Table of Contents
Cortical oscillations support sampling-based computations in spiking neural networks
Image copyright: Fig 3; Korcsak-Gorzo, A., Müller, M.G., Baumbach, A., Leng, L., Breitwieser, O.J., van Albada, S.J., Senn, W., Meier, K., Legenstein, R. and Petrovici, M.A., 2022. Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Computational Biology, 18(3), e1009753.
This paper shows how cortical oscillations can help the brain switch between different interpretations of sensory inputs. It provides a relationship between cortical background spiking activity and an effective Boltzmann temperature that links to sampling-based probabilistic inference. A potential role of cortical oscillations in memory replay, multisensory cue combination, and place cell flickering is furthermore analyzed.
Website: Cortical oscillations support sampling-based computations in spiking neural networks
Statistical Field Theory for Neural Networks
Image copyright: Fig. 12.1; Moritz Helias, David Dahmen, Statistical Field Theory for Neural Networks, (2020) https://doi.org/10.1007/978-3-030-46444-8
This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks.
Website: Statistical Field Theory for Neural Networks
Benchmarking neural network simulations with OpenAI Gym
Image copyright: CC-BY; Fig1; Jordan J, Weidel P and Morrison A (2019) A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. Front. Comput. Neurosci. 13:46. doi: 10.3389/fncom.2019.00046
Toolchain connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators.
Website: Benchmarking neural network simulations with OpenAI Gym
Striatal D1/D2 in action selection
Image copyright: CC-BY; Fig 1; Bahuguna, J., Weidel, P. and Morrison, A. (2019), Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework. Eur J Neurosci, 49: 737-753. https://doi.org/10.1111/ejn.14021
We developed a striatal model consisting of D1 and D2 medium spiny neurons (MSNs) and interfaced it to a simulated robot moving in an environment.
Website: Striatal D1/D2 in action selection
Data-driven Layer 2/3 model
Image copyright: CC-BY; Fig 8; Duarte R, Morrison A (2019) Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLOS Computational Biology 15(4): e1006781. https://doi.org/10.1371/journal.pcbi.1006781
We study the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data.
Website: Data-driven Layer 2/3 model
Image copyright: CC-BY; Fig 1; Pauli R, Weidel P, Kunkel S and Morrison A (2018) Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models. Front. Neuroinform. 12:46. doi: 10.3389/fninf.2018.00046
We reproduced a seminal study on the concept of polychrony in spiking Neural Networks. In the paper we discuss barriers to repoducibility and common pitfalls in modeling and how to avoid them. The associated github repository presents a fully reproducible workflow implementing the suggested Ideas.
Website: Resproducing Polychronization
Encoding / Decoding
Image copyright: CC-BY; Figure 1; R. Duarte, M. Uhlmann, D. den van Broek, H. Fitz, K. M. Petersson and A. Morrison, "Encoding symbolic sequences with spiking neural reservoirs," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, pp. 1-8, doi: 10.1109/IJCNN.2018.8489114.
We compare schemes for encoding symbolic input into spiking neural networks and test the ability of networks to discriminate their input as a function of the number of distinct symbols. We also compare decoding performance using different state variables and learning algorithms. Our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.
Website: Encoding / Decoding
Rigorous and Reproducible Neural Network Simulations
Image copyright: CC-BY; Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., Denker, M., 2018. Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90. https://doi.org/10.3389/fninf.2018.00090
In this back-to-back paper, we discuss concepts for verification and validation in computational modeling, and introduce the NetworkUnit software to enable model validation and comparison on the level of population activity.
Website: Rigorous and Reproducible Neural Network Simulations
Reproducing "Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function"
Image copyright: CC-BY; Rostami, Vahid, Junji Ito, Michael Denker, and Sonja Grün. 2017. “[Re] Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function.” ReScience 3 (1): 3. https://doi.org/10.5281/zenodo.583814.
In this [Re] Science paper, the work done in Riehle et al., 1997, Science 278 (5345): 1950–53, is replicated and made available in a reproducible manner. The resulting algorithm for the Python implementation of the Unitary Events analysis method is included in the Elephant library.
Website: Reproducing "Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function"
Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome
Image copyright: Fig 4; Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology 13(2): e1005179. https://doi.org/10.1371/journal.pcbi.1005179; Courtesy: M. Helias
We here investigate the critical role of specific structural links between neuronal populations for the global stability of cortex and elucidate the relation between anatomical structure and experimentally observed activity. Our novel framework enables the evaluation of the rapidly growing body of connectivity data on the basis of fundamental constraints on brain activity and the combination of anatomical and physiological data to form a consistent picture of cortical networks.
Website: Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome
Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models
Image copyright: Fig 5; Rostami V, Porta Mana P, Grün S, Helias M (2017) Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. PLOS Computational Biology 13(10): e1005762. https://doi.org/10.1371/journal.pcbi.1005762; Courtesy: M. Helias
We here show that pairwise maximum entropy models tends yield bimodal distributions if average correlations between units is positive. In the application to neuronal activity this is a problem, because the bimodality is an artefact of the statistical model and not observed in real data. This problem could also affect other fields in biology. We here explain under which conditions bimodality arises and present a solution based on introducing a collective negative feedback, corresponding to a modified maximum-entropy model. This result may point to the existence of a homeostatic mechanism active in the system that is not part of our set of observable units.
Website: Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models
Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit
Image copyright: Fig 1; Bos H, Diesmann M, Helias M (2016) Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. PLOS Computational Biology 12(10): e1005132. https://doi.org/10.1371/journal.pcbi.1005132; Courtesy: M. Helias
We introduce a method that determines the mechanisms and sub-circuits generating oscillations in structured spiking networks. The approach exposes the influence of individual connections on frequency and amplitude of these oscillations and therefore reveals locations, where biological mechanisms controlling oscillations and experimental manipulations have the largest impact. The new analytical tool replaces parameter scans in computationally expensive models, guides circuit design, and can be employed to validate connectivity data.
Website: Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit
Image copyright: CC-BY; Fig 1; Weidel P, Djurfeldt M, Duarte RC and Morrison A (2016) Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS. Front. Neuroinform. 10:31. doi: 10.3389/fninf.2016.00031
Middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC) enabling any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations.
Website: ROS-MUSIC adapters
Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model
Image copyright: CC-BY; Fig 4(a, b, c); Maksimov, A., Albada, S.J. van, and Diesmann, M. 2016. [Re] Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model. ReScience 2, 1, #6
Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models. This repository provides the examination of neural contraints with experimental data.
Website: Wave Propagation
Scalability of asynchronous networks
Image copyright: Fig 3; van Albada SJ, Helias M, Diesmann M (2015) Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. PLOS Computational Biology 11(9): e1004490. https://doi.org/10.1371/journal.pcbi.1004490; Courtesy: S. van Albada
We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
Website: Scalability of asynchronous networks
Image copyright: CC-BY; Fig 6 (L, M, N); Duarte RCF and Morrison A (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Front. Comput. Neurosci. 8:124. doi: 10.3389/fncom.2014.00124
We investigate the actions of dynamic excitatory and inhibitory synapses (STDP) and demonstrate their impact on the robustness and active maintenance of asynchronous irregular activity. Stable and compact stimulus representations are shown to result from the maintenance of this AI-type activity (achieved primarily through the action of iSTDP).
Website: Dynamic stability