Search (2 results, page 1 of 1)

0.049051642 = product of:
  0.07357746 = sum of:
    0.057666 = product of:
      0.172998 = sum of:
        0.172998 = weight(_text_:3a in 76) [ClassicSimilarity], result of:
          0.172998 = score(doc=76,freq=2.0), product of:
            0.36937886 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.043569047 = queryNorm
            0.46834838 = fieldWeight in 76, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=76)
      0.33333334 = coord(1/3)
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 76) [ClassicSimilarity], result of:
          0.047734402 = score(doc=76,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 76, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=76)
      0.33333334 = coord(1/3)
  0.6666667 = coord(2/3)

Abstract

A summary of brain theory is given so far as it is contained within the framework of Localization Theory. Difficulties of this "conventional theory" are traced back to a specific deficiency: there is no way to express relations between active cells (as for instance their representing parts of the same object). A new theory is proposed to cure this deficiency. It introduces a new kind of dynamical control, termed synaptic modulation, according to which synapses switch between a conducting and a non- conducting state. The dynamics of this variable is controlled on a fast time scale by correlations in the temporal fine structure of cellular signals. Furthermore, conventional synaptic plasticity is replaced by a refined version. Synaptic modulation and plasticity form the basis for short-term and long-term memory, respectively. Signal correlations, shaped by the variable network, express structure and relationships within objects. In particular, the figure-ground problem may be solved in this way. Synaptic modulation introduces exibility into cerebral networks which is necessary to solve the invariance problem. Since momentarily useless connections are deactivated, interference between di erent memory traces can be reduced, and memory capacity increased, in comparison with conventional associative memory

Source

http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_

0.020599846 = product of:
  0.061799537 = sum of:
    0.061799537 = product of:
      0.092699304 = sum of:
        0.057281278 = weight(_text_:network in 73) [ClassicSimilarity], result of:
          0.057281278 = score(doc=73,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.29521978 = fieldWeight in 73, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=73)
        0.035418026 = weight(_text_:22 in 73) [ClassicSimilarity], result of:
          0.035418026 = score(doc=73,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.23214069 = fieldWeight in 73, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=73)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Abstract

The central problem with understanding brain and mind is the neural code issue: understanding the matter of our brain as basis for the phenomena of our mind. The richness with which our mind represents our environment, the parsimony of genetic data, the tremendous efficiency with which the brain learns from scant sensory input and the creativity with which our mind constructs mental worlds all speak in favor of mind as an emergent phenomenon. This raises the further issue of how the neural code supports these processes of organization. The central point of this communication is that the neural code has the form of structured net fragments that are formed by network self-organization, activate and de-activate on the functional time scale, and spontaneously combine to form larger nets with the same basic structure.

Date

27.12.2020 16:56:22