The influence of task requirements on the fast visual processing of natural scenes was studied in... more The influence of task requirements on the fast visual processing of natural scenes was studied in 14 human subjects performing in alternation an ''animal'' categorization task and a single-photograph recognition task. Target photographs were randomly mixed with nontarget images and flashed for only 20 ms. Subjects had to respond to targets within 1 s. Processing time for image-recognition was 30 -40 ms shorter than for the categorization task, both for the fastest behavioral responses and for the latency at which event related potentials evoked by target and non-target stimuli started to diverge. The faster processing in image-recognition is shown to be due to the use of low-level cues, but source analysis produced evidence that, regardless of the task, the dipoles accounting for the differential activity had the same localization and orientation in the occipito-temporal cortex. We suggest that both tasks involve the same visual pathway and the same decisional brain area but because of the total predictability of the target in the image recognition task, the first wave of bottom-up feed-forward information is speeded up by top-down influences that might originate in the prefrontal cortex and preset lower levels of the visual pathway to the known target features. D
In speeded categorization tasks, decisions could be based on diagnostic target features or they m... more In speeded categorization tasks, decisions could be based on diagnostic target features or they may need the activation of complete representations of the object. Depending on task requirements, the priming of feature detectors through top-down expectation might lower the threshold of selective units or speed up the rate of information accumulation. In the present paper, 40 subjects performed a rapid go/no-go animal/non-animal categorization task with 400 briefly flashed natural scenes to study how performance depends on physical scene characteristics, target configuration, and the presence or absence of diagnostic animal features. Performance was evaluated both in terms of accuracy and speed and d ′ curves were plotted as a function of reaction time (RT). Such d ′ curves give an estimation of the processing dynamics for studied features and characteristics over the entire subject population. Global image characteristics such as color and brightness do not critically influence categorization speed, although they slightly influence accuracy. Global critical factors include the presence of a canonical animal posture and animal/background size ratio suggesting the role of coarse global form. Performance was best for both accuracy and speed, when the animal was in a typical posture and when it occupied about 20-30% of the image. The presence of diagnostic animal features was another critical factor. Performance was significantly impaired both in accuracy (drop 3.3-7.5%) and speed (median RT increase 7-16 ms) when diagnostic animal parts (eyes, mouth, and limbs) were missing. Such animal features were shown to influence performance very early when only 15-25% of the response had been produced. In agreement with other experimental and modeling studies, our results support fast diagnostic recognition of animals based on key intermediate features and priming based on the subject's expertise.
Most experimental and theoretical studies of brain function assume that neurons transmit informat... more Most experimental and theoretical studies of brain function assume that neurons transmit information as a rate code, but recent studies on the speed of visual processing impose temporal constraints that appear incompatible with such a coding scheme. Other coding schemes that use the pattern of spikes across a population a neurons may be much more efficient. For example, since strongly activated neurons tend to fire first, one can use the order of firing as a code. We argue that Rank Order Coding is not only very efficient, but also easy to implement in biological hardware: neurons can be made sensitive to the order of activation of their inputs by including a feed-forward shunting inhibition mechanism that progressively desensitizes the neuronal population during a wave of afferent activity. In such a case, maximum activation will only be produced when the afferent inputs are activated in the order of their synaptic weights.
... of meditation effects on the autonomic and immune systems as we improve our understanding of ... more ... of meditation effects on the autonomic and immune systems as we improve our understanding of these systems' relationship and their ... This type of hemispheric activity has also been correlated with both the autonomic system activity and emotional regulation (Craig, 2005), as ...
Recent studies have shown that characteristics of the face contain a wealth of information about ... more Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person's photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 s. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. P...
We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform ... more We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
Three monkeys performed a categorization task and a recognition task with brie£y £ashed natural i... more Three monkeys performed a categorization task and a recognition task with brie£y £ashed natural images, using in alternation either a large variety of familiar target images (animal or food) or a single (totally predictable) target.The processing time was 20 ms shorter in the recognition task in which false alarms showed that monkeys relied on low-level cues (color, form, orientation, etc.). The 20 -ms additional delay necessary in monkeys to perform the categorization task is compared with the 40 -ms delay previously found for humans performing similar tasks. With such short additional processing time, it is argued that neither monkeys nor humans have time to develop a fully integrated object representation in the categorization task and must rely on coarse intermediate representations. NeuroReport 16:349^354 c 2005 Lippincott Williams & Wilkins.
Working memory (WM) is a key executive function for operating aircraft, especially when pilots ha... more Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.
The speed with which neurones in the monkey temporal lobe can respond selectively to the presence... more The speed with which neurones in the monkey temporal lobe can respond selectively to the presence of a face implies that processing may be possible using only one spike per neurone, a finding that is problematic for conventional rate coding models that need at least two spikes to estimate interspike interval. One way of avoiding this problem uses the fact that integrate-and-fire neurones will tend to fire at different times, with the most strongly activated neurones firing first (Thorpe, 1990, Parallel Processing in Neural Systems). Under such conditions, processing can be performed by using the order in which cells in a particular layer fire as a code. To test this idea, we have explored a range of architectures using SpikeNET (Thorpe and Gautrais, 1997, Neural Information Processing Systems, 9), a simulator designed for modelling large populations of integrate-and-fire neurones. One such network used a simple four-layer feed-forward architecture to detect and localise the presence of human faces in natural images. Performance of the model was tested with a large range of grey-scale images of faces and other objects and was found to be remarkably good by comparison with more classic image processing techniques. The most remarkable feature of these results is that they were obtained using a purely feed-forward neural network in which none of the neurones fired more than one spike (thus ruling out conventional rate coding mechanisms). It thus appears that the combination of asynchronous spike propagation and rank order coding may provide an important key to understanding how the nervous system can achieve such a huge amount of processing in so little time.
The speed with which neurones in the monkey temporal lobe can respond selectively to the presence... more The speed with which neurones in the monkey temporal lobe can respond selectively to the presence of a face implies that processing may be possible using only one spike per neurone, a finding that is problematic for conventional rate coding models that need at least two spikes to estimate interspike interval. One way of avoiding this problem uses the fact that integrate-and-fire neurones will tend to fire at different times, with the most strongly activated neurones firing first (Thorpe, 1990, Parallel Processing in Neural Systems). Under such conditions, processing can be performed by using the order in which cells in a particular layer fire as a code. To test this idea, we have explored a range of architectures using SpikeNET (Thorpe and Gautrais, 1997, Neural Information Processing Systems, 9), a simulator designed for modelling large populations of integrate-and-fire neurones. One such network used a simple four-layer feed-forward architecture to detect and localise the presence of human faces in natural images. Performance of the model was tested with a large range of grey-scale images of faces and other objects and was found to be remarkably good by comparison with more classic image processing techniques. The most remarkable feature of these results is that they were obtained using a purely feed-forward neural network in which none of the neurones fired more than one spike (thus ruling out conventional rate coding mechanisms). It thus appears that the combination of asynchronous spike propagation and rank order coding may provide an important key to understanding how the nervous system can achieve such a huge amount of processing in so little time.
In the current study we sought to dissociate the component processes of working memory (WM) (vigi... more In the current study we sought to dissociate the component processes of working memory (WM) (vigilance, encoding and maintenance) that may be differentially impaired in attention-deficit/ hyperactivity disorder (ADHD). We collected electroencephalographic (EEG) data from 52 children with ADHD and 47 typically developing (TD) children, ages 7-14 years, while they performed a spatial Sternberg working memory task. We used independent component analysis and time-frequency analysis to identify midoccipital alpha (8 -12 Hz) to evaluate encoding processes and frontal midline theta (4 -7 Hz) to evaluate maintenance processes. We tested for effects of task difficulty and cue processing to evaluate vigilance. Children with ADHD showed attenuated alpha band event-related desynchronization (ERD) during encoding. This effect was more pronounced when task difficulty was low (consistent with impaired vigilance) and was predictive of memory task performance and symptom severity. Correlated with alpha ERD during encoding were alpha power increases during the maintenance period (relative to baseline), suggesting a compensatory effort. Consistent with this interpretation, midfrontal theta power increases during maintenance were stronger in ADHD and in high-load memory conditions. Furthermore, children with ADHD exhibited a maturational lag in development of posterior alpha power whereas age-related changes in frontal theta power deviated from the TD pattern. Last, subjects with ADHD showed age-independent attenuation of evoked responses to warning cues, suggesting low vigilance. Combined, these three EEG measures predicted diagnosis with 70% accuracy. We conclude that the interplay of impaired vigilance and encoding in ADHD may compromise maintenance and lead to impaired WM performance in this group.
Http Dx Doi Org 10 1162 089892901564234, Mar 13, 2006
& The processing required to decide whether a briefly flashed natural scene contains an animal ca... more & The processing required to decide whether a briefly flashed natural scene contains an animal can be achieved in 150 msec . Here we report that extensive training with a subset of photographs over a 3-week period failed to increase the speed of the processing underlying such rapid visual categorizations: Completely novel scenes could be categorized just as fast as highly familiar ones. Such data imply that the visual system processes new stimuli at a speed and with a number of stages that cannot be compressed. This rapid processing mode was seen with a wide range of visual complex images challenging the idea that short reaction times can only be seen with simple visual stimuli and implying that highly automatic feed-forward mechanisms underlie a far greater proportion of the sophisticated image analysis needed for everyday vision than is generally assumed.
2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000
Recent research has shown that the speed of image processing achieved by the human visual system ... more Recent research has shown that the speed of image processing achieved by the human visual system is incompatible with conventional neural network approaches that use standard coding schemes based on firing rate. An alternative is to use networks of asynchronously firing spiking neurones and use the order of firing across a population of neurones as a code. In this paper we summarize results that demonstrate a number of advantages of such coding schemes: (1) they allow very efficient transmission of information, (2) they are intrinsically invariant to variations in stimulus intensity and contrast, (3) they can be used in very large scale processing architectures to solve difficult problems including categorisation of objects in natural scenes, and (4) they are particularly suited for implementation in low-cost multi-processor hardware.
The influence of task requirements on the fast visual processing of natural scenes was studied in... more The influence of task requirements on the fast visual processing of natural scenes was studied in 14 human subjects performing in alternation an ''animal'' categorization task and a single-photograph recognition task. Target photographs were randomly mixed with nontarget images and flashed for only 20 ms. Subjects had to respond to targets within 1 s. Processing time for image-recognition was 30 -40 ms shorter than for the categorization task, both for the fastest behavioral responses and for the latency at which event related potentials evoked by target and non-target stimuli started to diverge. The faster processing in image-recognition is shown to be due to the use of low-level cues, but source analysis produced evidence that, regardless of the task, the dipoles accounting for the differential activity had the same localization and orientation in the occipito-temporal cortex. We suggest that both tasks involve the same visual pathway and the same decisional brain area but because of the total predictability of the target in the image recognition task, the first wave of bottom-up feed-forward information is speeded up by top-down influences that might originate in the prefrontal cortex and preset lower levels of the visual pathway to the known target features. D
In speeded categorization tasks, decisions could be based on diagnostic target features or they m... more In speeded categorization tasks, decisions could be based on diagnostic target features or they may need the activation of complete representations of the object. Depending on task requirements, the priming of feature detectors through top-down expectation might lower the threshold of selective units or speed up the rate of information accumulation. In the present paper, 40 subjects performed a rapid go/no-go animal/non-animal categorization task with 400 briefly flashed natural scenes to study how performance depends on physical scene characteristics, target configuration, and the presence or absence of diagnostic animal features. Performance was evaluated both in terms of accuracy and speed and d ′ curves were plotted as a function of reaction time (RT). Such d ′ curves give an estimation of the processing dynamics for studied features and characteristics over the entire subject population. Global image characteristics such as color and brightness do not critically influence categorization speed, although they slightly influence accuracy. Global critical factors include the presence of a canonical animal posture and animal/background size ratio suggesting the role of coarse global form. Performance was best for both accuracy and speed, when the animal was in a typical posture and when it occupied about 20-30% of the image. The presence of diagnostic animal features was another critical factor. Performance was significantly impaired both in accuracy (drop 3.3-7.5%) and speed (median RT increase 7-16 ms) when diagnostic animal parts (eyes, mouth, and limbs) were missing. Such animal features were shown to influence performance very early when only 15-25% of the response had been produced. In agreement with other experimental and modeling studies, our results support fast diagnostic recognition of animals based on key intermediate features and priming based on the subject's expertise.
Most experimental and theoretical studies of brain function assume that neurons transmit informat... more Most experimental and theoretical studies of brain function assume that neurons transmit information as a rate code, but recent studies on the speed of visual processing impose temporal constraints that appear incompatible with such a coding scheme. Other coding schemes that use the pattern of spikes across a population a neurons may be much more efficient. For example, since strongly activated neurons tend to fire first, one can use the order of firing as a code. We argue that Rank Order Coding is not only very efficient, but also easy to implement in biological hardware: neurons can be made sensitive to the order of activation of their inputs by including a feed-forward shunting inhibition mechanism that progressively desensitizes the neuronal population during a wave of afferent activity. In such a case, maximum activation will only be produced when the afferent inputs are activated in the order of their synaptic weights.
... of meditation effects on the autonomic and immune systems as we improve our understanding of ... more ... of meditation effects on the autonomic and immune systems as we improve our understanding of these systems' relationship and their ... This type of hemispheric activity has also been correlated with both the autonomic system activity and emotional regulation (Craig, 2005), as ...
Recent studies have shown that characteristics of the face contain a wealth of information about ... more Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person's photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 s. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. P...
We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform ... more We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
Three monkeys performed a categorization task and a recognition task with brie£y £ashed natural i... more Three monkeys performed a categorization task and a recognition task with brie£y £ashed natural images, using in alternation either a large variety of familiar target images (animal or food) or a single (totally predictable) target.The processing time was 20 ms shorter in the recognition task in which false alarms showed that monkeys relied on low-level cues (color, form, orientation, etc.). The 20 -ms additional delay necessary in monkeys to perform the categorization task is compared with the 40 -ms delay previously found for humans performing similar tasks. With such short additional processing time, it is argued that neither monkeys nor humans have time to develop a fully integrated object representation in the categorization task and must rely on coarse intermediate representations. NeuroReport 16:349^354 c 2005 Lippincott Williams & Wilkins.
Working memory (WM) is a key executive function for operating aircraft, especially when pilots ha... more Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.
The speed with which neurones in the monkey temporal lobe can respond selectively to the presence... more The speed with which neurones in the monkey temporal lobe can respond selectively to the presence of a face implies that processing may be possible using only one spike per neurone, a finding that is problematic for conventional rate coding models that need at least two spikes to estimate interspike interval. One way of avoiding this problem uses the fact that integrate-and-fire neurones will tend to fire at different times, with the most strongly activated neurones firing first (Thorpe, 1990, Parallel Processing in Neural Systems). Under such conditions, processing can be performed by using the order in which cells in a particular layer fire as a code. To test this idea, we have explored a range of architectures using SpikeNET (Thorpe and Gautrais, 1997, Neural Information Processing Systems, 9), a simulator designed for modelling large populations of integrate-and-fire neurones. One such network used a simple four-layer feed-forward architecture to detect and localise the presence of human faces in natural images. Performance of the model was tested with a large range of grey-scale images of faces and other objects and was found to be remarkably good by comparison with more classic image processing techniques. The most remarkable feature of these results is that they were obtained using a purely feed-forward neural network in which none of the neurones fired more than one spike (thus ruling out conventional rate coding mechanisms). It thus appears that the combination of asynchronous spike propagation and rank order coding may provide an important key to understanding how the nervous system can achieve such a huge amount of processing in so little time.
The speed with which neurones in the monkey temporal lobe can respond selectively to the presence... more The speed with which neurones in the monkey temporal lobe can respond selectively to the presence of a face implies that processing may be possible using only one spike per neurone, a finding that is problematic for conventional rate coding models that need at least two spikes to estimate interspike interval. One way of avoiding this problem uses the fact that integrate-and-fire neurones will tend to fire at different times, with the most strongly activated neurones firing first (Thorpe, 1990, Parallel Processing in Neural Systems). Under such conditions, processing can be performed by using the order in which cells in a particular layer fire as a code. To test this idea, we have explored a range of architectures using SpikeNET (Thorpe and Gautrais, 1997, Neural Information Processing Systems, 9), a simulator designed for modelling large populations of integrate-and-fire neurones. One such network used a simple four-layer feed-forward architecture to detect and localise the presence of human faces in natural images. Performance of the model was tested with a large range of grey-scale images of faces and other objects and was found to be remarkably good by comparison with more classic image processing techniques. The most remarkable feature of these results is that they were obtained using a purely feed-forward neural network in which none of the neurones fired more than one spike (thus ruling out conventional rate coding mechanisms). It thus appears that the combination of asynchronous spike propagation and rank order coding may provide an important key to understanding how the nervous system can achieve such a huge amount of processing in so little time.
In the current study we sought to dissociate the component processes of working memory (WM) (vigi... more In the current study we sought to dissociate the component processes of working memory (WM) (vigilance, encoding and maintenance) that may be differentially impaired in attention-deficit/ hyperactivity disorder (ADHD). We collected electroencephalographic (EEG) data from 52 children with ADHD and 47 typically developing (TD) children, ages 7-14 years, while they performed a spatial Sternberg working memory task. We used independent component analysis and time-frequency analysis to identify midoccipital alpha (8 -12 Hz) to evaluate encoding processes and frontal midline theta (4 -7 Hz) to evaluate maintenance processes. We tested for effects of task difficulty and cue processing to evaluate vigilance. Children with ADHD showed attenuated alpha band event-related desynchronization (ERD) during encoding. This effect was more pronounced when task difficulty was low (consistent with impaired vigilance) and was predictive of memory task performance and symptom severity. Correlated with alpha ERD during encoding were alpha power increases during the maintenance period (relative to baseline), suggesting a compensatory effort. Consistent with this interpretation, midfrontal theta power increases during maintenance were stronger in ADHD and in high-load memory conditions. Furthermore, children with ADHD exhibited a maturational lag in development of posterior alpha power whereas age-related changes in frontal theta power deviated from the TD pattern. Last, subjects with ADHD showed age-independent attenuation of evoked responses to warning cues, suggesting low vigilance. Combined, these three EEG measures predicted diagnosis with 70% accuracy. We conclude that the interplay of impaired vigilance and encoding in ADHD may compromise maintenance and lead to impaired WM performance in this group.
Http Dx Doi Org 10 1162 089892901564234, Mar 13, 2006
& The processing required to decide whether a briefly flashed natural scene contains an animal ca... more & The processing required to decide whether a briefly flashed natural scene contains an animal can be achieved in 150 msec . Here we report that extensive training with a subset of photographs over a 3-week period failed to increase the speed of the processing underlying such rapid visual categorizations: Completely novel scenes could be categorized just as fast as highly familiar ones. Such data imply that the visual system processes new stimuli at a speed and with a number of stages that cannot be compressed. This rapid processing mode was seen with a wide range of visual complex images challenging the idea that short reaction times can only be seen with simple visual stimuli and implying that highly automatic feed-forward mechanisms underlie a far greater proportion of the sophisticated image analysis needed for everyday vision than is generally assumed.
2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000
Recent research has shown that the speed of image processing achieved by the human visual system ... more Recent research has shown that the speed of image processing achieved by the human visual system is incompatible with conventional neural network approaches that use standard coding schemes based on firing rate. An alternative is to use networks of asynchronously firing spiking neurones and use the order of firing across a population of neurones as a code. In this paper we summarize results that demonstrate a number of advantages of such coding schemes: (1) they allow very efficient transmission of information, (2) they are intrinsically invariant to variations in stimulus intensity and contrast, (3) they can be used in very large scale processing architectures to solve difficult problems including categorisation of objects in natural scenes, and (4) they are particularly suited for implementation in low-cost multi-processor hardware.
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Papers by Arnaud Delorme