The Myth of Temporal Coding

NeuroElectroDynamics or NED is the study of  the dynamics and interaction of electrical charges in the brain [1]. The word neuroelectrodynamics is derived from neuro- meaning neurons, electro- electric field and -dynamics meaning movement.

The main idea of NED is that under the influence of electric fields, charges that interact perform computations and are capable to read, write and store information in their spatial distribution at molecular level within active neurons. The universal physical laws from classical mechanics, thermodynamics to quantum theory can be applied to generate a consistent mathematical model of brain computation.

The fundamental claim of NED is that temporal observables associated with neural coding ( temporal coding, spike timing occurrence, spike-timing-dependent plasticity, ) are epiphenomena determined by the dynamics and interaction of electric charges modulated by molecular changes in neurotransmitters levels, regulatory mechanisms of gene expression from DNA to proteins synthesis.

NED  highlights a specific form of computation by interaction which is a general physical model of computation extensively present in nature [6].

A spontaneous generation of action potentials and synaptic activities is needed to maintain physical interaction. Meaningful information encoded (written) within neurons and synapses at a molecular level  can be   transmitted synaptically and non-synaptically  during action potential propagation [1][5].

During these events, the required information is exchanged between molecular structures (proteins), which store fragments of information, and the generated electric flux, which carries and integrates meaningful information in the brain [5].

History

Early work started with a fundamental electrophysiological observation. Contrary to common belief, action potentials generated by the same neuron are not alike, they display changes in electrical patterns not just temporal variability.

Every recorded action potential can be characterized by a new measure, spike directivity that describes electrical activity in a biological neuron [2]. Significant changes in spike directivity are correlated with changes in behavior [3]. Since information is carried by electric charges [4], then their dynamics and interaction characterize complex computational processes in the brain.

References

[1] Aur D., Jog, MS., 2010 Neuroelectrodynamics: Understanding the brain language, IOS Press, 2010.

[2] Aur D., Connolly C.I., and Jog M.S., 2005 Computing spike directivity with tetrodes. J. Neurosci. Vol. 149, Issue 1, pp. 57–63.

[3] Aur D., Jog, M.S., 2007 Reading the Neural Code: What do Spikes Mean for Behavior? Nature Precedings, http://dx.doi.org/10.1038/npre.2007.61.1

[4] Aur D., Connolly C.I. and Jog M.S., 2006 Computing Information in Neuronal Spikes, Neural Processing Letters, 23:183-199.

[5] Aur, D. 2012,  A comparative analysis of integrating visual information in local neuronal ensembles. Journal of neuroscience methods, 207(1), 23- 30.http://www.ncbi.nlm.nih.gov/pubmed/22480985

[6] Aur D, Jog MS, Poznanski, R, Computing by physical interaction in neurons, Journal of integrative Neuroscience, vol. 10, Issue: 4, 2011, pp. 413-422http://www.ncbi.nlm.nih.gov/pubmed/22262533



Starting with Adrian's recordings the idea was that the neural code was embedded in  temporal patterns. Therefore,  in the last sixty years everything was felt to revolve around temporal patterns.
However, the main hypothesis of computational  neuroscience ( digital-like uniformity of action potentials) is not validated by recent experimental data [1],[2].
 From a false hypothesis (stereotypical spike) one can reach  false conclusions following  a correct analysis of data. The unrealistic hypothesis  impacted model validity, created a strong debate and generated unrealistic predictions  in behavioral studies, visual object recognition (e.g. grandmother cell).


The power of  counterexamples

We completely understand the frustration of neuroscientists regarding temporal coding. For more than 60 years they have tried to prove that temporal coding is the right model of  information processing that reflects the subtle nature of neural code. The paradox is that the same experiment used to show the organization of temporal patterns proves the fallacy of temporal coding. The procedural T-maze learning task became the best counterexample for temporal coding theory http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html. Currently, we can provide other  examples, however there is no need to generate new evidence. That’s the beauty of counterexamples and experimental design. You only need one single counterexample to throw down a "solid"theoretical construct. Quoting Feynman "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are or how many years you can keep a theory alive ! If it doesn't agree with experiment, it's wrong."

With a  digital (stereotype) spike, the temporal coding theory was born dead.

All issues in interpretation are generated by this  FALSE hypothesis of  a  digital (stereotype) spike, see the analogy:
a. 3=-3                   -------                    (3)2=(-3)2              -----                        9=9
b. (FALSE hypothesis) ------     (operation/transformation) ---- (CORRECT/ FALSE ?)
A.Stereotype spike    --------  adding spikes/firing rate   -------CONTROVERSIES
B. (FALSE hypothesis) ------- (transformation, STATISTICS )  -- CORRECT /FALSE ?

Therefore,  Bayes theory, nonlinear dynamics  ... have no real value if they are attached to a false hypothesis (digital spike). One can learn mathematics, solve complex problems without pretending to apply the mathematical theory to understand  ‘neural code’.  In fact  many controversies in the field were generated by keeping alive the reductionist  temporal coding paradigm, they lead to  inconsistent and naive interpretations regarding place cells, concept cells or mirror neurons.

References

1. Dorian Aur , Christopher I.  Connolly, Mandar S. Jog, 2005, Computing spike directivity with tetrodes, Journal of Neuroscience Methods, 149 (1), pp. 57-63.
2. Takuya Sasaki, Norio Matsuki, Yuji Ikegaya, 2011, Action-potential modulation during axonal conduction Science 331 (6017), pp. 599-601