Based on anatomical and physiological data, we have developed a computer simulation of piri-form (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a var...
Based on anatomical and physiological data, we have developed a computer simulation of piri-form (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a variety of conditions. Using a simple Hebb-type learning rule in conjunction with the cortical dynamics which emerge from the anatomical and physiological organization of the model, the simulations are capable of establishing cortical representations for different input patterns. The basis of these representations lies in the interaction of sparsely distributed, highly divergent/convergent interconnections between modeled neurons. We have shown that different representations can be stored with minimal interference. and that following learning these representations are resistant to input degradation, allowing reconstruction of a representation following only a partial presentation of an original training stimulus. Further, we have demonstrated that the degree of overlap of cortical representations for different stimuli can also be modulated. For instance similar input patterns can be induced to generate distinct cortical representations (discrimination). while dissimilar inputs can be induced to generate overlapping representations (accommodation). Both features are presumably important in classifying olfactory stimuli.
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The ...
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems- a credit card application task, and a problem in medical diagnosis. A measure of the monotonicity error of a candidate function is defined and an objective function for the enforcement of monotonicity is derived from Bayesian principles. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance on both problems.
Neurons and their networks underlie our perceptions, actions and memories. The latest work on information processing and storage at the single-cell level reveals previously unimagined complexity and dynamism.
Neurons and their networks underlie our perceptions, actions and memories. The latest work on information processing and storage at the single-cell level reveals previously unimagined complexity and dynamism.
Feature detection and tracking is a fundamental problem in computer vision research. By detecting and tracking features in an image sequence it is possible to recover information about both the motion of the viewer an...
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Intradendritic electrophysiological recordings reveal a bewildering repertoire of complex electrical spikes and plateaus that are difficult to reconcile with conventional notions of neuronal function. In this paper we...
Intradendritic electrophysiological recordings reveal a bewildering repertoire of complex electrical spikes and plateaus that are difficult to reconcile with conventional notions of neuronal function. In this paper we argue that such dendritic events are just an exuberant expression of a more important mechanism - a proportional current amplifier whose primary task is to offset electrotonic losses. Using the example of functionally important synaptic inputs to the superficial layers of an anatomically and electrophysiologically reconstructed layer 5 pyramidal neuron, we derive and simulate the properties of conductances that linearize and amplify distal synaptic input current in a graded manner. The amplification depends on a potassium conductance in the apical tuft and calcium conductances in the apical trunk.
Single nerve cells with static properties have traditionally been viewed as the building blocks for networks that show emergent phenomena. In contrast to this approach, we study here how the overall network activity c...
ISBN:
(纸本)9781558602229
Single nerve cells with static properties have traditionally been viewed as the building blocks for networks that show emergent phenomena. In contrast to this approach, we study here how the overall network activity can control single cell parameters such as input resistance, as well as time and space constants, parameters that are crucial for excitability and spatio-temporal integration. Using detailed computer simulations of neocortical pyramidal cells, we show that the spontaneous background firing of the network provides a means for setting these parameters. The mechanism for this control is through the large conductance change of the membrane that is induced by both non-NMDA and NMDA excitatory and inhibitory synapses activated by the spontaneous background activity.
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