A simulation of somatosensory cortical map plasticity

Sklar, E.

doi:10.1109/ijcnn.1990.137924

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…During the past few years there have been several efforts to develop computational models of such cortical map self-organization and map refinement (Obermeyer et al 1990;Pearson et al 1987;Grajski and Merzenich 1990;Sklar 1990;von der Malsburg 1973;Ritter et a/. 1989).…”

Section: Introductionmentioning

confidence: 99%

Cortical Map Reorganization as a Competitive Process

et al. 1994

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Past models of somatosensory cortex have successfully demonstrated map formation and subsequent map reorganization following localized repetitive stimuli or deafferentation. They provide an impressive demonstration that fairly simple assumptions about cortical connectivity and synaptic plasticity can account for several observations concerning cortical maps. However, past models have not successfully demonstrated spontaneous map reorganization following cortical lesions. Recently, an assumption universally used in these and other cortex models, that peristimulus inhibition is due solely to horizontal intracortical inhibitory connections, has been questioned and an additional mechanism, the competitive distribution of activity, has been proposed. We implemented a computational model of somatosensory cortex based on competitive distribution of activity. This model exhibits spontaneous map reorganization in response to a cortical lesion, going through a two-phase reorganization process. These results make a testable prediction that can be used to experimentally support or refute part of the competitive distribution hypothesis, and may lead to practically useful computational models of recovery following stroke.

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Section: Introductionmentioning

confidence: 99%

Cortical Map Reorganization as a Competitive Process

et al. 1994

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“…This evolution of OM leads to changes in the computed efficacy of excitatory synapses impinging on the barrel D2 cell, and the changes in cell response match the empirical electrophysiological observations. The ability of the present and previous BCM models (13,14) to explain complex experimental findings argues that the OM reflects an actual physiological mechanism.…”

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confidence: 62%

Dynamic synaptic modification threshold: computational model of experience-dependent plasticity in adult rat barrel cortex.

Benuskova

Diamond

Ebner

1994

Proc. Natl. Acad. Sci. U.S.A.

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Previous electrophysiological experiments have documented the response of neurons in the adult rat somatic sensory ("barrel") cortex to whisker movement after normal experience and after periods of experience with all but two whiskers trimmed close to the face (whisker "pairing9). To better understand how the barrel cortex adapts to changes in the flow of sensory activity, we have developed a computational model of a single representative barrel cell based on the Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity. The hallmark of the BCM theory is the dynamic synaptic modification threshold, Om, which dictates whether a neuron's activity at any given instant will lead to strengthening or weakening of the synapses impinging on it. The threshold OM is proportional to the neuron's activity averaged over some recent past. Whisker pairing was simulated by setting input activities of the cell to the noise level, except for two inputs that represented untrimmed whiskers. Initially low levels of cell activity, resulting from whisker trimming, led to low values for M. As certain synaptic weights potentiated, due to the activity of the paired inputs, the values of OM increased and after some time their mean reached an asymptotic value. This saturation of OM led to the depression of some inputs that were originally potentiated. The changes in cell response generated by the model replicated those observed in in vivo experiments. Previously, the BCM theory has explained salient features of developmental experience-dependent plasticity in kitten visual cortex. Our results suggest that the idea of a dynamic synaptic modification threshold, OM, is general enough to explain plasticity in different species, in different sensory systems, and at different stages of brain maturity.Although some forms of experience-dependent cortical plasticity disappear at the end of a developmental "critical" period (1, 2), the adult cortex retains a significant capacity to undergo functional changes in response to alterations in sensory input (3,4). We are interested in the rules that determine how the adult rat whisker system adapts to changes in the pattern ofafferent activity. The facial whiskers of rats are aligned in five rows (row A is dorsal and row E is ventral) and the whiskers within a row are numbered from caudal to rostral. Each facial whisker projects via the trigeminal nuclei and the thalamic ventral posterior medial nucleus (VPM) to a separate cluster ofneurons in layer IV ofa cortical barrel (5-8). Recently, we observed that the receptive fields (RFs) of neurons in adult rat barrel cortex were changed by altered whisker use (refs. 9-11; M.E.D., L.B., M. A. Nicolelis, F.F.E., and J. K. Chapin, unpublished work). To alter the pattern of sensory activity, all whiskers except two, D2 and one neighbor in the D row, were cut close to the fur on one side of the face. After 3, 7-10, or 30 days of whisker pairing (whiskers were reclipped regularly), the activity of single neurons in barrel D2 was measured in response...

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Computational study of experience-dependent plasticity in adult rat cortical barrel-column

Benuskova

Ebner

Diamond

et al. 1999

Network: Computation in Neural Systems

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We model experience-dependent plasticity in the adult rat S1 cortical representation of the whiskers (the barrel cortex) which has been produced by trimming all whiskers on one side of the snout except two. This manipulation alters the pattern of afferent sensory activity while avoiding any direct nerve damage. Our simplified model circuitry represents multiple cortical layers and inhibitory neurons within each layer of a barrel-column. Utilizing a computational model we show that the evolution of the response bias in the barrel-column towards spared whiskers is consistent with synaptic modifications that follow the rules of the Bienenstock, Cooper and Munro (BCM) theory. The BCM theory postulates that a neuron possesses a dynamic synaptic modification threshold, thetaM, which dictates whether the neuron's activity at any given instant will lead to strengthening or weakening of the synapses impinging on it. However, the major prediction of our model is the explanation of the delay in response potentiation in the layer-IV neurons through a masking effect produced by the thresholded monotonically increasing inhibition expressed by either the logarithmic function, h(x) = mu log(1 + x), or by the power function, h(x) = mu x(0.8-0.9), where mu is a constant. Furthermore, simulated removal of the supragranular layers (layers II/III) reduces plasticity of neurons in the remaining layers (IV-VI) and points to the role of noise in synaptic plasticity.

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A simulation of somatosensory cortical map plasticity

Cited by 4 publications

References 8 publications

Cortical Map Reorganization as a Competitive Process

Cortical Map Reorganization as a Competitive Process

Dynamic synaptic modification threshold: computational model of experience-dependent plasticity in adult rat barrel cortex.

Computational study of experience-dependent plasticity in adult rat cortical barrel-column

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