The for statement followed by the if conditional is equivalent to nesting the if statement inside the for loop in the previous example. This allows us to retrieve the index (timing) of only values when that represent spikes (recall we defined spike_value as 1). To the end of this is added a conditional, if x = spike_value. In this case, they are x, which is one value in spike_train, and i, the enumeration variable that stores the current position in the list (iteration through the loop). The enumerate() function enumerates (counts) the items as it goes through a loop, so each time through the loop we have two variables being tracked. It’s a useful and compact way to iterate over some set of items to get them into a list. Take some time to break this down and understand it - there’s a lot going on! Recall that list comprehension is used to create a for loop in a single line, and return a list. This is a slightly more complicated list comprehension than we’ve seen in the past. Each value represents an evenly-spaced point in time (e.g., every 1 ms), where 0 encodes time points at which no action potential was detected, and 1 represents times when action potentials occurred: We can store the data from each trial in a list, spike_train. Data was sampled from the electrode every millisecond. On each trial, recording was started and then the 550 nm light was turned on 4 ms later, for a duration of 10 ms. When light of that wavelength is directed at the neuron, it tends to fire.įor this hypothetical experiment, we used an electrode to record action potentials from this optogenetically-engineered neuron to confirm that it tends to fire in response to 550 nm light. Imagine an experiment involving a neuron that was genetically engineered for optogenetics, meaning that it expresses genes sensitive to a specific wavelength of light (550 nm, which is green to learn more about optogenetics, check out this video by Prof. # Import libraries we'll need import matplotlib.pyplot as plt import numpy as np Our first spike train # Reading and Visualizing Structural MRI Data Working with Multielectrode Data in pandasĪveraging ERPs: Creating MNE Evoked objects Procedural versus Object-Oriented Plotting in MatplotlibĪccessibility and Human Factors in Plotting At a time of growing political unrest around the world, the stress and discontent fueled by high food prices merits close attention.Introduction to Jupyter Notebooks in CoCalc Whether the primary concern is people’s well-being, or political stability, food price spikes should be a cause both for concern and for action. Such findings point to the need for a twin-track response to food price spikes: dynamic, accountable and progressive action by national governments, backed by greatly improved, co-ordinated responses at the global level. They analyze the causes of the problems they face as political problems, identifying a lack of responsiveness to their needs, and corruption and collusion among powerful politicians and business interests, as among the sources of the problems they face. They also respond politically: they contest official explanations of the causes, and they roundly criticize their governments for failing to act effectively. Key findings show that poor people do not merely cope by working harder, eating less, living more frugally, drawing down resources and assets, and managing on a day-to-day basis. The research offers insights into how economic shocks of this kind work to increase and perpetuate inequality, producing consistent patterns of ‘weak losers’ and ‘strong winners’. In order to understand the impact of the rise in global food prices through much of 2010 and into early 2011, Oxfam and research partners from the Institute of Development Studies spoke to people effected in Bangladesh, Indonesia, Kenya, and Zambia. The human face of global food price rises is often missing amongst the abstract discussions of macro-economic trends and global food price indices.
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