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    Review of 'Hypotheses of neural code and the information model of the neuron-detector'

    Hypotheses of neural code and the information model of the neuron-detectorCrossref
    Interesting objective to formalize notion of neuronal 'addressing' but poor arguments and execution
    Average rating:
        Rated 2.5 of 5.
    Level of importance:
        Rated 3 of 5.
    Level of validity:
        Rated 2 of 5.
    Level of completeness:
        Rated 2 of 5.
    Level of comprehensibility:
        Rated 2 of 5.
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    Hypotheses of neural code and the information model of the neuron-detector

    This paper deals with the problem of neural code solving. On the basis of the formulated hypotheses the information model of a neuron-detector is suggested, the detector being one of the basic elements of an artificial neural network (ANN). The paper subjects the connectionist paradigm of ANN building to criticism and suggests a new presentation paradigm for ANN building and neuroelements (NE) learning. The adequacy of the suggested model is proved by the fact that is does not contradict the modern propositions of neuropsychology and neurophysiology.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      artificial intelligence,neural code,artificial neural network,neuron-detector

      Review text

      This work explores the notion of neural addressing based on unique detection and 'learning' of specific neurotransmission signalling. This paper contributes to the important effort of exploring various potential neural coding mechanisms in the brain. The author clearly exhibits innovative thinking and proposes formal mechanisms for detection, competition, and learning based on the hypothetical mechanism. A number of weaknesses are clearly apparent however, including:

      - Description of perception is rudimentary and an insufficient account of perception theory is provided

      - When claiming to state definitions, the author often produces paragraphs with multiple statements rather than discrete definitions

      - Definition of ‘presentation’ lacks rigour: what do ‘all stages of information processing’ correspond to?

      - No clear definition of what the author means by a ‘perceptive image’

      - Hypothesis 2 claims that neurons transmit their ‘address’ how so? - does the author mean that the spatial characteristic of neurons is what is important for information processing? Unclear.

      - Hypothesis 2 claims that levels of excitation of neurons are primarily expressed by their spiking frequency, this is not the case as many different types of neurons may spike with various characteristics (see Izhikevich 2004 for some alternatives)

      - Hypothesis 3: “The ‘address’ of a neuron in the module of information processing is coded by a unique for this module set and relation of neurotransmitters generated in the neuron.” Awkward.

      - the idea of neurotransmitter mixtures being specific to neurons and constituting ‘addresses’ is interesting - but it is unclear how this benefits coding

      - The author claims that the bounds of frequency spiking for all neurons is between 100 Hz and 1000 Hz, this is incorrect - most neuronal spiking take place below the 100 Hz mark

      - Likelihood based approaches to neural coding are not the only alternative to frequency codes. Many other coding dynamics have been observed and hypothesized: temporal code, phase code, rank code, etc.

      - Hypothesis 4.1 is not a hypothesis but an observed fact

      - Hypothesis 4.3 introduces ‘neuron learning’ for the first time and isn’t clear what the purpose or mechanism for this is - perhaps the author should focus on the notion of presynaptic ’addresses’

      - The author’s second criticisms of synaptic change via Hebb’s rule is unfounded: there cannot be limitless increase of synaptic weighting - saturation occurs

      - formalism on the ‘competition’ and ‘teaching’ of neuron detectors is of interest but not sufficiently introduced

      - What is the biological basis for normalization or other model features?

      - The notion of information synthesis and response analysis is important and could be explored in much greater detail rather than limiting to the concluding remarks


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