• Corpus ID: 248693162

Dendritic predictive coding: A theory of cortical computation with spiking neurons

@inproceedings{Mikulasch2022DendriticPC,
  title={Dendritic predictive coding: A theory of cortical computation with spiking neurons},
  author={Fabian A. Mikulasch and Lucas Rudelt and Michael Wibral and Viola Priesemann},
  year={2022}
}
These authors contributed equally Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive, and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition, and predictive… 

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References

SHOWING 1-10 OF 113 REFERENCES
Learning prediction error neurons in a canonical interneuron circuit
TLDR
A well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex, making a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.
Predictive Coding of Dynamical Variables in Balanced Spiking Networks
TLDR
The approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.
Dendritic cortical microcircuits approximate the backpropagation algorithm
TLDR
A novel view of learning on dendritic cortical circuits and on how the brain may solve the long-standing synaptic credit assignment problem is introduced, in which error-driven synaptic plasticity adapts the network towards a global desired output.
Spike-Based Population Coding and Working Memory
TLDR
It is proposed that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons, which can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons.
Connectivity reflects coding: a model of voltage-based STDP with homeostasis
TLDR
A model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants is created and found that the plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code.
Predictive coding in balanced neural networks with noise, chaos and delays
TLDR
This work provides and solves a general theoretical framework for dissecting the differential contributions neural noise, synaptic disorder, chaos, synaptic delays, and balance to the fidelity of predictive neural codes, reveals the fundamental role that balance plays in achieving superclassical scaling, and unifies previously disparate models in theoretical neuroscience.
Modelling plasticity in dendrites: from single cells to networks
Emergence of synaptic organization and computation in dendrites
TLDR
Recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations are summarized.
Causal Inference and Explaining Away in a Spiking Network
TLDR
It is demonstrated that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons.
Active dendrites enable strong but sparse inputs to determine orientation selectivity
TLDR
It is predicted that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output, which would allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.
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