The Role of Synaptic Tagging and Capture for Memory Dynamics in Spiking Neural Networks

Memory serves to process and store information about experiences such that this information can be used in future situations. The transfer from transient storage into long-term memory, which retains information for hours, days, and even years, is called consolidation. In brains, information is primarily stored via alteration of synapses, so-called synaptic plasticity. While these changes are at first in a transient early phase, they can be transferred to a late phase, meaning that they become stabilized over the course of several hours. This stabilization has been explained by so-called synaptic tagging and capture (STC) mechanisms. To store and recall memory representations, emergent dynamics arise from the synaptic structure of recurrent networks of neurons. This happens through so-called cell assemblies, which feature particularly strong synapses. It has been proposed that the stabilization of such cell assemblies by STC corresponds to so-called synaptic consolidation, which is observed in humans and other animals in the first hours after acquiring a new memory. The exact connection between the physiological mechanisms of STC and memory consolidation remains, however, unclear. It is equally unknown which influence STC mechanisms exert on further cognitive functions that guide behavior. On timescales of minutes to hours (that means, the timescales of STC) such functions include memory improvement, modification of memories, interference and enhancement of similar memories, and transient priming of certain memories. Thus, diverse memory dynamics may be linked to STC, which can be investigated by employing theoretical methods based on experimental data from the neuronal and the behavioral level. In this thesis, we present a theoretical model of STC-based memory consolidation in recurrent networks of spiking neurons, which are particularly suited to reproduce biologically realistic dynamics. Furthermore, we combine the STC mechanisms with calcium dynamics, which have been found to guide the major processes of early-phase synaptic plasticity in vivo. In three included research articles as well as additional sections, we develop this model and investigate how it can account for a variety of behavioral effects. We find that the model enables the robust implementation of the cognitive memory functions mentioned above. The main steps to this are: 1. demonstrating the formation, consolidation, and improvement of memories represented by cell assemblies, 2. showing that neuromodulator-dependent STC can retroactively control whether information is stored in a temporal or rate-based neural code, and 3. examining interaction of multiple cell assemblies with transient and attractor dynamics in different organizational paradigms. In summary, we demonstrate several ways by which STC controls the late-phase synaptic structure of cell assemblies. Linking these structures to functional dynamics, we show that our STC-based model implements functionality that can be related to long-term memory. Thereby, we provide a basis for the mechanistic explanation of various neuropsychological effects.2021-09-0

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Stabilization through self-coupling in networks of small-world and scale-free topology

Abstract Mechanisms that ensure the stability of dynamical systems are of vital importance, in particular in our globalized and increasingly interconnected world. The so-called connectivity-stability dilemma denotes the theoretical finding that increased connectivity between the components of a large dynamical system drastically reduces its stability. This result has promoted controversies within ecology and other fields of biology, especially, because organisms as well as ecosystems constitute systems that are both highly connected and stable. Hence, it has been a major challenge to find ways to stabilize complex systems while preserving high connectivity at the same time. Investigating the stability of networks that exhibit small-world or scale-free topology is of particular interest, since these topologies have been found in many different types of real-world networks. Here, we use an approach to stabilize recurrent networks of small-world and scale-free topology by increasing the average self-coupling strength of the units of a network. For both topologies, we find that there is a sharp transition from instability to asymptotic stability. Then, most importantly, we find that the average self-coupling strength needed to stabilize a system increases much slower than its size. It appears that the qualitative shape of this relationship is the same for small-world and scale-free networks, while scale-free networks can require higher magnitudes of self-coupling. We further explore the stabilization of networks with Kronecker-Leskovec topology. Finally, we argue that our findings, in particular the stabilization of large recurrent networks through small increases in the unit self-regulation, are of practical importance for the stabilization of diverse types of complex systems

Arbor Library v0.7

What's Changed Neuroscience/documentation prep extracellular potentials tutorial. Updated corresponding example by @espenhgn in #1825 Add documentation on faster NMODL. by @thorstenhater in #1840 Add tstop doc in recipe API doc by @schmitts in #1852 Use list comprehension to speed up creation of connections by @schmitts in #1864 Support markers in neurolucida ascii files by @bcumming in #1867 simplified create_polygon function in lfpykit example by @espenhgn in #1881 Better key for the lfpykit intersphinx mapping by @Helveg in #1878 Sort (in python) the mechanism names (for convenience). by @thorstenhater in #1882 Always emit weight expression. by @thorstenhater in #1875 Add support for epoch callbacks by @bcumming in #1873 Add introspection for global properties. by @thorstenhater in #1890 Add point probes to demo by @schmitts in #1891 Tut/use avail threads by @brenthuisman in #1900 Allen tutorial by @brenthuisman in #1781 New policy 'round_robin_halt' by @jlubo in #1868 Axial Diffusion by @thorstenhater in #1729 Add some convenience to simulation creation. by @thorstenhater in #1904 Predefine SWC Regions. by @thorstenhater in #1911 Diffusion Example Improvements (and a bit of clean-up) by @thorstenhater in #1914 Inhomogeneous parameters by @AdhocMan in #1887 Fix line numbers in tutorials and assorted doc corrections by @brenthuisman in #1917 Core Be more lenient when accepting args to file I/O by @thorstenhater in #1819 modcc: generate missing node_index read needed for reading time t in the mechanisms by @noraabiakar in #1866 Add Developer Documentation by @thorstenhater in #1639 Isolate external catalogues from libarbor.a. by @thorstenhater in #1837 Build, testing, CI Build Python 3.10 binary wheels. Add v0.6 to spackfile. by @brenthuisman in #1817 export API by @boeschf in #1824 export doc by @boeschf in #1849 Include CMAKE+CUDA iff NVCC is needed. by @thorstenhater in #1855 Bit more on Spack, fix in tutorial by @brenthuisman in #1838 json submodule added by @brenthuisman in #1871 Fix a bug where Debian/Ubuntu's Python malfunctions by @brenthuisman in #1894 Have dependency version policy by @brenthuisman in #1865 random123 submodule added by @brenthuisman in #1872 Fix tool installation paths. by @thorstenhater in #1905 Adopt Black for Python. by @thorstenhater in #1906 add cmake checks for non-bundled random123 by @bcumming in #1907 Temporarily disable A64FX CI by @bcumming in #1910 Adopt flake8 by @thorstenhater in #1908 Move Python build to pyproject.toml , bump Python minver to 3.7, fix macos wheel generation by @brenthuisman in #1916 Weekly CI Python wheel build pushes to Test.PyPI.org by @brenthuisman in #1921 Fixes, optimization Users may not give dt < 0. by @thorstenhater in #1821 Fix ubenches compilation errors by @noraabiakar in #1828 death test: wrong signal by @boeschf in #1858 Make brunel.py setup faster. by @thorstenhater in #1854 add a better error. by @thorstenhater in #1846 Fix load_component for label-dict by @noraabiakar in #1859 Found out the hard way that this is still needed :/ by @thorstenhater in #1860 Elide memcpy where not needed by @thorstenhater in #1863 Bug fix: Fix voltage vector size in threshold_watcher contstructor by @noraabiakar in #1820 remove test statements for move ctor by @boeschf in #1899 Code Quality: PVS Studio Finds by @thorstenhater in #1901 Two context decomp swaps were forgotten by @brenthuisman in #1912 New Contributors @jlubo made their first contribution in #1868 @AdhocMan made their first contribution in #188

Arbor Library v0.8

# v0.8   ** 2022 11 15 **   Welcome to another installment of the Arbor simulator!   In this release we add a CHANGELOG, where major new features and breaking changes will be mentioned specifically.

Arbor Library v0.8.1

# v0.8.1   ** 2022 12 20 **   A holiday release! Not much has changed in a month, but we'd like to share it all the same. Notably, the [Arbor GUI](https://github.com/arbor-sim/gui/) [is co-released](https://github.com/arbor-sim/gui/releases/tag/v0.8) as of Arbor v0.8, and v0.8.1 will be no different.   ## Major new features   - Voltage Processes: add the VOLTAGE_PROCESS mechanism kind to modcc, allowing for direct writing to the membrane voltage (#2033) - Spack gpu option: added conditional variant for cuda builds to enable GPU-based random number generation (#2043) - SDE Tutorial (#2044)   ## Breaking changes since v0.7   - None !   ## Bug fixed   - Fix ornstein_uhlenbeck example on gpu (#2039) - Setting ARB_MODCC was broken and nunfunctional. Fixed. (#2029) - The `--cxx` flag in `arbor-build-catalogue` is now properly used; falls back to `c++`. (#2051)   ## Full commit log   * Post release: add Zenodo entry, add Spack entry, update docs and scripts by @brenthuisman in https://github.com/arbor-sim/arbor/pull/2036 * BUGFIX: add ARB_CUDA flag to example catalogue by @boeschf in https://github.com/arbor-sim/arbor/pull/2039 * Additional builtin functions to Arbor's NMODL dialect by @boeschf in https://github.com/arbor-sim/arbor/pull/2035 * Throw better errors when we cannot look up ion diffusivity by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2040 * ⚡ Voltage Processes by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2033 * simplify make catalogue by @boeschf in https://github.com/arbor-sim/arbor/pull/2042 * spack gpu option by @boeschf in https://github.com/arbor-sim/arbor/pull/2043 * Remove deprecated spike_detector. by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2041 * make ARB_MODCC functional again by @brenthuisman in https://github.com/arbor-sim/arbor/pull/2029 * Yank v0.5 from Spack file, it does not build due to change in setting arch by @brenthuisman in https://github.com/arbor-sim/arbor/pull/2037 * Use pugixml instead of libxml2 by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2048 * [BUGFIX] a-b-c: actually set compiler, improved default by @brenthuisman in https://github.com/arbor-sim/arbor/pull/2051 * Sde Tutorial by @boeschf in https://github.com/arbor-sim/arbor/pull/2044 * Set Python to known version for all CI workflows by @brenthuisman in https://github.com/arbor-sim/arbor/pull/2058 * consistent mechanism ids by @boeschf in https://github.com/arbor-sim/arbor/pull/2057 * Fix docs about exprelr. by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2064 * Allow __ in profiler names. by @thorstenhater in https://github.com/arbor-sim/arbor/pull/2065 * CI fixes for wheel building, prep for musllinux, Spack by @brenthuisman in https://github.com/arbor-sim/arbor/pull/206