Elephant 0.1.0 released

We are pleased to announce the first release of the Elephant toolbox for the analysis of neurophysiology data.

Elephant builds on the Python scientific stack (NumPy, SciPy) to provide a set of well-tested analysis functions for spike train data and time series recordings from electrodes, such as spike train statistics, power spectrum analysis, filtering, cross-correlation and spike-triggered averaging. The toolbox also provides tools to generate artificial spike trains, either from stochastic processes or by controlled perturbations of real spike trains. Elephant is built on the Neo data model, and takes advantage of the broad file-format support provided by the Neo library. A bridge to the Pandas data analysis library is also provided.

Elephant is a community-based effort, aimed at providing a common platform to test and distribute code from different laboratories, with the goal of improving the reproducibility of modern neuroscience research. If you are a neuroscience researcher or student using Python for data analysis, please consider joining us, either to contribute your own code or to help with code review and testing.

Elephant is the direct successor to NeuroTools and maintains ties to complementary projects such as OpenElectrophy and SpykeViewer. It is also the default tool for electrophysiology data analysis in the Human Brain Project.

As a simple example, let's generate some artificial spike train data using a homogeneous Poisson process:

from elephant.spike_train_generation import homogeneous_poisson_process
from quantities import Hz, s, ms
spiketrains = [
    homogeneous_poisson_process(rate=10.0*Hz, t_start=0.0*s, t_stop=100.0*s)
    for i in range(100)]

and visualize it in Matplotlib:

import matplotlib.pyplot as plt
import numpy as np
for i, spiketrain in enumerate(spiketrains):
    t = spiketrain.rescale(ms)
    plt.plot(t, i * np.ones_like(t), 'k.', markersize=2)
plt.axis('tight')
plt.xlim(0, 1000)
plt.xlabel('Time (ms)', fontsize=16)
plt.ylabel('Spike Train Index', fontsize=16)
plt.gca().tick_params(axis='both', which='major', labelsize=14)
plt.show()
Now we calculate the coefficient of variation of the inter-spike interval for each of the 100 spike trains.

from elephant.statistics import isi, cv
cv_list = [cv(isi(spiketrain)) for spiketrain in spiketrains]

As expected for a Poisson process, the values cluster around 1:

plt.hist(cv_list)
plt.xlabel('CV', fontsize=16)
plt.ylabel('count', fontsize=16)
plt.gca().tick_params(axis='both', which='major', labelsize=14)
plt.show()

ANN: HoloViews 1.0 data visualization and ImaGen 2.0 pattern generation in Python

We are pleased to announce the first public release of HoloViews, a free Python package for scientific and engineering data visualization:

     http://ioam.github.io/holoviews

and version 2.0 of ImaGen, a free Python package for generating two-dimensional patterns useful for vision research and computational modeling:

     http://ioam.github.io/imagen

HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations of any scientific data very easily.  With HoloViews, you can see your data as publication-quality figures almost instantly, so that you can focus on the data itself, rather than on laboriously putting together your figures.  Even complex multi-subfigure layouts and animations are very easily built using HoloViews.

ImaGen provides highly configurable, resolution-independent input patterns, directly visualizable using HoloViews but also available without any plotting package so that they can easily be incorporated directly into your computational modeling or visual stimulus generation code.  With ImaGen, any software with a Python interface can immediately support configurable streams of 0D, 1D, or 2D patterns, without any extra coding.

HoloViews and ImaGen are very general tools, but they were designed to solve common problems faced by vision scientists and computational modelers.  HoloViews makes it very easy to visualize data from vision research, whether it is visual patterns, neural activity patterns, or more abstract measurements or analyses.  Essentially, HoloViews provides a set of general, compositional, multidimensional data structures suitable for both discrete and continuous real-world data, and pairs them with separate customizable plotting classes to visualize them without extensive coding.

ImaGen 2.0 uses the continuous coordinate systems provided by HoloViews to implement flexible resolution-independent generation of streams of patterns, with parameters controlled by the user and allowing randomness or other arbitrary changes over time.  These patterns can be used for visual stimulus generation, testing or training computational models, initializing connectivity in models, or any other application where random or dynamic but precisely controlled streams of patterns are needed.

Features:

- Freely available under a BSD license
- Python 2 and 3 compatible
- Minimal external dependencies -- easy to integrate into your workflow
- Declarative approach provides powerful compositionality with minimal coding
- Include extensive, continuously tested IPython Notebook tutorials
- Easily reconfigurable using documented and validated parameters
- Animations are supported natively, with no extra work
- Supports reproducible research -- simple specification, archived in an IPython Notebook, providing a recipe for regenerating your results
- HoloViews is one of three winners of the 2015 UK Open Source Awards

To get started, check out ioam.github.io/holoviews and ioam.github.io/imagen!

Jean-Luc Stevens
Philipp Rudiger
Christopher Ball
James A. Bednar

Students: spend the summer improving brain research software tools in Google Summer of Code

From Malin Sandström at the INCF:

Are you a student interested in brain research and software development? Or do you know one?

This year again, INCF is participating as mentoring organization in the Google Summer of Code, a global program that offers students stipends to spend the summer writing code for open source projects. INCF has 27 project proposals offered by mentors from the international research community, many of them with a computational neuroscience slant. All projects deal with development and/or improvement of open source tools that are used in the neuroscience community.

You can see our full list of projects here: https://incf.org/gsoc/2015/proposals

To be eligible, students must fulfill the Google definition of 'student': an individual enrolled in or accepted into an accredited institution including (but not necessarily limited to) colleges, universities, masters programs, PhD programs and undergraduate programs.

Student applications open on Monday, March 16.

GSoC questions welcome to: gsoc@incf.org

Workshop Announcement - "HBP Hippocamp CA1: Collaborative and Integrative Modeling of Hippocampal Area CA1"

Registration is now open for the workshop "HBP Hippocamp CA1: Collaborative and Integrative Modeling of Hippocampal Area CA1", to be held March 31st - April 1st, 2015 at UCL School of Pharmacy in London, supported by the Human Brain Project (www.humanbrainproject.eu).

 In short, the aims of the workshop are two-fold. First, to engage the larger community of experimentalists and modelers working on hippocampus, and highlight existing modeling efforts and strategic datasets for modeling hippocampal area CA1. Second, to define and bootstrap an inclusive community-driven model and data-integration process to achieve open pre-competitive reference models of area CA1 (and, ultimately, the rest of the hippocampus), which are well documented, validated, and released at regular intervals (supported in part by IT infrastructure funded by HBP). Involvement from the community interested in characterization and modeling of the hippocampus is highly encouraged. To keep the meeting focused on the task, participation will be limited to ~30 people, so registration is required.

Please consult the meeting website at http://neuralensemble.org/meetings/HippocampCA1/ for registration and further details.

Organizing committee: Jo Falck (UCL), Szabolcs Káli (Hungarian Academy of Sciences), Sigrun Lange (UCL), Audrey Mercer (UCL), Eilif Muller (EPFL), Armando Romani (EPFL) and Alex Thomson (UCL).

PyNN 0.8 beta 2 released

We're happy to announce the second beta release of PyNN 0.8.

With this release we are getting close to a first release candidate: the NEST and NEURON backends are essentially feature complete, although with a few bugs remaining. If you're using one of these simulators as your PyNN backend we recommend using this beta release for new projects; now would also be a good time to think about upgrading existing projects. The Brian backend is less well developed, but considerable progress has been made.

For a list of the main changes between PyNN 0.7 and 0.8, see the release notes for the 0.8 alpha 1 release.

For the changes in this beta release see the release notes. In particular note that this release requires NEST version 2.4, and for the first time supports Python 3.2+ as well as Python 2.6 and 2.7. Support for NEST 2.6 will be provided in an upcoming release.

The source package is available from the INCF Software Center.

What is PyNN?

PyNN (pronounced 'pine' ) is a simulator-independent language for building neuronal network models.

In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian).

Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models.

PyNN is also being used as a user-friendly interface to neuromorphic hardware systems.

The code is released under the CeCILL licence (GPL-compatible).

GSoC Open Source Brain: Cortical Connections

Cortical Connections

In the same vein that the post before this one we will show here how to construct the connections between the cortical layers. In order to do so we will construct a function that works in general for any arbitrary connectivity, we describe in the following its structure. First, as in the thalamo-cortical connectivity, we have again the same structure of a function that loops over the target population extracting the relevant parameters that characterize these neurons. Furthermore we have another function that loops over the source population creating the corresponding tuples for the connection list. Is in this last function where the particular connectivity rule is implemented.

In the particular case of the Troyer model the connectivity between the cortical cells is determined by the correlation between the receptive fields of the neurons, the receptive fields here being Gabor functions. In more detail the neurons whose receptive fields are more correlated will be the ones more likely to have excitatory connections between them. On the other hand the ones whose receptive fields are less correlated will be more likely to receive inhibitory connections. In this post we show two schemes that accomplish this connectivity. The first one uses the fact the parameters of the receptive field to calculate a connectivity and the second one uses the receptive fields directly to calculate the correlations. We present the determining functions in the stated order down here.

Now we present the function that creates the connectivity for a given neuron par. The circular distance between the orientation and phases are calculated as a proxy to estimate how similar the receptive fields of the neurons are. After that, the distance between them is weighted and normalized with a normal function in order to obtain a value that we can interpret as a probability value. Finally in order to calculate the connectivity we sample n_pick times with the given probability value to see hwo strong a particular connection should be.

 
def cortical_to_cortical_connection(target_neuron_index, connections, source_population, n_pick, g, delay, source_orientations,
                                    source_phases, orientation_sigma, phase_sigma, target_neuron_orientation,
                                    target_neuron_phase, target_type):
    """
    Creates the connections from the source population to the target neuron

    """
    for source_neuron in source_population:
        # Extract index, orientation and phase of the target
        source_neuron_index = source_population.id_to_index(source_neuron)
        source_neuron_orientation = source_orientations[source_neuron_index]
        source_neuron_phase = source_phases[source_neuron_index]

        # Now calculate phase and orientation distances
        or_distance = circular_dist(target_neuron_orientation, source_neuron_orientation, 180)

        if target_type:
            phase_distance = circular_dist(target_neuron_phase, source_neuron_phase, 360)
        else:
            phase_distance = 180 - circular_dist(target_neuron_phase, source_neuron_phase, 360)

        # Now calculate the gaussian function
        or_gauss = normal_function(or_distance, mean=0, sigma=orientation_sigma)
        phase_gauss = normal_function(phase_distance, mean=0, sigma=phase_sigma)

        # Now normalize by guassian in zero
        or_gauss = or_gauss / normal_function(0, mean=0, sigma=orientation_sigma)
        phase_gauss = phase_gauss / normal_function(0, mean=0, sigma=phase_sigma)

        # Probability is the product
        probability = or_gauss * phase_gauss
        probability = np.sum(np.random.rand(n_pick) < probability)  # Samples
        synaptic_weight = (g / n_pick) * probability


        if synaptic_weight > 0:
                    connections.append((source_neuron_index, target_neuron_index, synaptic_weight, delay))

    return connections
      
    

Note that the overall strength is weighted by the conductivity value g that is passed as an argument. Furthermore a delay that is also passed as an argument is added to the list as the last element of the tuple.

Secondly we present the full correlation scheme. In this scheme we utilize the kernels directly to calculate the spatial correlation between them. In particular after we have flattened our kernels Z to have a series instead of a matrix we use the function perasonr from scipy.stats to calculate the correlation. Again as in the case above we use this probability to sample n_pick times and then calculate the relative connectivity strength with this.

 
def cortical_to_cortical_connection_corr(target_neuron_index, connections, source_population, n_pick, g, delay,
                                    source_orientations, source_phases, target_neuron_orientation, target_neuron_phase,
                                    Z1, lx, dx, ly, dy, sigma, gamma, w, target_type):
    """
    Creates the connections from the source population to the target neuron

    """
    for source_neuron in source_population:
        # Extract index, orientation and phase of the target
        x_source, y_source = source_neuron.position[0:2]
        source_neuron_index = source_population.id_to_index(source_neuron)
        source_neuron_orientation = source_orientations[source_neuron_index]
        source_neuron_phase = source_phases[source_neuron_index]

        Z2 = gabor_kernel(lx, dx, ly, dy, sigma, gamma, source_neuron_phase, w, source_neuron_orientation,
                          x_source, y_source)


        if target_type:
            probability = pearsonr(Z1.flat, Z2.flat)[0]
        else:
            probability = (-1) * pearsonr(Z1.flat, Z2.flat)[0]

        probability = np.sum(np.random.rand(n_pick) < probability)  # Samples
        synaptic_weight = (g / n_pick) * probability

        if synaptic_weight > 0:
                    connections.append((source_neuron_index, target_neuron_index, synaptic_weight, delay))

    return connections
    

Note that the overall strength is weighted by the conductivity value g that is passed as an argument. Furthermore a delay that is also passed as an argument is added to the list as the last element of the tuple.

We now show how a plot that illustrates how the probabilities change when the parameters that determined the gabor function are changed for each scheme.

In the figure above we have int he upper part how the probability for the first scheme a neuron with phase 0 and orientation 0 change as we vary the phase (left) and orientation (right). In the two graphs bellow we have the same for the second scheme we presented

GSoC Open Source Brain: Thalamo-Cortical Connections

Thalamo-cortical connections

In this post I will show how to build arbitrary custom connections in PyNN. We will illustrate the general technique in the particular case of the Troyer model. In the Troyer model the connections from the LGN to the cortex are determined with a gabor-profile therefore I am going to describe the required functions to achieve such an aim.

In the PyNN documentation we find that one of the ways of implementing arbitrary connectivity patterns is to use the FromListConnector utility. In this format we have to construct a list of tuples with a tuple for each connection. In each tuple we need to include the index of the source neuron (the neuron from which the synapse originates), the index of the target neuron (the neuron into which the synapse terminates), the weight and the delay. For example (0, 1, 5, 0.1) would indicate that we have a connection from the neuron 0 to the neuron 1 with a synaptic weight of 5 and a delay of 0.1.

In the light of the explanation above we need to construct a function that is able to construct a list with the appropriate weights given a target and a source populations. In order to start moving towards this goal we will first write a function that connects a given neuron in the target population to all the neurons in the source population. We first present the function here bellow and we will explain it later:

 
def lgn_to_cortical_connection(cortical_neuron_index, connections, lgn_neurons, n_pick, g, delay, polarity, sigma,
                               gamma, phi, w, theta, x_cortical, y_cortical):
    """
    Creates connections from the LGN to the cortex with a Gabor profile.

    This function adds all the connections from the LGN to the cortical cell with index = cortical_neuron_index. It
    requires as parameters the cortical_neruon_index, the current list of connections, the lgn population and also
    the parameters of the Gabor function.

    Parameters
    ----
    cortical_neuron_index : the neuron in the cortex -target- that we are going to connect to
    connections: the list with the connections to which we will append the new connnections
    lgn_neurons: the source population
    n_pick: How many times we will sample per neuron
    g: how strong is the connection per neuron
    delay: the time it takes for the action potential to arrive to the target neuron from the source neuron
    polarity: Whether we are connection from on cells or off cells
    sigma: Controls the decay of the exponential term
    gamma: x:y proportionality factor, elongates the pattern
    phi: Phase of the overall pattern
    w: Frequency of the pattern
    theta: Rotates the whole pattern by the angle theta
    x_cortical, y_cortical : The spatial coordinate of the cortical neuron

    """

    for lgn_neuron in lgn_neurons:
            # Extract position
            x, y = lgn_neuron.position[0:2]
            # Calculate the gabbor probability
            probability = polarity * gabor_probability(x, y, sigma, gamma, phi, w, theta, x_cortical, y_cortical)
            probability = np.sum(np.random.rand(n_pick) < probability)  # Samples

            synaptic_weight = (g / n_pick) * probability
            lgn_neuron_index = lgn_neurons.id_to_index(lgn_neuron)

            # The format of the connector list should be pre_neuron, post_neuron, w, tau_delay
            if synaptic_weight > 0:
                connections.append((lgn_neuron_index, cortical_neuron_index, synaptic_weight, delay))

    

The first thing to note from the function above are its arguments. It contains the source population and the particular target neuron that we want to connect to. It also contains all the connectivity and gabor-function related parameters. In the body of the function we have one loop over the whole source population that decides whether we add a connection from a particular cell or not. In order to decide if we add a connection we have to determine the probability from the gabor function. Once we have this we sample n_pick times and add a weighted synaptic weight accordingly to the list for each neuron.

In the function above we have the values of the gabor function passed as arguments. However, the values of each gabor function depend on the nature of the cell of the target population. In the light of this we will construct another function that loops over the target population and extracts the appropriate gabor values for each function in this population. We again present the function and then explain it:

def create_lgn_to_cortical(lgn_population, cortical_population, polarity,  n_pick, g, delay,  sigma, gamma, phases,
                           w, orientations):
    """
    Creates the connection from the lgn population to the cortical population with a gabor profile. It also extracts
    the corresponding gabor parameters that are needed in order to determine the connectivity.
    """

    print 'Creating connection from ' + lgn_population.label + ' to ' + cortical_population.label

    # Initialize connections
    connections = []

    for cortical_neuron in cortical_population:
        # Set the parameters
        x_cortical, y_cortical = cortical_neuron.position[0:2]
        cortical_neuron_index = cortical_population.id_to_index(cortical_neuron)
        theta = orientations[cortical_neuron_index]
        phi = phases[cortical_neuron_index]

        # Create the connections from lgn to cortical_neuron
        #lgn_to_cortical_connection(cortical_neuron_index, connections, lgn_population, n_pick, g, polarity, sigma,
        #gamma, phi, w, theta, x_cortical, y_cortical)

        lgn_to_cortical_connection(cortical_neuron_index, connections, lgn_population, n_pick, g, delay, polarity, sigma,
                                   gamma, phi, w, theta, 0, 0)

    return connections
    

This function requires as arguments the source and target populations as well as the necessary parameters that characterize each cell connectivity: orientation and phase. In the body of the function we have a loop over the cortical population that extracts the relevant parameters -position, orientation and phase- and then calls the function that we already describe previously in order to create the connectivity from the source population to the cell in place.

So now we have the necessary functions to construct a list. Now, we can use FromListConnector to transform the list into a connector. And the use this to define a Projection. We define both the excitatory and inhibitory connections. We abstract this complete set into the following function:

def create_thalamocortical_connection(source, target, polarity, n_pick, g, delay, sigma, gamma, w, phases, orientations, simulator):
    """
    Creates a connection from a layer in the thalamus to a layer in the cortex through the mechanism of Gabor sampling
    """

    # Produce a list with the connections
    connections_list = create_lgn_to_cortical(source, target, polarity, n_pick, g, delay, sigma, gamma, phases, w, orientations)

    # Transform it into a connector
    connector = simulator.FromListConnector(connections_list, column_names=["weight", "delay"])

    # Create the excitatory and inhibitory projections
    simulator.Projection(source, target, connector, receptor_type='excitatory')
    simulator.Projection(source, target, connector, receptor_type='inhibitory')      
    

With this we can create in general connections from one target population to the other. We can even change change the gabor function for whatever we want if we want to experiment with other connectivity patterns. Finally we present down here an example of a sampling from a Gabor function with the aglorithm we just constructed:

So in the image we show in the left the sampling from the ideal Gabor function in the right.