Biomedical subjects

Simon J Thorpe

Publications and source records attributed to Simon J Thorpe.

9 recordsLinked to original sources

PubMed2005

Spike times make sense.

Many behavioral responses are completed too quickly for the underlying sensory processes to rely on estimation of neural firing rates over extended time windows. Theoretically, first-spike times could underlie such rapid responses, but direct evidence has been lacking. Such evidence has now been uncovered in the human somatosensory system. We discuss these findings and their potential generalization to other sensory modalities, and we consider some future challenges for the neuroscientific community.

Action Potentials↗

PubMed2005

The time course of visual processing: backward masking and natural scene categorisation.

Human observers are very good at deciding whether briefly flashed novel images contain an animal and previous work has shown that the underlying visual processing can be performed in under 150 ms. Here we used a masking paradigm to determine how information accumulates over time during such high-level categorisation tasks. As the delay between test image and mask is increased, both behavioural accuracy and differential ERP amplitude rapidly increase to reach asymptotic levels around 40-60 ms. Such results imply that processing at each stage in the visual system is remarkably rapid, with information accumulating almost continuously following the onset of activation.

Adult↗

PubMed2005

Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high-level categorization task using natural images. Human subjects performed a go/no-go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (approximately 70% correct) with only 10-12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was approximately 60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task.

Adult↗

PubMed2005

Neurons tune to the earliest spikes through STDP.

Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.

Action Potentials↗

PubMed2004

How parallel is visual processing in the ventral pathway?

Visual object perception is usually studied by presenting one object at a time at the fovea. However, the world around us is composed of multiple objects. The way our visual system deals with this complexity has remained controversial in the literature. Some models claim that the ventral pathway, a set of visual cortical areas responsible for object recognition, can process only one or very few objects at a time without ambiguity. Other models argue in favor of a massively parallel processing of objects in a scene. Recent experiments in monkeys have provided important data about this issue. The ventral pathway seems to be able to perform complex analyses on several objects simultaneously, but only during a short time period. Subsequently only one or very few objects are explicitly selected and consciously perceived. Here, we survey the implications of these new findings for our understanding of object processing.

Consciousness↗

PubMed2004

Processing of one, two or four natural scenes in humans: the limits of parallelism.

The visual processing of objects in natural scenes is fast and efficient, as indexed by behavioral and ERP data [Nature 381 (1996) 520]. The results from a recent experiment suggested that such fast routines work in parallel across the visual field when subjects were presented with two natural scenes simultaneously [Nature Neurosci. 5 (2002) 629]. In the present experiment, the visual system was driven to its limits by presenting one, two or four scenes simultaneously. Behavior and ERP reveal a clear cost in processing an increasing number of scenes. However, a parallel-late selection model can still account for the results. This model is developed and discussed with reference to behavioral, single-unit and ERP data.

Electroencephalography↗

PubMed2003

SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons.

Many biological neural network models face the problem of scalability because of the limited computational power of today's computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modelled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (< 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.

Action Potentials↗

PubMed2002

Surfing a spike wave down the ventral stream.

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Action Potentials↗

PubMed2002

Parallel processing in high-level categorization of natural images.

Models of visual processing often include an initial parallel stage that is restricted to relatively low-level features, whereas activation of higher-level object descriptions is generally assumed to require attention. Here we report that even high-level object representations can be accessed in parallel: in a rapid animal versus non-animal categorization task, both behavioral and electrophysiological data show that human subjects were as fast at responding to two simultaneously presented natural images as they were to a single one. The implication is that even complex natural images can be processed in parallel without the need for sequential focal attention.

Adult↗