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Gerstner Lab Profile
Gerstner Lab

@compneuro_epfl

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The Laboratory of Computational Neuroscience @EPFL_en studies models of #neurons, #networks of #neurons, #synapticplasticity, and #learning in the brain.

Lausanne, Switzerland
Joined March 2018
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@compneuro_epfl
Gerstner Lab
2 years
Our latest results (with @nickyclayton22) is now out in @NatureComms:.🥳. We propose a model of *28* behavioral experiments with food caching jays using a *single* neural network equipped with episodic-like memory and 3-factor RL plasticity rules. 1/6.
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@compneuro_epfl
Gerstner Lab
5 months
The LCN is gradually moving away from X. You can follow our most recent news on our new account on BlueSky at
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@compneuro_epfl
Gerstner Lab
7 months
RT @sobeckerneuro: If you're at #foragingconference2024 , come check out our poster (#60) with @modirshanechi and @compneuro_epfl today!.Us….
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@compneuro_epfl
Gerstner Lab
8 months
RT @modirshanechi: I'm thrilled to share that I was recently awarded the @EPFL_en Dimitris N. Chorafas Foundation Award for my Ph.D. thesis….
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@compneuro_epfl
Gerstner Lab
8 months
RT @GauteEinevoll: Episode #22 in #TheoreticalNeurosciencePodcast: On 50 years with the Hopfield network model - with Wulfram Gerstner @….
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@compneuro_epfl
Gerstner Lab
8 months
RT @bsimsek13: 📢 I'm on the faculty job market this year! . My research explores the foundations of deep learning and analyzes learning and….
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@compneuro_epfl
Gerstner Lab
10 months
RT @roxana_zeraati: Headed to @BernsteinNeuro Conference this weekend and interested in how biological computation is performed across diff….
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@compneuro_epfl
Gerstner Lab
11 months
RT @schmutz_val: 1. Synaptic weight scaling in O(1/N) self-induces a form of (implicit) spatial structure in networks of spiking neurons, a….
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arxiv.org
The dynamics of spatially-structured networks of $N$ interacting stochastic neurons can be described by deterministic population equations in the mean-field limit. While this is known, a general...
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@compneuro_epfl
Gerstner Lab
1 year
RT @GraeffJohannes: And it's a book! Together with @okaysteve, we have gathered some of the leading experts in the field who have generousl….
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@compneuro_epfl
Gerstner Lab
1 year
RT @NatureComms: Approximation-free training method for deep SNNs using time-to-first-spike coding.
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@compneuro_epfl
Gerstner Lab
1 year
RT @BellecGuill: Today in @NatureComms . 📝. Open-puzzle: training event-based spiking neurons is mysteriously impossible. @Ana__Stan 👩🏻‍🔬….
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nature.com
Nature Communications - To address challenges of training spiking neural networks (SNNs) at scale, the authors propose a scalable, approximation-free training method for deep SNNs using...
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@compneuro_epfl
Gerstner Lab
1 year
RT @FlaviohMar: 📕Recovering network weights from a set of input-output neural activations 👀.Ever wondered if this is even possible? 🤔. Chec….
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@compneuro_epfl
Gerstner Lab
1 year
Specifically, we propose a Spiking Neural Network model where E-I imbalance is used to extract a surprise signal for modulation of synaptic plasticity (via three-factor learning rules). This design connects high-level cognitive models of surprise to circuit-level mechanisms.
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@compneuro_epfl
Gerstner Lab
1 year
Normative theories show that a surprise signal is necessary to speed up learning after an abrupt change in the environment; but how can such a speed-up be implemented in the brain? đź§ . We make a proposition in our new paper in @PLOSCompBiol.
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journals.plos.org
Author summary Everybody knows the subjective feeling of surprise and behavioral reactions to surprising events such as startle response and pupil dilation are widely studied—but how can surprise...
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@compneuro_epfl
Gerstner Lab
1 year
RT @modirshanechi: What do we talk about when we talk about "curiosity"? 🤔.In our new paper in @TrendsNeuro (with @KacperKond, @compneuro_e….
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@compneuro_epfl
Gerstner Lab
1 year
RT @akaijsa: Excited that our new position piece is out! In this article, @summerfieldlab and I review three recent advances in using deep….
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