#####################
Dendritic Computation
#####################

Motivation(s)
=============

Many artificial neuronal networks assume an individual neuron sums up the
synaptic input and decides whether to initiate an action potential via comparing
the sum against a (nonlinear) threshold.  This model ignores the recent findings
on how the linear and nonlinear mechanisms in the dendritic tree could serve
as computational building blocks.

Proposed Solution(s)
====================

The authors review recent works showing neuronal dendrites may be essential for
the computations performed by the neuron and the network.

Evaluation(s)
=============

The authors list promising results such as

- directional selectivity in visual neurons of flies,
- coincidence detection in auditory neurons of chicks,
- temporal integration over long timescales in oculomotor system of goldfishes,
- image processing (e.g. convolution) in dendrites of flies,
- lobula giant movement detector of locusts, and
- forward masking in omega neurons of crickets.

Note that these are still just experiments and only serves as a guide for future
research.

Future Direction(s)
===================

- Would duplicating the weights in a neural network but force the duplicate to
  decay in a manner similar to reinforcement learning be beneficial?
- If the topology of a neural network undergoes remodelling
  (e.g. pruning, expansion, clustering, Hebbian theory) during back propagation,
  would the error improve?

Question(s)
===========

- What are some experiments that involve stimulating a living person's brain?

Analysis
========

Deep learning with nonlinear activation functions and hidden layers is using the
most basic approximation of neurons: each neuron receives hundreds to thousands
of input spike trains, yet it has only one axon for signaling a single output.
Although the experimental findings are promising, the subjects are still
non-human or dead brain cell samples.  Nevertheless, the results provide
valuable insights into the brain such as the dendritic mechanism that
backpropagates action potentials.

Notes
=====

Computations in Passive Dendrites
---------------------------------

- Nonlinear interaction between excitation and shunting inhibition on small
  dendritic branches can implement logical operations.
- A high-frequency fluctuating current injected in the dendritic pipette will
  evoke high-frequency and large-amplitude local voltage responses, but the
  response recorded by the somatic pipette will be attenuated and smoothed
  (low pass filtered).
- Excitatory inputs to the same branch tend to sum sublinearly, whereas inputs
  on different branches sum linearly.

  - In neurons with active dendrites, clusters of inputs that are synchronously
    active on the same branch can evoke a local dendritic spike.

- Coincidence detection in pyramidal neurons is tuned by their dendritic
  branching pattern.

.. rubric:: References

.. bibliography:: refs.bib
   :all: