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WIREs Data Mining Knowl Discov
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Shannon versus semantic information processing in the brain

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Claude Shannon insisted that attributing an interpretation or meaning to information will destroy its generality and limit its scope. According to him it is only the statistical nature of information that matters. Semantic information is on the other hand, meaning of the information. Pattern recognition plays a big role in neural signal processing. Patterns can be thought of encoded semantic information in neural signals. In neuroscience both types of information have been studied extensively, without ever mentioning the term “semantic information,” whereas “Shannon information” has become a household name among the neuroscientists, often even without the term “Shannon.” In fact, neural information in general is a combination of both. In this review we highlight Shannon information theoretical aspects and semantic information theoretical aspects in neural information processing. In fact, neural information in general, is a combination of both. It has been elaborated how an organized study of semantic processing of neural information, particularly from a time series data mining point of view, can aid our understanding of information processing in the brain. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Algorithmic Development > Biological Data Mining Algorithmic Development > Structure Discovery
(a) In vivo neural spike signals from hippocampus of a transgenic mouse (stratum pyramidale, CA1) measured using six microelectrodes during light stimulation with LFP shown on top. A train of light pulses with a frequency of 1 Hz and a duty cycle of 50% was applied. (b) Raster plot of sorted neural signals on E2 and E3. (c) Close‐up transient plots of neural signals detected from E2 during “off” and “on” cycles. (d) Sorted neural spike signals from E2, E3, E4, and E5 and their corresponding raster plots and peristimulus time histograms (PSTH) of 100 events (i.e., pulses). The inset shows the autocorrelograms of the sorted signals (bar: 20 ms). Taken from Son et al. ()
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Directional flow on simulated focal intracranial EEG from two channels before, during and after an epileptic seizure. Vertical lines indicate seizure onset and offset. (Top) Granger causality. (Bottom) PCMI. 1 window = 1,024 points, where sample frequency is 1,024 Hz
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Mutual information across nine electrodes in the seizure onset zone (epileptogenic focus—all electrodes are depth EEG electrodes in the hippocampus and amygdala) before, during and after an epileptic seizure. Vertical lines indicate seizure onset and offset. Abscissa indicates time in second and ordinate indicates mutual information
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13 3‐point motifs, using which as an alphabet any discrete signal can be expressed as a string in that alphabet. For a mathematical proof see Majumdar and Jayachandran (Majumdar & Jayachandran, )
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Comparison of mutual information (MI) with other bivariate measures of synchronization commonly used for processing of neural signals. AC, amplitude correlation; PLV, phase locking value(Aydore, Pantazis, & Lehay, ); PS, phase synchronization(Rosenblum, Pikovsky, & Kurths, ); h‐square(Wendling, Bartolomei, Bellanger, & Cauvel, ); coherence(Bastos & Schoffelen, ). All the measures were run on a pair of human epileptic focal sEEG channels before, during and after seizure. Vertical lines indicate seizure onset and offset time points
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Different types of primary visual cortical neuronal spike trains intracellularly recorded in vivo in anasthetized cat after current injection. (a) Spike trains of regular spiking (RS) pyramidal cells in layer VI. (b) Spike trains of intrinsic bursting (IB) pyramidal cells in layer V. (c) Spike trains of fast spiking (FS) cells. (d) Spike trains of chattering (CH) cells in layer III. From Gray and McCormick(Gray & McCormick, ), reprinted with permission from AAAS
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(Top) Spontaneous spike train of a pacemaker dopamine neurons in the substantia nigra pars compacta (SNc) of a mouse brain slice (M. Puopolo, unpublished observations). (Bottom left) A magnified action potential of the above spike train. (Bottom right) dV/dt versus V plot of the action potential. IS, initial segment (of axon); SD, somatic dendrite. Taken from Bean(Bean, )
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Algorithmic Development > Biological Data Mining
Algorithmic Development > Structure Discovery
Fundamental Concepts of Data and Knowledge > Knowledge Representation

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