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Society today is like a membrane potential at -200mV

Out work about "Single Cortical Neurons as Deep Artificial Neural Networks" was finally published in Neuron With Mickey London and Idan Segev Neuron paper: bit.ly/3CJic8E code: bit.ly/3CJUeKv data: bit.ly/3xIm6uW All new results in thread below

I intend to complete my PhD within a year, sending out feelers for postdocs or permanent positions. I'm a computational neuroscientist interested in bridging the gap between biological and artificial neurons. In no particular order my interests include: 🧵

In our paper Burst Control, we explore the synaptic input conditions leading to spike burst firing in layer 5 pyramidal cells doi.org/10.1371/journa… Idan Segev ELSC Brain

These are menorahs, trust me

This is gold

A new preprint about how adding dendrites can potentially explain the phenomenon of multiple synaptic contacts between neurons in the brain: bioRxiv: biorxiv.org/content/10.110… With @sapir_shap Mickey London Idan Segev

A Generalized Framework for Calcium-Based Plasticity Describes Weight-Dependent Synaptic Changes in Behavioral Time Scale Plasticity biorxiv.org/cgi/content/sh… #biorxiv_neursci

In our new preprint (see below), we generalized previous models of calcium-based plasticity with a simplified framework, known as the fixed point — learning rate (FPLR) framework. We then used this model to explain behavioral time scale plasticity (BTSP).

This is appalling.

New preprint (link ↓): Connectionists think that plasticity in the brain might enable it to learn like an artificial neural network. Machine learning people have some ideas about learning, and neuroscientists have some ideas about plasticity. (1/N)

Interesting. It feels intuitively right, but I don't think I'd reach the conclusion on my own.