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P J Sjöström

Publications and source records attributed to P J Sjöström.

2 recordsLinked to original sources

Rate, timing, and cooperativity jointly determine cortical synaptic plasticity.

Cortical long-term plasticity depends on firing rate, spike timing, and cooperativity among inputs, but how these factors interact during realistic patterns of activity is unknown. Here we monitored plasticity while systematically varying the rate, spike timing, and number of coincident afferents. These experiments demonstrate a novel form of cooperativity operating even when postsynaptic firing is evoked by current injection, and reveal a complex dependence of LTP and LTD on rate and timing. Based on these data, we constructed and tested three quantitative models of cortical plasticity. One of these models, in which spike-timing relationships causing LTP "win" out over those favoring LTD, closely fits the data and accurately predicts the build-up of plasticity during random firing. This provides a quantitative framework for predicting the impact of in vivo firing patterns on synaptic strength.

Action Potentials↗

Artificial neural network-aided image analysis system for cell counting.

BACKGROUND: In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. METHODS: Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. RESULTS: The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. CONCLUSIONS: ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

Cell Count↗