Computational Neuroscience II: Neural Simulations

Psychology 403C/503C

General Information

- Do I have the necessary computational and biological backgrounds to take this class? Self Test.
- Registering: If you have any concern about your background, feel free to discuss with the instructor.

Topics and Readings (subject to changes)

Week 1: Introduction to Modeling

By means of introduction to the field (optional readings): (Sejnowski, Koch, Churchland, 1988) and (Abbott, 2008)

Other Optional Readings:
(Olsen et al, 2007)
: Examples of models, neuromorphic engineering emphasis.
(Lytton 2008): Multilevel modeling in the context of epilepsy. How to understand a phenomenon at multiple levels of investigation (from channels to large networks) using computational models.
(Gerstner and Naud, 2009): How good are neural models, detailed Vs abstract models.
23 problems in systems neuroscience. Oxford University Press, 2005. van Hemmen and Sejnowski (Eds): As the title suggests...! wide-field view of computational issues from insects to monkeys. Good examples of how basic principles cut across preparation and modeling levels. requires some background in neuroscience. See also Ralph Adolphs (2015) view.
(Einevoll et al, 2019): The Scientific Case for Brain Simulations. A review of neural simulation approaches.
(Kriegeskorte and Douglas, 2018): review of the field with emphasis on cognitive processes
(Hassabis et al, 2017): What are some of the links between Neuroscience and Artificial Intelligence?
(Bassett and Sporns, 2017): Network Neuroscience. The use of Graph Theory modeling approaches to understand the nervous system at multiple levels.

Week 2: Crash Class in Basic Neuroscience

Assigned readings: none

Other Optional Readings: Any introductory text book covering neurons, synapses, neuroanatomy and methods in neuroscience research.
Few links you might be interested in exploring:
- Neuroscience Online (U. of Texas)
- Foundation of Neuroscience (Open Textbook, Michigan State Univ.)
- The Open neuroscience Initiative

Week 3: Introduction to NEURON and the Simulation 'Cycles'.

Assigned Readings: (Hines, Carnevale 2001) (Hines, Carnevale 2000)

Other Optional Readings:
(Brette et al, 2007): Simulation of networks of spiking neurons: A review of tools and strategies
(McDougal et al, 2017): Twenty years of ModelDB and beyond: Building essential modeling tools for the future of neuroscience
(Kumbhar et al, 2019): CoreNEURON : An Optimized Compute Engine for the NEURON Simulator. An optimized version of NEURON for supercomputing platforms.
(Ascoli et al, 2007): NeuroMorpho.Org: A Central Resource forNeuronal Morphologies. A description of, a large database of neural morphologies

Week 4: Sodium, Potassium and the Action Potential

Assigned Readings: (Hodgkin and Huxley, 1952) up to p520. (Naundorf et al. 2006):

Other Optional Readings:
(Schmidt and Knosche, 2019): Action potential propagation and synchronisation in myelinated axons. - Focus on AP propagation
(Schuetze, 1983): The discovery of the Action Potential - A historical perspective of Bernstein and du Bois-Reymond 1st recording of the action potential.
(Bean, 2007): The action potential in mammalian central neurons - A good review of the Na and K channel contributions to a variety of AP shapes and properties.
(SenGupta et al, 2010): Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates - An interesting comparative approcah focused on energy consumption.

Week 5: The Current Flora - HMW1

Assigned Readings: (Khorkova et al. 2007) (Traub et al. 1991, up to Fig 11):

Other Optional Readings:
(Maffeo et al, 2012): Modeling and Simulation of Ion Channels - Mathematically focussed review of the modeling of several types of channels
(Sigg, 2014): Modeling ion channels: Past, present, and future - Mathematically focussed review

Week 6: Calcium Dynamics

Assigned Readings:(Ali and Kwan, 2020) (Grienberger and Konnerth, 2012)

Other Optional Readings:
(Blackwell, 2013): Approaches and tools for modeling signaling pathways and calcium dynamics in neurons.
(Bell et al, 2019): Dendritic spine geometry and spine apparatus organization govern the spatiotemporal dynamics of calcium. A different modeling approach at the molecular level using MCELL.
(Garbo et al, 2007): Calcium signalling in astrocytes and modulation of neural activity. Don't forget the computational roles of glial cells!
(Patoary et al, 2020): Parallel stochastic discrete event simulation of calcium dynamics in neuron. Detailed stochastic simulations of calcium dynamics using NEURON.
(Hayashida and Yagi, 2002): On the Interaction Between Voltage-Gated Conductances and Ca2 Regulation Mechanisms in Retinal Horizontal Cells. example Ca current and internal dynamics using NEURON.

Week 7: No class

Week 8: Morphology and Dendritic Integration (Passive Dendrites) - HMW2

Assigned Readings: (Stuart and Spruston 1998) (London and Hausser, 2005)

Other Optional Readings:
(Rall 2003): Perpective on Neuron Model Complexity. In the HandBook of Brain Theory and Neural Networks (Arbib, Ed).
(Saparov, Schwemmer, 2015): Effects of passive dendritic tree properties on the firing dynamics of a leaky-integrate-and-fire neuron. Simulations on how the branching structure of a neuron can make it bistable.
(Gabbiani and Fox, 2017a) (Gabbiani and Fox 2017b): The passive cable. The Passive Dendrite. For the mathematically enclined.
(Wen and Chklovskii, 2008): A Cost–Benefit Analysis of Neuronal Morphology. Theory that the dendritic structure of a neuron optimizes the synaptic access to the soma.
(Payeur et al, 2019): Classes of dendritic information processing. A short functional review of different types of dendritic functions.
(Jin et al, 2019): ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology. Recent review and software description of dendritic morphology reconstruction.

Week 9: Dendritic Processing (Active Dendrites) - Midterm

Assigned Readings: None.
Note: Papers covered in the midterm are those from week 4-7 included. We start discussing end-of-semester simulations projects around that time.

Other Optional Readings:
(Mainen and Sejnowski, 1996): Influence of dendritic structure on firing pattern in model neocortical neurons. NEURON model showing how active dendrites morphology influence firing patterns.
(Gabbiani and Cox, 2017c): The active dendritic tree. For the mathematically enclined.
(Hu and Vervaeke, 2018): Synaptic Integration in Cortical Inhibitory Neuron Dendrites. Dendritic integration in inhibitory neurons show some differences to those in pyramidal cells.
(Behabadi and Mel, 2014): Mechanisms underlying subunit independence in pyramidal neuron dendrites. Computer simulations and data in vitro (Polsky et al, 2004) and in vivo (Voigt and Harnett, 2020) review of the idea that dendritic segments may function independently.
(Beaulieu-Laroche et al, 2019): Widespread and Highly Correlated Somato-dendritic Activity in Cortical Layer 5 Neurons. Experimental evidence suggesting the opposite view (dendrites and soma are correlated)

Week 10: Synaptic Transmission. The Four Main Types

Assigned Readings: (Wilson Laurent 2005) (Kuhn et. al. 2004)

Other Optional Readings:
(Rumsey and Abbott, 2006): Synaptic Democracy in Active Dendrites.
(Destexhe et al, 1994): Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Comprehensive review of the use of kinetic schemes to model various types of synaptic transmission.
(Li et al, 2019): Dendritic computations captured by an effective point neuron model. A way to capture the complex synaptic integrations of active dendritic tress in a simple one-compartment model neuron.
(Froemke, 2015): Plasticity of Cortical Excitatory-Inhibitory Balance. A good review of the E/I balance and its control, with a focus on long-term plasticity and neuromodulation.
(Bhatia et al, 2019): Precise excitation-inhibition balance controls gain and timing in the hippocampus. Experimental and modeling explorations of the functional consequences of the E/I balance in the CA3-CA1 connections.
(Rothman and Silver, 2014): Data-Driven Modeling of Synaptic Transmission and Integration. Phenomenological models of excitatory synaptic transmission accounting for short-term plasticity, integration and stochasticity.
(Wang et al, 2010): Synchrony of Thalamocortical Inputs Maximizes Cortical Reliability. How synaptic inputs influence the reliability and precision of a multi-compartmental reconstructed neuron.
(Moortgat et al, 2000): Gap junction effects on precision and frequency of a model pacemaker network. an example of the modelingand function of gap junctions in the electric fish.

Week 11: Realistic Synaptic Transmission - Short Term Synaptic Dynamics

Assigned Readings: (Abbott and Regehr, 2004) (Pfister et. al. 2010)

Other Optional Readings:
(Maass and Zador, 1999): Dynamic Stochastic Synapses as Computational Units. and (Varela et al, 1997): A Quantitative Description of Short-Term Plasticity at Excitatory Synapses in Layer 2/3 of Rat Primary Visual Cortex. The model used in class is based on these two modeling approaches.
(Jedrzejewska et al, 2017): Calcium dynamics predict direction of synaptic plasticity in striatal spiny projection neurons. Focus on calcium dynamics and STDP.
(Grangeray-vilmint et al, 2018): Short-Term Plasticity Combines with Excitation–Inhibition Balance to Expand Cerebellar Purkinje Cell Dynamic Range. interaction between short-term dynamics and excitatory/inhibitory balance.
(Fellous and Corral-Frias, 2015): Reliability and Precision Are Optimal for Non-Uniform Distributions of Presynaptic Release Probability. Stochasyic synaptic transmission.
(Abbott, et al, 1997): Synaptic depression and cortical gain control. A functional view of synaptic depression at the network level.
(Zucker and Regehr, 2002): Short-term Synaptic Plasticity. A good revew of the experimental findings and mechanistic theories. An extensive model of synaptic transmission and short-term plasticity by the same group (Pan and Zucker, 2009)

Week 12: Small Networks and Central Pattern Generators - HMW3

Assigned Readings: (Lieb and Frost, 1997) (Purvis et al, 2007)

Other Optional Readings:
(Lamb and Calabrese, 2012): Small is beautiful: models of small neuronal networks. A nice review of the main models of central pattern generators.
(Calin-Jageman et al, 2007): Parameter Space Analysis Suggests Multi-Site Plasticity Contributes to Motor Pattern Initiation in Tritonia. Homework 3 is based on this paper.
(Nadim et al, 1998): Frequency Regulation of a Slow Rhythm by a Fast Periodic Input. Simulation done in class.
(Fellous and Linster, 1998). Computational Models of Neuromodulation. A review of the main approaches to modeling neuromodulation, and a recent review ofthe experimental neuromodulation of various CPGs (Golowash, 2019).

Week 13: No Class

Week 14: Q and A Session: Update on Term-Projects

... no assigned readings ...

Week 15: Simplified Models of Neurons and Large(r) Networks

Assigned Readings: (Koch 1997) (Abesuriya et al, 2018)

Other Optional Readings and Other Resources:
(Gratiy et al, 2019): BioNet: A Python interface to NEURON for modeling large-scale networks.
(Abeysuriya et al, 2019): A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. An example of the use of the Wilson-Cowan model.
(Lukosevicius & Jaeger, 2009): Reservoir computing approaches to recurrent neuralnetwork training. Review of the Reservoir Computing approaches. See also (Tanaka et. al, 2019).
(Burkitt, 2006a): A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input.
(Burkitt, 2006b): A Review of the Integrate-and-fire Neuron Model: II. Inhomogeneous synaptic input and network.
(Brette and Gerstner, 2005): Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity. A variant of the Integrate and Fire model.
(Izhikevich, 2004): Which Model to Use for Cortical Spiking Neurons? A review of the main features of cortical spiking and their implementations by various modeling approaches. Description of the Izhikevich model.
(Yamaku et al, 2019): The stochastic Fitzhugh–Nagumo neuron model in the excitable regime embeds a leaky integrate-and-fire model.
(Moye et al, 2018): Data Assimilation Methods for Neuronal State and Parameter Estimation. Interesting on 2 counts: how to use data assimilation techniques to automatically tune the parameter of a model, and use of the Morris-Lecar model.
A few other Neural Simulators:
- Connectionist Simulator Emergent (Aisa et al, 2008), and its new (as of 2019) repository.
- Large network simulation environment: NEST
- The GENESIS simulator

Week 16: Project Presentations.

Week 17: Final (Th 12/14, 10:30-12:30am)