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Workshop 2 Abstracts and Lecture Materials: Open Discussion: Author: Wyeth
Bair, Center for Neural Science, NYU Streaming Video: Real
Media The most common use of single microelectrodes is to record unitary
action potentials from single neurons, but a variety of signals
can be recorded from these electrodes, depending on the frequency
band selected and the method of extracting measurements from the
signal. For example, it has recently become popular to record "local
field potentials" (LFPs) from microelectrodes, by using signals
in the EEG band (1-150 Hz). Contributors to the LFP are thought
to include currents associated with local action potentials and,
especially, local synaptic potentials. Simple physical considerations
suggest that signals in these frequency bands may in fact arise
from very wide areas of brain. Moreover, signals in this band that
are synchronized will dominate the LFP, and signals that are temporally
incoherent will go undetected. Interpreting LFP recordings and relating
them to local brain circuits is therefore an uncertain and perilous
undertaking. Recordings of multiunit activity are also possible,
using a somewhat higher frequency band, but their relationship to
local circuit activity is also difficult to determine with certainty.
Author: Dana
Ballard, University of Rochester Recent experimental measurements have suggested an increasing importance
of synchronous pikes in cortical computation. These observations
are difficult ro reconcile with decades of single-cell recordings
that have revealed the correlation of increased firing rate with
beahvioral measures. One possibility is that the cortex has adopted
a signaling strategy that makes extensive use of synchrony for fast
communication, but does it in a way that is consistent with the
rate-code indications. We suggest a way in which this could be done.
Specifically we show how synchronous spike codes on both feed forward
and feedback connections between the Lateral Geniculate Nucleus
(LGN) and cortex can be used to form oriented receptive fields given
natural images as input. The novel features of our spike model is
that it combines synchronous updating of inputs with a probabilistic
signaling strategy. We show that these features allow the reproduction
of synchronicity measured in the LGN as well as classical rate-code
features. Author: Todd
S. Braver, Ph.D, Department of Psychology, Washington University,
http://iac.wustl.edu/~ccpweb One of the hallmarks of human cognition is the ability to flexibly adjust to changing internal states and environmental contingencies by exerting control over thoughts and actions. Progress in understanding the neural mechanisms that mediate this process will require a convergence of empirical studies examining the dynamics of how such neural systems operate during cognitive control and computational analyses aimed at elucidating the formal principles that underlie such operations. We have developed a computational framework that specifies core operations of cognitive control in terms of the interactions of the lateral prefrontal cortex (PFC), anterior cingulate cortex (ACC), and the mesocortical dopamine (DA) neuromodulatory system (Braver and Cohen, 2000; Botvinick et al., 2001; Braver and Cohen, 2001). In this theory, lateral PFC represents and maintains context information that can be used to bias attention and action systems in accordance with internal goals. . The DA system modulates activation in lateral PFC, enabling both flexible updating of representations while insuring robust active maintenance. The ACC detects the presence of conflict in the response system (e.g., co-activation of competing response tendencies), and conveys such information to control systems such as lateral PFC, so that control states can be adjusted to reduce the occurrence of future conflict. An example case is presented in which computational and neuroimaging data were integrated in a convergent manner. In particular, we describe a study of sequential choice responding in which conflict fluctuates on a trial-by-trial basis (Jones et al., in press). We show how our model can fit detailed aspects of this complex dataset and predict trial-by-trial modulation in ACC activity.
Author:Nicholas Brunel Streaming Video: Real Media
Author: Daniel
Bullock, Cognitive & Neural Systems Department, Boston University The basal ganglia and frontal cortex together enable animals to learn and perform conditional responses that acquire rewards when prepotent responses must be suppressed. Dopamine (DA) cells in the substantia nigra pars compacta and adjacent ventral tegmental area learn to selectively respond to unexpected rewards or omissions of expected rewards (Schultz, 1998; modeled in Brown, Bullock & Grossberg, 1999). Such DA cells project widely to the striatum and frontal cortex, and these cells' phasic responses appear to act as reinforcement learning signals. A separate class of basal ganglia outputs "gate" voluntary saccades via inhibitory GABAergic projections to the superior colliculus and motor thalamus (Hikosaka & Wurtz, 1989). Anatomical and physiological studies suggest a highly differentiated pattern of interactions between these basal ganglia outputs and distinct frontal cortical regions and laminae. A new computational theory (Brown, Bullock & Grossberg, 2000) of how the laminar circuitry of the frontal cortex interacts with the basal ganglia, motor thalamus, superior colliculus, and inferotemporal and parietal cortices, offers insights into how these brain regions cooperate to learn and perform conditional behaviors under guidance by DAergic learning signals. The associated neural model, whose frontal cortical component represents the frontal eye fields, describes interactions and dynamics within these circuits with a large system of differential equations. Simulations of the model illustrate how it provides a functional explanation of the dynamics of 17 electrophysiologically identified cell types found in the modeled areas. The model emphasizes striatal feedforward competition among cortically represented plans, and explains how action planning/priming (in cortical layers III-Va and VI) is dissociated from execution (via layer Vb). It also explains how learning enables stimuli to serve as spatial movement targets, as discriminative cues to withhold movement, or as cues to generate remembered actions not directed toward the cue's location. The model integrates neurophysiological, anatomical, and behavioral data from a range of eye movement tasks in primates, including: single, overlap, gap, and memory-guided saccades; and gaze fixation maintained despite distractors.
Author:Carson Chow Streaming Video: Real Media Author:Jonathan Cohen Streaming Video: Real
Media Author: José
Luis Contreras-Vidal, Department of Kinesiology, University
of Maryland In planning visually guided movements, such as pointing and reaching to visual targets, the brain transforms target information in visuo-spatial coordinates into motor commands. The internal model of this visuo-motor transformation needs to be modified in response to altered environments, such as a screen cursor rotation (Teulings et al., 2002; Kagerer et al., 1997). Under such distortions, one must practice to acquire an internal model of the novel environment, which would represent the altered relationship between the cursor movement and the hand/mouse movement. Prior studies suggest that the process of adaptation to a rotational bias depends on the time course of the distortion (Kagerer et al., 1997; Robertson and Miall, 1999). Lesion studies in non-human primates and in clinical populations (cerebellar syndrome and Parkinson's disease) indicate a differential involvement of brain structures in adaptation to gradual as compared to sudden visuo-motor distortions. In this talk, I will describe a neural network model of fronto-parietal, fronto-striatal, and parieto-cerebellar networks thought to be engaged in learning a new internal model of a kinematic distortion. These networks can be differentiated in terms of their learning rules (unsupervised learning, reinforcement learning and supervised learning), the error signals (inferior olive for the cerebellar network, dopamine for the basal ganglia system), the spatio-temporal resolution (high-resolution for cerebellar network, poor resolution for basal ganglia), and the timing of recruitment of these structures during learning. This proposal is consistent with the view that the basal ganglia may be involved in the selection of appropriate movements and/or cognitive strategies based on explicit error signals, whereas the cerebellum may be involved in the recalibration of motor commands through the adjustment and optimization of movement parameters. Thus, functional basal ganglia engagement should be critical in tasks that are initially effortful and in which correct responses are self-selected through trial-and-error mechanisms (Contreras-Vidal and Schultz, 1999). Once the appropriate action has been found and stabilized, the cerebellum can fine-tune the internal model through practice until the task can be performed automatically.
Author: Gustavo
Deco, Siemens We present a neurodynamical model for visual cognition. We assume
that the construction of explicit mechanistic models to gain the
computational aspects of cognitive processes involved in visual
information processing can provide a conceptual framework for establishing
and understanding the underlying basic principles. More specific,
we follow a computational neuroscience approach in order to study
the role of top-down and bottom-up processes by the interaction
of attention and memory in visual object perception. We adopt the
theoretical framework of neurodynamics for integrating known experimental
facts and hypotheses at all neuroscience levels. Neurodynamics offers
a quantitative formulation for describing the dynamical evolution
of single neurons, neural networks and coupled modules of networks.
We explicitly model the feedforward (bottom-up) and feedback (top-down)
interactions between posterior (V1,V2,V4,IT,PP) and anterior (PFC,OFC,BG)
brain areas which are known to build the neural network underlying
processing and coding, modulation and storage of visual information.
The main ingredients of this formulation are based on the theory
of nonlinear dynamical systems and the statistical theory of neural
learning. The model is developed on the basis of a concrete mathematical
description of brain mechanisms involved allowing complete simulation
and prediction of effects of the disruption of sub-mechanisms in
the model. Thus the model can predict specific impairments in visual
information selection, attentional modulation, and visual working
memory capabilities, and their interaction, in patients suffering
from focal brain injury. The simulation experiments are empirically
verified by testing normal subjects and patients. The model integrates,
in a unifying form, the explanation of several existing experimental
data at different neuroscience levels. At the microscopic neural
level, we simulated single cell recordings, at the mesoscopic level
of cortical areas we reproduced the results of fMRI studies, and
at the macroscopic perceptual level psychophysical performances.
Specific predictions at the different neuroscience levels have also
been done. These predictions inspired single cell, fMRI and psychophysical
experiments.
Author: John George,
Los Alamos National Laboratory, Biophysics Studies of information processing by the brain span a range of spatial and temporal scales. Functional processing streams consist of dozens of distinct areas arrayed across much of neocortex, yet network function may depend critically on the activities of individual cells. Responses of the system can last for hundreds of milliseconds, but sub-millisecond timing may be functionally relevant. No single imaging methodology provides all of the information required for studies of the functional organization and dynamics of the brain. However, integrated computational models can exploit the complementary strengths and transcend limitations of individual methods. Model-based analyses of neural electromagnetic data (MEG and EEG) allow inferences about regions and dynamics of neural activation, in spite of the fundamental ambiguity of the inverse problem. Models of tissue geometry and conductivity estimated from MRI or CT and estimates of conductivity from diffusion MRI or impedance tomography can improve the accuracy of the forward calculation required for source localization. Cortical anatomy from MRI provides useful constraints on inverse procedures that attempt tomographic reconstruction of regions of neural activity. We have developed a Bayesian technique for the analysis of MEG/EEG data that provides a powerful framework for probabilistic inference based on multiple forms of anatomical, functional and physiological data. The underlying spatial source model is a patch of contiguous cortical voxels, defined by a series of dilation operations about a seed point on the cortical surface. Cortical voxels have a prior probability for activity, with current oriented normal to the local cortical surface. In the absence of specific prior knowledge, a uniform prior probability is assumed. Alternatively, priors might be assigned based onPET or fMRI data for the individual subject, or drawn from spatial/temporal probabilistic databases based on multiple subjects and functional imaging modalities. Markov Chain Monte Carlo techniques are used to sample the source model parameter space to estimate the posterior probability distribution, allowing identification of consistent features across many possible solutions. The use of a spatio-temporal model of neural activation produces significant gains in the resolution and accuracy of probabilistic mapping. This approach allows integration of multiple, complementary (occasionally disparate) forms of image data on a macroscopic scale. To explore the dynamics activities of networks on a microscopic scale, we have turned to optical techniques. We have recently demonstrated the imaging of rapid intrinsic optical responses that track the electrical dynamics of neuronal activation, using a novel image probe and high performance video techniques. Near infrared illumination is provided around the perimeter of the image probe, providing dark field illumination that enhances contrast of light scattering signals. The image probe can be configured to create confocal, spectrally resolved images of tissue. Stimulation elicits characteristic spatial and temporal optical patterns within brain tissue, corresponding to at least four distinct physical processes. Two early optical components are synchronous with fast electrical evoked responses and reflect direct neural response components not seen in previous imaging studies. Such fast responses may be attributed to a number of biochemical or cellular processes, including neural swelling that accompanies activation. The slower signals reflect the hemodynamic changes that underlie functional imaging techniques such as fMRI. We have employed fast optical signals to visualize expected spatial patterns of physiological activation of rat somato-sensory "barrel" cortex. Optical signals showed evidence of high frequency structure correlated with synchronous oscillatory activity observed in simultaneous electrical recordings. In order to account for dynamic neural behavior, we are developing
simulation tools that allow us to predict experimentally observable
responses of neural populations. We will use these capabilities
to generate testable hypotheses, and to optimize network models
that account for observed responses. The convergence of dynamic
neuroimaging with neural network modeling techniques, may allow
us to understand integrated function of the brain as revealed by
noninvasive macroscopic methods, in terms of the dynamic activities
of networks of individual neurons. Author: Stephen
Grossberg, Department of Cognitive and Neural Systems, Boston
University The processes whereby our brains continue to learn about a changing
world in a stable fashion throughout life are proposed to lead to
conscious experiences. These processes include the learning of top-down
expectations, the matching of these expectations against bottom-up
data, the focusing of attention upon the expected clusters of information,
and the development of resonant states between bottom-up and top-down
processes as they reach a predictive and attentive consensus between
what is expected and what is there in the outside world. It is suggested
that all conscious states in the brain are resonant states, and
that these resonant states trigger learning of sensory and cognitive
representations when they amplify and synchronize distributed neural
signals that are bound by the resonance. The name Adaptive Resonance
Theory, or ART, summarizes the predicted link between resonance
and learning. Illustrative psychophysical and neurobiological data
are explained using these concepts from early vision, visual object
recognition, auditory streaming, and speech perception, among others.
It is noted how these mechanisms seem to be realized by known laminar
circuits of sensory and cognitive neocortex. In particular, they
seem to be operative at all levels of the visual system. It is suggested
that sensory and cognitive processing in the What processing stream
of the brain obey top-down matching and learning laws that are often
complementary to those used for spatial and motor processing in
the brain's Where/How processing stream. This enables sensory and
cognitive representations to maintain their stability as we learn
more about the world, while allowing spatial and motor representations
to forget learned maps and gains that are no longer appropriate
as our bodies develop and grow from infanthood to adulthood. Procedural
memories are proposed to be unconscious because the inhibitory matching
process that supports these spatial and motor processes cannot lead
to resonance. Author: David Hansel, Neurobiology & Biophysics,
University of Freiburg, Germany. Streaming Video: Real
Media Author: Detlef
Heck, Neurobiology & Biophysics, University of Freiburg,
Germany. Streaming Video: Real
Media Neocortical neurons in vivo are embedded in networks with intensive
ongoing activity. How this network activity affects the neurons'
integrative properties and what functional consequences this may
have at the network level remains largely unknown. Most of our knowledge
regarding these properties is based on recordings in vitro, where
network activity is strongly diminished or even absent. We have
performed two complementary series of experiments based on intracellular
recordings in anaesthetized rat frontal cortex measuring (i) the
relationship between the excursions of a neuron's membrane potential
and the level of activity of the surrounding network, (ii) how cortical
neurons integrate synaptic inputs and (III) how integration of synaptic
inputs is affected by ongoing network activity. Based on the results
of these experiments we suggest that network activity strongly affects
the integration time window in cortical neurons increasing the importance
of synchrony during periods of high network activity. I will briefly
introduce a new method which allows us to induce in vivo-like network
activity in acute neocortical brain slices. Author: Philip
Holmes, Program in Applied and Computational Mathematics Princeton
University I will describe joint work with Eric Brown, Jeff Moehlis, Ed Clayton
and Gary Aston-Jones in which we model the response of neurons in
the brain nucleus locus coeruleus (LC) in target detection and selective
attention tasks. Extracellular recordings on behaving monkeys show
varying responses dependent on stimulus type and whether the LC
is in its phasic or tonic mode. From membrane voltage and ion channel
equations, we derive a phase oscillator model for LC neurons. Average
firing probabilities of a pool of cells in response to stimuli over
many trials are then computed via a probability density formulation.
Using this, we show that: (1) Response is elevated in populations
with lower firing rates; (2) Response decays at exponential or faster
rates due to noise and distributions of neuron frequencies; and
(3) Shorter stimuli preferentially cause refractory periods. These
results may account for much of the observed response variability. Author: David
Horn, Tel Aviv University We explore the tendency of a perceptual system to create internal representations of features appearing in the concepts that it learns. This process is empirically demonstrated by requiring subjects to learn a set of new perceptual concepts and to verbally report their components. These reports reflect the internal representations that have been created. A neural network is trained in a similar paradigm, and used to model the mental search task and the verbal report task. The network replicates the empirical results and leads to a prediction that future learning of new concepts will be facilitated if they contain the previously acquired features. This is empirically verified by a second experiment. Author: Barry
Horwitz, National Institute on Deafness and other Communication
Disorder This talk will present an overview to the different functional
brain imaging methods (especially PET and fMRI), to the kinds of
questions these methods try to address, and to some of the questions
associated with functional neuroimaging data for which neural modeling
must be employed to provide reasonable answers. In particular, the
way computational modeling can be used to relate neural activity
to functional brain imaging signals will be addressed. 1. Tagamets, M.A., & Horwitz, B. (1998). Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320. 2. Horwitz, B., Tagamets, M.A., & McIntosh, A.R. (1999). Neural modeling, functional brain imaging and cognition. Trends in Cogn. Sci., 3, 91-98. 3. Horwitz, B., Friston, K.J., & Taylor, J.G. (2000). Neural modeling and functional brain imaging: An overview. Neural Networks, 13, 829-846. Author: Jim
Houk, Northwestern University Additional Materials: PDF1
PDF2 PDF3
PDF4
Widespread areas of the cerebral cortex send input to the striatum of the basal ganglia (BG) and to the cerebellum (CB) through the pons. Improved anatomical methods have revealed quite specific channels of signal processing through BG and CB that feed back to many different areas of the cerebral cortex (Middleton and Strick, 2001). The existence of segregated subcortical channels is calling for a revised interpretation of subcortical signal processing (Houk, 2001). I will discuss two computational models of signal processing in these loops. One demonstrates the suitability of the loop through BG for encoding the serial order of sensory events (Beiser and Houk, 1998). The other explores the suitability of the loop through CB for predictively regulating movement commands. 1. Barto, A. G., Fagg, A. H., Sitkoff, N., & Houk, J. C. (1999). A cerebellar model of timing and prediction in the control of reaching. Neural Computation, 11, 565-594. 2. Beiser, D. G., & Houk, J. C. (1998). Model of cortical-basal ganglionic processing: Encoding the serial order of sensory events. Journal of Neurophysiology, 79, 3168-3188. 3. Houk, J. (2001). Neurophysiology of frontal-subcortical loops. Frontal-subcortical circuits in psychiatry and neurology. D. G. Lichter and J. L. Cummings, eds. New York: Guilford Publications, pp. 92-113. 4. Middleton, F. A., & Strick, P. L. (2001). A revised neuroanatomy
of frontal subcortical circuits. D. G. Lichter and J. L. Cummings,
eds., Frontal-subcortical circuits in psychiatry and neurology.
New York: Guilford Publications. Author: Peter Latham,
UCLA Department of Neurobiology Author: Steve
Lisberger, Howard Hughes Medical Institute Smooth pursuit eye movements use a neural circuit that consists
of multiple cortical and cerebellar areas to allow primates to track
smoothly moving objects. Recordings from many loci within the pursuit
circuits have revealed neurons that discharge in relation to the
image motion that drives pursuit, the eye velocity of pursuit, or
both. However, these observations have not elucidated different
roles for different areas, nor revealed why there are so many different
components to the pursuit circuit. Our goal has been to record pursuit
behavior in a variety of paradigms to reveal the underlying components
of pursuit, and then to use physiological techniques to determine
how each area of the pursuit circuit contributes to the different
components of the behavior. Author: Randy
McIntosh, The Rotman Research Institute The determination of the link between the mental operations considered in cognitive psychology and the biological operations of the brain has been a perpetual challenge for neuroscientists. There is a growing agreement that cognitive function is carried by some combination of localized operations in a brain area and distributed interactions among several regions, though these two notions are often conceived as a dichotomy. However, there remains a difficulty as to the precise mixture of these two notions that exemplifies the brain's translation of biological operations to cognition. I will propose that such a translation may be best appreciated under a principle of Neural Context. Systems-level neuroanatomy shows that most parts of the brain receive projections from many areas and send to projections to many others. Neural context emphasizes that the precise pattern these structural connections are functionally engaged is the translation of brain operations into cognitive operations. Consequently, the same region may show exactly the same pattern of activity across many different tasks, but because the pattern of interactions with other connected areas differs, the region contributes to these different cognitive operations. Stated differently, the neural context within which an area is active embodies the cognitive operation.Background information for these ideas can be found at: http://psych.utoronto.ca/~mcintosh/#Pub Author: Miguel Nicolelis Author: Rajesh
Rao, Dept of Computer Science and Engineering & Neurobiology
and Behavior Program University of Washington, Seattle A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this talk, we discuss how a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary Markov model. We illustrate the suggested approach using a visual motion detection task. Our simulation results show that the model network exhibits direction selectivity and correctly computes the posterior probabilities for motion direction. When used to solve the well-known random dots motion discrimination task, the model generates responses that mimic the activities of evidence-accumulating neurons in cortical areas LIP and FEF. In addition, the model predicts reaction time distributions that are similar to those obtained in human psychophysical experiments that manipulate the prior probabilities of targets and task urgency. Author: John
Rinzel , Center for Neural Science and Courant Institute of
Mathematical Sciences, New York University Many developing circuits show spontaneous oscillations. We study models for the slow episodic population rhythms (time scale, mins) that are seen in chick embryonic spinal cord. We use firing rate models for the population activity in a recurrent network of excitatory-coupled cells. Fast/slow methods are used to analyze the models. The primary candidate for the slow negative feedback mechanism that sets the burst period is a form of synaptic depression. The individual units have simple tonic firing properties. Specific predictions based on the model about how the rhythm is affected due to brief stimuli that switch the system from the quiescent to the active phase have now been confirmed in experiments. A positive correlation was found between episode duration and the preceding inter-episode interval, but not with the following interval, suggesting that episode onset is stochastic while episode termination occurs deterministically, when network excitability falls to a fixed level. We also formulate and analyze a minimal model that demonstrates the plausibility of a specific mechanism for depression: the slow modulation of the synaptic reversal potential (for the GABA synapses, which are depolarizing at this stage of development). Preliminary results show that a cell-based network (integrate-and-fire units) with synaptic depression can also alternate between phases of active firing and quiescence. (with J Tabak, M O'Donovan, B Vladimirski) 1. Tabak, J., Senn, W., O'Donovan, M.J., & Rinzel, J. (2000). Modeling of spontaneous activity in developing spinal cord using activity-dependent depression in an excitatory network. J Neuroscience, 20, 3041-3056. 2. Tabak, J., Rinzel, J., & O'Donovan, M.J. (2001). The role
of activity-dependent network depression in the expression and self-regulation
of spontaneous activity in the developing spinal cord. J Neuroscience,
21, 8966-8976.
Author: Martin
Stetter, Siemens AG, CT IC 4 Our visual system represents an important and complex part of the
brain, and is thought to contribute already to precognitive and
cognitive processes including attention, working memory and conflict
handling. Here Iconcentrate on early visual processing in V1 of
higher mammals and use this reduced system as an example for modeling
different putatively intraareal network effects on multiple scales
in space. Author: Malle
Tagamets, University of Maryland Although functional human neuroimaging methods such as positron
emission tomography (PET) and functional magnetic resonance imaging
(fMRI) have become increasingly important in the study of human
brain function, there is still a large gap between the imaging results
and underlying neuronal processes such as those described in single-cell
animal studies. While the single-cell studies examine the spiking
activity of single or relatively few neurons, usually excitatory
pyramidal cells, imaging data reflects the integrated synaptic activity
of large populations of mixed cortical cell types. A few efforts
have been made to bridge the gap between these vastly different
scales by the use of large-scale modeling (Arbib et al., 1995; Tagamets
& Horwitz, 1998). Some of the factors and constraints that need
to be considered in building such a model are discussed. An emphasis
is placed on features of the circuits in the brain that are likely
to have an effect on the relationship between imaging data and the
underlying neuronal activity. In particular, it is demonstrated
how the interaction of the relatively sparse inter-regional connections
and rich local circuitry may have unintuitive effects on imaging
data, especially in the presence of synaptic inhibition or neuromodulation.
1. Arbib, M. A., Bischoff, A., Fagg, A. H., & Grafton, S. T.
(1995). Synthetic PET: Analyzing large-scale properties of neural
networks. Human Brain Mapping, 2, 225-233. Author: Dave
Terman , Mathematical Biosciences Institute Author: Marius
Usher, Institute of Cognitive Neuroscience The talk will present a neurocomputational framework to account
for choice-RT and maintenance in working memory, as measured in
tasks such as speeded choice and free/cued recall of list of items.
The model makes use of a stochastic accumulation process, where
neural leak, recurrent excitation and lateral inhibition play important
roles (Usher & McClelland, 2001; Haarmann & Usher, 2001;
Davelaar & Usher, 2002a). Variations in these parameters account
for individual differences and online changes of the parameters
take place, in a task dependent way, in response to attentional
processes mediated by neuromodulation (Usher & Davelaar, 2002b). Author: XJ Wang , Brandeis University |
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