A History of Spike-Timing-Dependent Plasticity
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Burst-Dependent Synaptic Plasticity Can Coordinate Learning in Hierarchical Circuits
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.30.015511; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 Burst-dependent synaptic plasticity can coordinate learning in 3 hierarchical circuits 4 1 2,3 4 5,6,7,8 5 Alexandre Payeur • Jordan Guerguiev • Friedemann Zenke Blake A. Richards ⇤• and Richard 1,9, 6 Naud ⇤• 7 1 uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of 8 Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada 9 2 Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, 10 Canada 11 3 Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada 12 4 Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland 13 5 Mila, Montr´eal, QC, Canada 14 6 Department of Neurology and Neurosurgery, McGill University, Montr´eal, QC, Canada 15 7 School of Computer Science, McGill University, Montr´eal, QC, Canada 16 8 Learning in Machines and Brains Program, Canadian Institute for Advanced Research, 17 Toronto, ON, Canada 18 9 Department of Physics, University of Ottawa, Ottawa, ON, Canada 19 Corresponding authors, email: [email protected], [email protected] ⇤ 20 Equal contributions • 21 Abstract 22 Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that 23 it depends on pre and postsynaptic activity. -
Robust Rhythmogenesis Via Spike Timing Dependent Plasticity
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.23.217026; this version posted March 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Robust Rhythmogenesis via Spike Timing Dependent Plasticity Gabi Socolovsky1, 2, ∗ and Maoz Shamir1, 2, 3 1Department of Physics, Faculty of Natural Sciences, 2Zlotowski Center for Neuroscience, 3Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er-Sheva, Israel (Dated: March 31, 2021) Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine tuning is achieved. Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit rhythmic activity, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. -
Towards Biologically Plausible Gradient Descent by Jordan
Towards biologically plausible gradient descent by Jordan Guerguiev A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Cell and Systems Biology University of Toronto c Copyright 2021 by Jordan Guerguiev Abstract Towards biologically plausible gradient descent Jordan Guerguiev Doctor of Philosophy Graduate Department of Cell and Systems Biology University of Toronto 2021 Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is de- pendent on pre- and post-synaptic neuronal activities, and can be mediated by neuromodulatory signals. However, to date, computational models of learning that are based on pre- and post-synaptic activity and/or global neuromodulatory reward signals for plasticity have not been able to learn complex tasks that animals are capable of. In the machine learning field, neural network models with many layers of computations trained using gradient descent have been highly successful in learning difficult tasks with near-human level performance. To date, it remains unclear how gradient descent could be implemented in neural circuits with many layers of synaptic connections. The overarching goal of this thesis is to develop theories for how the unique properties of neurons can be leveraged to enable gradient descent in deep circuits and allow them to learn complex tasks. The work in this thesis is divided into three projects. The first project demonstrates that networks of cortical pyramidal neurons, which have segregated apical dendrites and exhibit bursting behavior driven by dendritic plateau potentials, can in theory leverage these physiological properties to approximate gradient descent through multiple layers of synaptic connections. -
Hebbian Plasticity Requires Compensatory Processes on Multiple Timescales
Hebbian plasticity requires compensatory processes on multiple timescales 1; 2 Friedemann Zenke ∗and Wulfram Gerstner January 16, 2017 1) Dept. of Applied Physics, Stanford University, Stanford, CA 94305, USA orcid.org/0000-0003-1883-644X 2) Brain Mind Institute, School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne EPFL, Switzerland orcid.org/0000-0002-4344-2189 This article is published as: Zenke, F., and Gerstner, W. (2017). Hebbian plasticity requires compensatory processes on multiple timescales. Phil. Trans. R. Soc. B 372, 20160259. DOI: 10.1098/rstb.2016.0259 http://rstb.royalsocietypublishing.org/content/372/1715/20160259 Abstract We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: While homeostasis of synapses found in exper- iments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes. Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in ad- dition to slow forms of homeostatic plasticity there are rapid compensatory processes which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosy- naptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. -
Cooperation Across Timescales Between and Hebbian and Homeostatic Plasticity
This position paper has not been peer reviewed or edited. It will be finalized, reviewed and edited after the Royal Society meeting on ‘Integrating Hebbian and homeostatic plasticity’ (April 2016). Cooperation across timescales between and Hebbian and homeostatic plasticity Friedemann Zenke1 and Wulfram Gerstner2 April 13, 2016 1) Dept. of Applied Physics, Stanford University, Stanford, CA 2) Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne EPFL Abstract We review a body of theoretical and experimental research on the interactions of homeostatic and Hebbian plasticity, starting from a puzzling observation: While homeostasis of synapses found in experiments is slow, homeostasis of synapses in most mathematical models is rapid, or even instantaneous. Even worse, most existing plasticity models cannot maintain stability in simulated networks with the slow homeostatic plasticity reported in experiments. To solve this paradox, we suggest that there are both fast and slow forms of homeostatic plasticity with distinct functional roles. While fast homeostatic control mechanisms interacting with Hebbian plasticity render synaptic plasticity intrinsically stable, slower forms of homeostatic plasticity are important for fine-tuning neural circuits. Taken together we suggest that learning and memory relies on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. Introduction DRAFT Homeostasis refers to a group of physiological processes at different spatial and temporal scales that help to maintain the body, its organs, the brain, or even individual neurons in the brain in a target regime where they function optimally. A well-known example is the homeostatic regulation of body temperature in mammals, maintained at about 37 degrees Celsius independent of weather condition and air temperature. -
Cosyne Program Book
COSYNE MAIN CONFERENCE Denver, CO: 2020 27 feb–1 mar cosyne.org Program Summary Thursday, 27 February 4:00p Registration opens 4:45p Welcome reception 6:15p Opening remarks 6:30p Session 1: The ABCs—Artificial and Biological Circuits Invited speaker: Matthew Botvinick; 2 accepted talks 8:00p Poster Session I Friday, 28 February 8:30a Session 2: Latent space Invited speaker: Mehrdad Jazayeri; 3 accepted talks 10:30a Session 3: Learning to decide Invited speaker: Linda Wilbrecht; 3 accepted talks 12:00n Lunch break 2:00p Session 4: From theory to practice Invited speaker: John Cunningham; 3 accepted talks 4:15p Session 5: Invited speaker: Marta Zlatic; 2 accepted talks 5:30p Dinner break 8:00p Poster Session II Saturday, 29 February 8:30a Session 6: Visual hierarchy Invited speaker: Hendrikje Nienborg; 3 accepted talks 10:30a Session 7: Motor learning Invited speaker: Megan Carey; 3 accepted talks 12:00n Lunch break 2:00p Session 8: Planning and exploration Invited speakers: Sam Gershman, Wei Ji Ma 4:15p Session 9: The social brain Invited speaker: Gul Dolen; 2 accepted talks 5:30p Dinner break 8:00p Poster Session III COSYNE 2020 i Sunday, 01 March 8:30a Session 10: Where am I? Invited speaker: Lisa Giocomo; 3 accepted talks 10:30a Session 11: What and where is that smell? Invited speaker: Rainer Friedrich; 3 accepted talks 12:00n Lunch break 2:00p Session 12: Can you be flexible? Invited speaker: Christopher Harvey; 3 accepted talks ii COSYNE 2020 Deep Learning for Novices and Pros Use MATLAB® to import, design, train, evaluate, and deploy deep networks Labeled Ground Truth Network Prediction Learn more at mathworks.com/deep-learning DataLogger - Wireless Neural Recording System The New DataLogger system is an untethered, battery-powered neural recording system that allows wideband continuous recording at 40 kHz sampling rate. -
About Cosyne
Program Summary All times are EST (Eastern Standard Time, GMT-5) Tuesday, 23 February 11:00a Cosyne Tutorial: Recurrent Neural Networks for Neuroscience 03:00p Paths for leadership in promoting the careers of scientists Wednesday, 24 February 08:00a Poster Session 1a 10:00a Session 1: 3 accepted talks 11:40a Session 2: 4 accepted talks 01:00p Simons Foundation Bridge to Independence Awards: Facilitating the transition from postdoc to junior faculty 02:00p Session 3: 3 accepted talks 03:00p Poster Session 1b 05:00p Mini-symposium on the history of race and racism in science 07:00p Poster Session 1c Thursday, 25 February 08:00a Poster Session 2a 10:00a Session 4: 3 accepted talks 11:40a Session 5: 4 accepted talks 02:00p Session 6: 3 accepted talks 03:00p Poster Session 2b 05:00p Expanding horizons: Diverse perspectives in computational neuroscience 07:00p Poster Session 2c Friday, 26 February 08:00a Poster Session 3a 10:00a Session 7: 3 accepted talks 11:40a Session 8: 4 accepted talks 02:00p Session 9: 3 accepted talks 03:00p Poster Session 3b 07:00p Poster Session 3c COSYNE 2021 i ii COSYNE 2021 About Cosyne About Cosyne The annual Cosyne meeting provides an inclusive forum for the exchange of experimental and theoretical/computational approaches to problems in systems neuroscience. To encourage interdisciplinary interactions, the main meeting is arranged in a single track. A set of invited talks are selected by the Executive Committee and Organizing Commit- tee, and additional talks and posters are selected by the Program Committee, based on submitted abstracts.