Ben Kanter @beneuroscience@qoto.org

@albertcardona @SussilloDavid @dickretired I partially agree. Without the connectome you will need to make some assumptions, but I think the advancements in understanding that could be possible with fine-grained perturbations sans connectome would be still quite large.

@dickretired Perturb brains circuits in the way that test state-space concepts of neural activity. E.g. stimulate on particular vectors to, e.g., move along a putative line attractor.

@SussilloDavid @dickretired I second this take from David. I would add, we need this for testing learning algorithms as well, bc we really need to be able to set specific activitystates to see how this induces changes downstream.

I see that people are very politely introducing themselves again now that things have really got going here.

For those I haven't met yet, I'm a cognitive neuroscientist interested in the brain mechanisms of human memory.

My university leadership role involves efforts to enhance interdisciplinary research and to improve the culture we work in, such as increasing diversity in leadership, empowering ECRs and professional services staff, and incentivising open research practices.

#introduction

A quick #introduction by way of a recent paper: hippocampal-centric models of episodic memory are incomplete.

Here's why/our fix:

The anterior thalamic nuclei: core components of a tripartite episodic memory system

Aggleton & O’Mara
Nature Rev Neurosci (2022)

https://www.nature.com/articles/s41583-022-00591-8

#memory #brain #anteriorthalamus #korsakoff #tripartite #episodic #memorysystem

#introduction

I am a researcher at #Mila, the Québec AI institute, and a prof at #McGill University.

My research sits at the intersection of #AI and #neuroscience, with a focus on #learning and #memory.

Most centrally, I'm interested in credit assignment in both space and time, and universal principles of learning related to those questions.

#introduction

I am a PhD student @SaxeLab at the University of Oxford studying how brains can preserve previously acquired knowledge during learning.

My research aims at developing mathematical tools and using simulation studies to understand algorithms that describe and analyse learning in the brain. In particular, I am interested in the learning dynamics of gradient-based algorithms and how they apply to learning in biological organisms.

#introduction I am a professor at Penn and also co-director of the CIFAR Learning in Machines and Brains program. I like to think about neuroscience, AI, and science in general. Neuromatch. Recently, much of my thinking is about Rigor in science and I just started leading a large NIH funded initiative community for rigor (C4R) that aims at teaching scientific rigor.

My interests are broad: Causality, ANNs, Logic of Neuroscience, Neurotech, Data analysis, AI, community, science of science