# Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity.

@article{Aljadeff2019CorticalCA, title={Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity.}, author={Johnatan Aljadeff and James A. D’Amour and Rachel E. Field and Robert C. Froemke and Claudia Clopath}, journal={arXiv: Neurons and Cognition}, year={2019} }

The cortex learns to make associations between stimuli and spiking activity which supports behaviour. It does this by adjusting synaptic weights. The complexity of these transformations implies that synapses have to change without access to the full error information, a problem typically referred to as "credit-assignment". However, it remains unknown how the cortex solves this problem. We propose that a combination of plasticity rules, 1) Hebbian, 2) acetylcholine-dependent and 3) noradrenaline…

## 14 Citations

### A solution to temporal credit assignment using cell-type-specific modulatory signals

- BiologybioRxiv
- 2020

This work re-analyzes the mathematical basis of gradient descent learning in recurrent spiking neural networks (RSNNs) in light of the recent single-cell transcriptomic evidence for cell-type-specific local neuropeptide signaling in the cortex and suggests a computationally efficient on-chip learning method for bio-inspired artificial intelligence.

### Correlation-invariant synaptic plasticity

- Biology
- 2021

This work develops a theory for synaptic plasticity that is invariant to second-order correlations in the input and demonstrates how correlation-invariance enables biologically realistic models to develop sparse population codes, despite diverse levels of variability and heterogeneity.

### Cell-type-specific modulatory signaling promotes learning in spiking neural networks

- Biology
- 2021

This work re-analyzes the mathematical basis of gradient descent learning in recurrent spiking neural networks (RSNNs) in light of the recent single-cell transcriptomic evidence for cell-type-specific local neuropeptide signaling in the cortex.

### Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields

- Biology, PsychologyThe Journal of Neuroscience
- 2020

This work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields, showing how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change.

### 1 Burst-dependent synaptic plasticity can coordinate learning in 2 hierarchical circuits 3

- Biology
- 2020

It is shown that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections.

### Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

- BiologyNature Neuroscience
- 2021

It is shown that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections and solve challenging tasks that require deep network architectures.

### Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields

- Biology, Psychology
- 2019

It is confirmed that a neuron9s receptive field doesn't follow directly from the weight profiles of its presynaptic afferents, in line with recent experimental findings showing dramatic context-dependent changes of neurons9 receptive fields.

### Reverse Differentiation via Predictive Coding

- Computer ScienceAAAI
- 2022

This work generalizes (PC and) Z-IL by directly defining it on computational graphs, and shows that it can perform exact reverse differentiation, which results in the first PC algorithm that is equivalent to BP in the way of updating parameters on any neural network.

### Can the Brain Do Backpropagation? - Exact Implementation of Backpropagation in Predictive Coding Networks

- Computer ScienceNeurIPS
- 2020

A BL model is proposed that produces exactly the same updates of the neural weights as BP, while employing local plasticity, i.e., all neurons perform only local computations, done simultaneously and is modified to modify it to an alternative BL model that works fully autonomously.

### Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

- Computer ScienceArXiv
- 2022

It is demonstrated that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely, and a theorem is presented explaining this phenomenon.

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