A dendritic mechanism for balancing synaptic flexibility and stability.

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation.

Coordinated head direction representations in mouse anterodorsal thalamic nucleus and retrosplenial cortex.

The sense of direction is critical for survival in changing environments and relies on flexibly integrating self-motion signals with external sensory cues. While the anatomical substrates involved in head direction (HD) coding are well known, the mechanisms by which visual information updates HD representations remain poorly understood. Retrosplenial cortex (RSC) plays a key role in forming coherent representations of space in mammals and it encodes a variety of navigational variables, including HD.

Vectorized instructive signals in cortical dendrites during a brain-computer interface task.

Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments.

Differential dendritic integration of long-range inputs in association cortex via subcellular changes in synaptic AMPA-to-NMDA receptor ratio.

Synaptic NMDA receptors can produce powerful dendritic supralinearities that expand the computational repertoire of single neurons and their respective circuits. This form of supralinearity may represent a general principle for synaptic integration in thin dendrites. However, individual cortical neurons receive many diverse classes of input that may require distinct postsynaptic decoding schemes.

Allometric rules for mammalian cortical layer 5 neuron biophysics.

The biophysical properties of neurons are the foundation for computation in the brain. Neuronal size is a key determinant of single neuron input-output features and varies substantially across species. However, it is unknown whether different species adapt neuronal properties to conserve how single neurons process information.

Widespread and Highly Correlated Somato-dendritic Activity in Cortical Layer 5 Neurons.

Dendritic integration can expand the information-processing capabilities of neurons. However, the recruitment of active dendritic processing in vivo and its relationship to somatic activity remain poorly understood. Here, we use two-photon GCaMP6f imaging to simultaneously monitor dendritic and somatic compartments in the awake primary visual cortex.