Human single-neuron responses at the threshold of conscious recognition.

We studied the responses of single neurons in the human medial temporal lobe while subjects viewed familiar faces, animals, and landmarks. By progressively shortening the duration of stimulus presentation, coupled with backward masking, we show two striking properties of these neurons. (i) Their responses are not statistically different for the 33-ms, 66-ms, and 132-ms stimulus durations, and only for the 264-ms presentations there is a significantly higher firing. (ii) These responses follow conscious perception, as indicated by the subjects' recognition report. Remarkably, when recognized, a single snapshot as brief as 33 ms was sufficient to trigger strong single-unit responses far outlasting stimulus presentation. These results suggest that neurons in the medial temporal lobe can reflect conscious recognition by "all-or-none" responses.

Sparse but not 'grandmother-cell' coding in the medial temporal lobe.

Although a large number of neuropsychological and imaging studies have demonstrated that the medial temporal lobe (MTL) plays an important role in human memory, there are few data regarding the activity of neurons involved in this process. The MTL receives massive inputs from visual cortical areas, and evidence over the last decade has consistently shown that MTL neurons respond selectively to complex visual stimuli. Here, we focus on how the activity patterns of these cells might reflect the transformation of visual percepts into long-term memories. Given the very sparse and abstract representation of visual information by these neurons, they could in principle be considered as 'grandmother cells'. However, we give several arguments that make such an extreme interpretation unlikely.

Decoding visual inputs from multiple neurons in the human temporal lobe.

We investigated the representation of visual inputs by multiple simultaneously recorded single neurons in the human medial temporal lobe, using their firing rates to infer which images were shown to subjects. The selectivity of these neurons was quantified with a novel measure. About four spikes per neuron, triggered between 300 and 600 ms after image onset in a handful of units (7.8 on average), predicted the identity of images far above chance. Decoding performance increased linearly with the number of units considered, peaked between 400 and 500 ms, did not improve when considering correlations among simultaneously recorded units, and generalized to very different images. The feasibility of decoding sensory information from human extracellular recordings has implications for the development of brain-machine interfaces.

Invariant visual representation by single neurons in the human brain.

It takes a fraction of a second to recognize a person or an object even when seen under strikingly different conditions. How such a robust, high-level representation is achieved by neurons in the human brain is still unclear. In monkeys, neurons in the upper stages of the ventral visual pathway respond to complex images such as faces and objects and show some degree of invariance to metric properties such as the stimulus size, position and viewing angle. We have previously shown that neurons in the human medial temporal lobe (MTL) fire selectively to images of faces, animals, objects or scenes. Here we report on a remarkable subset of MTL neurons that are selectively activated by strikingly different pictures of given individuals, landmarks or objects and in some cases even by letter strings with their names. These results suggest an invariant, sparse and explicit code, which might be important in the transformation of complex visual percepts into long-term and more abstract memories.

Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.

Frequency evolution during tonic-clonic seizures.

By using the Short Time Fourier Transform, we analyzed the EEG frequency evolution during tonic-clonic seizures on 18 scalp recordings corresponding to 7 patients admitted for Video-EEG monitoring. This information was correlated with clinical findings observed in the video recordings. From the time-frequency plots, we recognized patterns related with brain activity even when embedded in a background of muscle artifacts. In 13/18 seizures we found a clear frequency dynamics characterized by an activity originally localized at about 8 Hz, later slowing down to about 1.5 Hz. In the remaining cases muscle artifacts hinder the disclosure of a clear frequency evolution. The clonic phases started when the main frequency slowed down to about 3 Hz. We conclude that the Short Time Fourier Transform is very useful for a quantitative analysis of epileptic seizures, especially when muscle artifacts contaminate the recordings. We further conclude that the clonic phase starts as a response to brain activity that can be only established when brain oscillations are slow enough to be followed by the muscles.