Exemplo n.º 1
0
def activity(x, encoders, alpha, bias):
    '''Implements J = alpha * x.dot(encoders) + bias, expands dimensions if
    all arguments are 1-D. Result is of shape (x.shape[0], encoders.shape[0])
    * x: The range over which to render the functions
    * encoders: Vectors which dot-x
    * alpha: 1D gain coefficient
    * bias: 1D additive bias term'''
    (x, encoders, alpha, bias) = utils.force_array((x, encoders, alpha, bias))
    return utils.dot_expand(x, encoders) * alpha + bias
Exemplo n.º 2
0
def lif_fit(x_max, x_intercepts, y_targets, encoders, t_ref=0.002, t_rc=0.02):
    '''Returns [[alpha, J_bias]] for 'leaky-integrate-and-fire'/lif tuning model
    '''
    (x_max, x_intercepts,
     y_targets, encoders) = utils.force_array((x_max, x_intercepts,
                                               y_targets, encoders))
    B = np.exp((1./t_rc) * (t_ref - 1 / y_targets))
    alpha = (1. / (1. - B) - 1.) / utils.diag_dot(x_max - x_intercepts, encoders)
    bias = 1. - alpha * utils.diag_dot(x_intercepts, encoders)
    return np.vstack((alpha, bias))
Exemplo n.º 3
0
def linear_tuning_fit(x_targets, x_intercepts, y_targets, encoders):
    '''Returns [[alpha, J_bias]] for rect_linear tuning curve'''

    (x_targets, x_intercepts,
     y_targets, encoders) = utils.force_array((x_targets, x_intercepts,
                                               y_targets, encoders))
    alphas = None
    J_biases = None
    alphas = y_targets / utils.diag_dot(x_targets - x_intercepts, encoders)
    J_biases = -utils.diag_dot(x_intercepts, encoders) * alphas
    return np.vstack((alphas, J_biases))