Multidimensional scaling in observation space and likelihood space.

<p>A multidimensional scaling technique is used to illustrate the capability of the likelihood space in increasing the separability of the clusters. (A) The distance measurement and multidimensional scaling results for pairs of spike trains from the human face and car stimuli in the observation space. (B) The distance measurement and multidimensional scaling results for the same spike trains after projection onto the likelihood space.</p

Projection of spike train onto likelihood space.

<p>Sample response of a single neuron to face stimulus presentation in raster plot format. This data is for the repeated trials, where each row is the spike train recorded for any individual trial. The transformation of the spike train for the single trial, from the observation space into a likelihood space, is illustrated. Based on previous observations and estimated stimuli conditional probability distribution, each point in the new space is generated by the projection of the binary vector of spike train.</p

Passive fixation task.

<p>The paradigm for the passive fixation task is illustrated. The presentation of the stimulus sequence started after the monkey maintained fixation for 300 ms. Each stimulus lasted 300 ms and was followed by another stimulus after a 700 ms interstimulus interval. The sequence stopped when 36 stimuli were presented, or when the monkey broke the gaze fixation.</p

Information content of the face neuron.

<p>(A) The face specific information is approximated in the rate based framework. Based on a peristimulus time histogram, the relevant probability densities are estimated empirically and used for face specific information calculation. (B) The face specific information is approximated in the likelihood space based framework. The probability model of the joint spiking activity is used for face specific information estimation.</p

Model parameter estimation.

<p>Sample responses of a neuron from IT cortex of a macaque monkey while performing the passive fixation task. The spike trains in repeated trials, in the form of a raster plot and the estimated conditional intensity function are shown for (A) a human face presentation with 95% goodness-of-fit criteria and (B) a car presentation. For face stimulus the raster plot is used for fitting the point process model on the neuronal responses with the conditional intensity estimation. The goodness-of-fit criterion is used to compare the point process model with conventional peristimulus time histogram.</p

Recording areas and the average firing rate's response of the neuronal population.

<p>Recording positions were evenly distributed at anterior 14–20 mm over the ventral bank of the superior temporal sulcus and the ventral convexity up to the medial bank of the anterior middle temporal sulcus with 1-mm track intervals.</p

Extending the likelihood space for populations of neurons.

<p>The likelihood space generation for populations of neurons based on projecting the spiking activity of the population recorded from 100 neurons in IT cortex. These recordings were taken while the human face, dogface, and car images were presented to the monkey. The marked point processes theory was used for developing the probability model for the population.</p

Dynamic between-stimulus distance measure.

<p>(A) Dynamic distance measurement between pairs of stimuli from two different categories in a 100-ms sliding time window, with 10-ms sliding step based on correlation distance. (B) Dynamic distance measurement for the same stimulus pair with 100-ms sliding time window and 10-ms sliding step based on stimulus distance in the likelihood space.</p

Projection onto likelihood space.

<p>The repeated trial observation of neuronal spiking activity was used to estimate the probability model of the spike train. This enabled us to transfer any spike train into likelihood space and represent it as a single point. The coordinate components of this point are equal to the probability of spike train generated from a specific stimulus. (A) Reconstruction of likelihood space for the neural activity of a single neuron in IT cortex, while the human face and car pictures were presented. Since we reconstructed the space with respect only two stimuli, the projected space has only two dimensions. (B) The likelihood space was generated for the same neuron while spike trains from presenting human face, dog face, and car images were projected.</p