Einevoll, Biophys J 94:784 (2008)

• [4] KH Pettersen et al, J Comp Neurosci 24:291 (2008)

• [5] H Lindén et al, J Comp Neurosci 29: 423 (2010)

• [6] H Lindén et al, Neuron 72:859 (2011)

• [7] C Nicholson and JA Freeman, J Neurophsyiol 38:356 (1975)

• [8] KH Pettersen et al, J Neurosci Meth 154:116 (2006)

• [9] S Łęski et al, Neuroinform 5:207 (2007)

• [10] S Łęski et al, Neuroinform 9:401 (2011)

• [11] J Potworowski et al, Neural Comp  24:541 (2012)

• [12] GT Einevoll et al, J Neurophysiol 97:2174 (2007)

• [13] SL Gratiy et al, Front Neuroinf  5:32 (2011)

• [14] ML Hines et al, Front Neuroinf 3:1 (2009)

T4: Probabilistic inference as a neural-computing paradigm

Dejan Pecevski (Graz University of Technology, Graz, Austria)

Probabilistic inference has been proven to be a very suitable framework for explaining many of the computations that the brain performs in face of great amount of uncertainty present in the sensory inputs and its internal representations of the world [Rao et al, 2002, Fiser et al. 2010, Tenenbaum et al, 2011, Kording et al, 2004]. However, it still remains an open question how these probabilistic inference computations are implemented in the neural circuits of the brain. In this tutorial we will present recent results that give new perspectives on how probabilistic inference and learning could be carried out by networks of spiking neurons.
The tutorial is organized in two parts. In the first part we will briefly overview several basic topics from probabilistic inference, including graphical models, belief propagation, Markov chain Monte Carlo methods and Gibbs sampling. In the second part we will start by describing a recently developed framework for probabilistic inference with stochastic networks of spiking neurons that performs Markov chain Monte Carlo sampling, called neural sampling [Buesing et al. 2011]. We will further show that by introducing specific network motifs or dendritic computation in the spiking neural networks, they can be made to perform neural sampling in general graphical models that exhibit also higher-order relations between the random variables [Pecevski et al, 2011]. We will then continue discussing results about learning probabilistic models, in particular a study where it was shown that STDP in a stochastic winner-take-all network structure implements the expectation-maximization algorithm, a powerful machine learning algorithm for unsupervised learning [Nessler et al., 2009]. In [Habenschuss et al., 2012] the model from [Nessler et al. 2009] was extended with homeostatic plasticity of the neuronal excitabilities, which improved the performance and robustness of learning. It was also demonstrated theoretically that this extended model can be understood as performing expectation-maximization under posterior constraints. Finally, we will show how many winner-take-all network motifs as in [Habenschuss et al., 2012] can be combined together in a larger recurrent spiking neural network which is capable of solving a generic learning task: to learn a probabilistic model from input data streams, where the dependencies in the probabilistic model can be a priori based on any arbitrary graphical model structure.

References:

• Rao, R. P. N., Olshausen, B. A. and Lewicki, M. S. eds., Probabilistic models of the brain: Perception and neural function, Mit Press, 2002

• Fiser, J., Berkes, P., Orbán, G. and Lengyel, M., Statistically optimal perception and learning: from behavior to neural representations, Trends Cogn Sci, 2010, 14, 119-130
  

• Tenenbaum, J. B., Kemp, C., Griffiths, T. L. and Goodman, N. D., How to Grow a Mind: Statistics, Structure, and Abstraction, Science, 2011, 331, 1279-1285
  

• Kording, K. P. and Wolpert, D. M., Bayesian integration in sensorimotor learning, Nature, 2004, 427, 244-247
  

• Buesing, L., Bill, J., Nessler, B. and Maass, W., Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons, PLoS Comput Biol, 2011, 7, e1002211.
  

• Pecevski, D., Buesing, L. and Maass, W., Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons, PLoS Comput Biol, 2011, 7, e1002294
  

• Nessler, B., Pfeiffer, M. and Maass, W., STDP enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems 22, 2009, 1357-1365
  

• Habenschuss, S., Bill, J. and Nessler, B., Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints, Advances in Neural Information Processing Systems 25, 2012, 782-790

T5: Brain Activity at Rest: Dynamics and Structure of the Brain in Health and Disease

Gustavo Deco (Universitat Pompeu Fabra, Spain)

Perceptions, memories, emotions, and everything that makes us human, demand the flexible integration of information represented and computed in a distributed manner. The human brain is structured into a large number of areas in which information and computation is highly segregated. Normal brain function requires the integration of functionally specialized but widely distributed brain areas. We contend that the functional and encoding roles of diverse neuronal populations across areas are subject to the intra- and inter-cortical dynamics. In this tutorial, we try to elucidate precisely the interplay and mutual entrainment between local brain area dynamics and global network dynamics, in order to understand how segregated distributed information and processing is integrated. We can deepen our understanding of the mechanisms underlying brain functions by complementing structural and activation based analysis with dynamics. In particular, a large body of fMRI, MEG, EEG, and optical imaging experiments reveal that the ongoing brain activity of the brain at rest is not trivial but highly structured in very specific spatio-temporal patterns known as Resting State Networks (RSN). Indeed, the Functional Connectivity (FC) at rest, i.e. the spatial correlation matrix between the temporal signals reflecting spontaneous brain activity at different positions, is topologically very well structured according to the underlying RSNs. A profound understanding of these operations will help to elucidate the computational principles underlying higher brain functions and their breakdown in brain diseases. Thus, we will discuss the effects on the resting state of lesions and different type of damage in neuropsychiatric disorder.

T6: Developing neuron and synapse models for NEST

Abigail Morrison (Research Center Jülich, Germany)
Jochen Eppler (Research Center Jülich, Germany)

The neural simulation tool NEST [1] is a simulator for heterogeneous networks of point neurons or neurons with a small number of electrical compartments aiming at simulations of large neural systems. It is implemented in C++ and runs on a large range of architectures from single-processor desktop computers to large clusters with thousands of processor cores. This tutorial is an extension course for anybody who is already working with either the neural simulation tool NEST, or has other experience with the simulation of networks of point neuron models. Some programming background in C++ is helpful but not required. We will start with a refresher on setting up networks of neurons focussing on customising the neuronal and synaptic parameters before, during and after creation of the network elements. In the second part we will provide a hands-on demonstration of how to develop a new neuron or synapse model for NEST. We will start from a skeleton and follow the process through to using the new model in a network simulation.

References:

• [1] Marc-Oliver Gewaltig and Markus Diesmann (2007) NEST (Neural Simulation Tool), Scholarpedia 2 (4), p. 1430

T7: Advanced modelling of spiking neural networks with BRIAN

Romain Brette (École Normale Supérieure, Paris, France)
Marcel Stimberg (École Normale Supérieure, Paris, France)
Victor Benichoux (École Normale Supérieure, Paris, France)
Cyrille Rossant (University College London, UK)
Nelson Cortés Hernández (École Normale Supérieure, Paris, France)
Dan Goodman (Massachusetts Eye and Ear Infirmary, Boston, USA)
Bertrand Fontaine (Albert Einstein College of Medicine, New York, USA)

(see schedule)

BRIAN [1,2] is a simulator for spiking neural networks, written in the Python programming language. It focuses on making the writing of simulation code as quick as possible and on flexibility: new and non-standard models can be readily defined using mathematical notation. This tutorial will present the current state of development of BRIAN and will enable participants to adapt and extend Brian to their needs. It will also cover existing Brian extensions (brian hears [3], model fitting toolbox [4], compartmental modelling) and introduce "best practices" for complex simulations. Furthermore, it will present strategies for improving the speed of simulations by using Brian's C code generation mechanism [5].
We strongly encourage interested participants to mail us further suggestions or comments beforehand: marcel.stimberg{at}ens.fr. Note that this tutorial is not meant as a beginner's introduction to BRIAN, participants should either already use BRIAN in their research or be at least familiar with it (e.g. by working through the tutorials available at [1]) before the tutorial starts.

References:

• [1] http://briansimulator.org

• [2] Goodman DFM and Brette R (2009). The Brian simulator. Front Neurosci doi:10.3389/neuro.01.026.2009.

• [3] Fontaine B, Goodman DFM, Benichoux V, Brette R (2011). Brian Hears: online auditory processing using vectorisation over channels. Frontiers in Neuroinformatics 5:9. doi:10.3389/fninf.2011.00009

• [4] Rossant C, Goodman DFM, Platkiewicz J and Brette, R. (2010). Automatic fitting of spiking neuron models to electrophysiological recordings. Frontiers in Neuroinformatics. doi:10.3389/neuro.11.002.2010

• [5] Goodman, DFM. (2010). Code generation: a strategy for neural network simulators. Neuroinformatics. doi:10.1007/s12021-010-9082-x
  

T8: Managing complex workflows in neural simulation and data analysis

Andrew P. Davison (UNIC, CNRS, Gif sur Yvette)
Sonja Grün (Research Center Jülich, Germany)
Michael Denker (Research Center Jülich, Germany)

In our attempts to uncover the mechanisms that govern brain processing on the level of interacting neurons, neuroscientists have taken on the challenge of tackling the sheer complexity exhibited by neuronal networks. Neuronal simulations are nowadays performed with a high degree of detail, covering large, heterogeneous networks. Experimentally, electrophysiologists can simultaneously record from hundreds of neurons in complicated behavioral paradigms. The data streams of simulation and experiment are thus highly complex; moreover, their analysis becomes most interesting when considering their intricate correlative structure.

The increases in data volume, parameter complexity, and analysis difficulty represent a large burden for researchers in several respects. Experimenters, who traditionally need to cope with various sources of variability, require efficient ways to record the wealth of details of their experiment ("meta data") in a concise and machine-readable way. Moreover, to facilitate collaborations between simulation, experiment and analysis there is a need for common interfaces for data and software tool chains, and clearly defined terminologies. Most importantly, however, neuroscientists have increasing difficulties in reliably repeating previous work, one of the cornerstones of the scientific method. At first sight this ought to be an easy task in simulation or data analysis, given that computers are deterministic and do not suffer from the problems of biological variability. In practice, however, the complexity of the subject matter and the long time scales of typical projects require a level of disciplined book-keeping and detailed organization that is difficult to keep up.

The failure to routinely achieve replicability in computational neuroscience (probably in computational science in general, see Donoho et al., 2009 [1]) has important implications for both the credibility of the field and for its rate of progress (since reuse of existing code is fundamental to good software engineering). For individual researchers, as the example of ModelDB has shown, sharing reliable code enhances reputation and leads to increased impact.

In this tutorial we will identify the reasons for the difficulties often encountered in organizing and handling data, sharing work in a collaboration, and performing manageable, reproducible yet complex computational experiments and data analyses. We will also discuss best practices for making our work more reliable and more easily reproducible by ourselves and others -- without adding a huge burden to either our day-to-day research or the publication process.

We will cover a number of tools that can facilitate a reproducible workflow and allow tracking the provenance of results from a published article back through intermediate analysis stages to the original data, models, and/or simulations. The tools that will be covered include Git [2], Mercurial [3], Sumatra [4], VisTrails [5], odML [6], Neo [7]. Furthermore, we will highlight strategies to validate the correctness, reliability and limits of novel concepts and codes when designing computational analysis approaches (e.g., [8-10]).

References:

• [1] Donoho et al. (2009) 15 Years of Reproducible Research in Computational Harmonic Analysis, Computing in Science and Engineering 11: 8-18. doi:10.1109/MCSE.2009.15

• [2] http://git-scm.com/

• [3] http://mercurial.selenic.com/

• [4] http://neuralensemble.org/sumatra

• [5] http://www.vistrails.org/

• [6] http://www.g-node.org/projects/odml

• [7] http://neuralensemble.org/neo

• [8] Pazienti and Grün (2006) Robustness of the significance of spike correlation with respect to sorting errors. Journal of Computational Neuroscience 21:329-342.

• [9] Louis et al. (2010) Generation and selection of surrogate methods for correlation analysis. In: Analysis of parallel spike trains. eds. Grün & Rotter. Springer Series in Computational Neuroscience.

• [10] Louis et al. (2010) Surrogate spike train generation through dithering in operational time. Front. Comput. Neurosci. 4: 127. doi: 10.3389/fncom.2010.00127
  

T9: Massively Parallel Time Encoding and Channel Identification Machines

Aurel A. Lazar (Columbia University, New York, US)

Part I: Time Encoding Machines

The nature of the neural code is fundamental to theoretical and systems neuroscience [1]. Can information about the sensory world be faithfully represented by a population of sensory neurons? What features of the stimulus are encoded by a multidimensional spike train? How can these features be decoded? Why does the cochlear nerve carry some 30,000 fibers and the optic nerve some 1,000,000? We will discuss these questions using a class of neural encoding circuits called Time Encoding Machines (TEMs) [2]. TEMs model the encoding of stimuli in early sensory systems with neural circuits with arbitrary connectivity and feedback. These circuits are realized with temporal, spectro-temporal and/or spatio-temporal receptive fields, and biophysical neuron models with stochastic conductances (Hodgkin-Huxley, Morris-Lecar, etc.) [3, 4, 5, 6]. The tutorial will review key theoretical results and provide numerous examples of massively parallel neural encoding circuits and stimulus decoding algorithms with the Time Encoding Machines Toolbox.