7 research outputs found
How spiking neurons give rise to a temporal-feature map
A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cellsâ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit
from the nonlinearity
Self-Organized Criticality in Developing Neuronal Networks
Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (nâ=â20) and find four different phases, related to their morphological maturation: An initial low-activity state (â19 DIV) is followed by a supercritical (â20 DIV) and then a subcritical one (â36 DIV) until the network finally reaches stable criticality (â58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro
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Dynamics of neuronal populations modeled by a WilsonâCowan system account for the transient visibility of maskedstimul
Encoding of Dynamic Visual Stimuli by Primate Area MT Neurons
Neural stimulus selectivity is thought to be optimized for the representation of real-world stimuli. Neural coding properties, therefore, may adapt to different environments. Here, we address the question if tuning curves depend on the statistics of visual stimuli. This is done by studying the directional tuning of macaque area MT neurons exposed to dynamic motion stimuli of two different direction progression statistics. Despite an apparent difference of tuning curves across stimulus conditions, our results support the view that the underlying encoding system is robust and subject to only restricted malleability by stimulus statistics. Key words: directional tuning, stimulus statistics, area MT, reverse correlation