CiteULike: awooga's model

Synaptic Theory of Working Memory

Gianluigi Mongillo — 2008-03-14T12:03:46-00:00

Science, Vol. 319, No. 5869. (14 March 2008), pp. 1543-1546.

It is usually assumed that enhanced spiking activity in the form of persistent reverberation for several seconds is the neural correlate of working memory. Here, we propose that working memory is sustained by calcium-mediated synaptic facilitation in the recurrent connections of neocortical networks. In this account, the presynaptic residual calcium is used as a buffer that is loaded, refreshed, and read out by spiking activity. Because of the long time constants of calcium kinetics, the refresh rate can be low, resulting in a mechanism that is metabolically efficient and robust. The duration and stability of working memory can be regulated by modulating the spontaneous activity in the network. 10.1126/science.1150769

Spike timing, calcium signals and synaptic plasticity

Per Sjostrom — 2008-03-19T11:19:32-00:00

Current Opinion in Neurobiology, Vol. 12, No. 3. (1 June 2002), pp. 305-314.

Plasticity at central synapses depends critically on the timing of presynaptic and postsynaptic action potentials. Key initial steps in synaptic plasticity involve the back-propagation of action potentials into the dendritic tree and calcium influx that depends nonlinearly on the action potential and synaptic input. These initial steps are now better understood. In addition, recent studies of processes as diverse as gene expression and channel inactivation suggest that responses to calcium transients depend not only their amplitude, but on their time course and on the location of their origin.

Effects of Dopaminergic Modulation on the Integrative Properties of the Ventral Striatal Medium Spiny Neuron

Jason Moyer — 2008-03-04T03:40:49-00:00

J Neurophysiol, Vol. 98, No. 6. (1 December 2007), pp. 3731-3748.

Dopaminergic modulation produces a variety of functional changes in the principal cell of the striatum, the medium spiny neuron (MSN). Using a 189-compartment computational model of a ventral striatal MSN, we simulated whole cell D1- and D2-receptormediated modulation of both intrinsic (sodium, calcium, and potassium) and synaptic currents (AMPA and NMDA). Dopamine (DA) modulations in the model were based on a review of published experiments in both ventral and dorsal striatum. To objectively assess the net effects of DA modulation, we combined reported individual channel modulations into either D1- or D2-receptor modulation conditions and studied them separately. Contrary to previous suggestions, we found that D1 modulation had no effect on MSN nonlinearity and could not induce bistability. In agreement with previous suggestions, we found that dopaminergic modulation leads to changes in input filtering and neuronal excitability. Importantly, the changes in neuronal excitability agree with the classical model of basal ganglia function. We also found that DA modulation can alter the integration time window of the MSN. Interestingly, the effects of DA modulation of synaptic properties opposed the effects of DA modulation of intrinsic properties, with the synaptic modulations generally dominating the net effect. We interpret this lack of synergy to suggest that the regulation of whole cell integrative properties is not the primary functional purpose of DA. We suggest that D1 modulation might instead primarily regulate calcium influx to dendritic spines through NMDA and L-type calcium channels, by both direct and indirect mechanisms. 10.1152/jn.00335.2007

Serotonin and the Evaluation of Future Rewards: Theory, Experiments, and Possible Neural Mechanisms

Nicolas Schweighofer — 2007-06-29T02:35:43-00:00

Annals of the New York Academy of Sciences, Vol. 1104, No. 1. (May 2007), pp. 289-300.

The computational neurobiology of learning and reward

Nathaniel Daw — 2007-04-19T16:11:56-00:00

Current Opinion in Neurobiology, Vol. 16, No. 2. (April 2006), pp. 199-204.

Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a `reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.

Serotonin, Inhibition, and Negative Mood

Peter Dayan — 2008-02-28T15:48:33-00:00

PLoS Computational Biology, Vol. 4, No. 2. (1 February 2008), e4.

Pavlovian predictions of future aversive outcomes lead to behavioral inhibition, suppression, and withdrawal. There is considerable evidence for the involvement of serotonin in both the learning of these predictions and the inhibitory consequences that ensue, although less for a causal relationship between the two. In the context of a highly simplified model of chains of affectively charged thoughts, we interpret the combined effects of serotonin in terms of pruning a tree of possible decisions, (i.e., eliminating those choices that have low or negative expected outcomes). We show how a drop in behavioral inhibition, putatively resulting from an experimentally or psychiatrically influenced drop in serotonin, could result in unexpectedly large negative prediction errors and a significant aversive shift in reinforcement statistics. We suggest an interpretation of this finding that helps dissolve the apparent contradiction between the fact that inhibition of serotonin reuptake is the first-line treatment of depression, although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions.

Neural mechanisms of selective visual attention.

R Desimone — 2006-07-25T19:54:27-00:00

Annu Rev Neurosci, Vol. 18 (1995), pp. 193-222.

On the computational architecture of the neocortex

D Mumford — 2007-10-04T15:33:20-00:00

Biological Cybernetics, Vol. 66, No. 3. (7 January 1992), pp. 241-251.

This paper is a sequel to an earlier paper which proposed an active role for the thalamus, integrating multiple hypotheses formed in the cortex via the thalamo-cortical loop. In this paper, I put forward a hypothesis on the role of the reciprocal, topographic pathways between two cortical areas, one often a ‘higher’ area dealing with more abstract information about the world, the other ‘lower’, dealing with more concrete data. The higher area attempts to fit its abstractions to the data it receives from lower areas by sending back to them from its deep pyramidal cells a template reconstruction best fitting the lower level view. The lower area attempts to reconcile the reconstruction of its view that it receives from higher areas with what it knows, sending back from its superficial pyramidal cells the features in its data which are not predicted by the higher area. The whole calculation is done with all areas working simultaneously, but with order imposed by synchronous activity in the various top-down, bottom-up loops. Evidence for this theory is reviewed and experimental tests are proposed. A third part of this paper will deal with extensions of these ideas to the frontal lobe.

Synergetics: An Introduction. Nonequilibrium Phase Transitions and Self- Organization in Physics, Chemistry and Biology (Springer Series in Synergetics)

H Haken — 2007-10-04T15:27:46-00:00

(08 November 1978)

This book is an often-requested reprint of two classic texts by H. Haken: "Synergetics. An Introduction" and "Advanced Synergetics". Synergetics, an interdisciplinary research program initiated by H. Haken in 1969, deals with the systematic and methodological approach to the rapidly growing field of complexity. Going well beyond qualitative analogies between complex systems in fields as diverse as physics, chemistry, biology, sociology and economics, Synergetics uses tools from theoretical physics and mathematics to construct an unifying framework within which quantitative descriptions of complex, self-organizing systems can be made. This may well explain the timelessness of H. Haken's original texts on this topic, which are now recognized as landmarks in the field of complex systems. They provide both the beginning graduate student and the seasoned researcher with solid knowledge of the basic concepts and mathematical tools. Moreover, they admirably convey the spirit of the pioneering work by the founder of Synergetics through the essential applications contained herein that have lost nothing of their paradigmatic character since they were conceived.

Adaptive Resonance Theory

G Carpenter — 2007-10-04T15:18:21-00:00

INTRODUCTION Principles derived from an analysis of experimental literatures in vision, speech, cortical development, and reinforcement learning, including attentional blocking and cognitive-emotional interactions, led to the introduction of adaptive resonance as a theory of human cognitive information processing (Grossberg, 1976). The theory has evolved as a series of real-time neural network models that perform unsupervised and supervised learning, pattern recognition, and prediction (Duda,...

An Integrated Microcircuit Model of Attentional Processing in the Neocortex

Salva Ardid — 2007-08-20T01:34:36-00:00

J. Neurosci., Vol. 27, No. 32. (8 August 2007), pp. 8486-8495.

Selective attention is a fundamental cognitive function that uses top-down signals to orient and prioritize information processing in the brain. Single-cell recordings from behaving monkeys have revealed a number of attention-induced effects on sensory neurons, and have given rise to contrasting viewpoints about the neural underpinning of attentive processing. Moreover, there is evidence that attentional signals originate from the prefrontoparietal working memory network, but precisely how a source area of attention interacts with a sensory system remains unclear. To address these questions, we investigated a biophysically based network model of spiking neurons composed of a reciprocally connected loop of two (sensory and working memory) networks. We found that a wide variety of physiological phenomena induced by selective attention arise naturally in such a system. In particular, our work demonstrates a neural circuit that instantiates the "feature-similarity gain modulation principle," according to which the attentional gain effect on sensory neuronal responses is a graded function of the difference between the attended feature and the preferred feature of the neuron, independent of the stimulus. Furthermore, our model identifies key circuit mechanisms that underlie feature-similarity gain modulation, multiplicative scaling of tuning curve, and biased competition, and provide specific testable predictions. These results offer a synthetic account of the diverse attentional effects, suggesting a canonical neural circuit for feature-based attentional processing in the cortex. 10.1523/JNEUROSCI.1145-07.2007

Transient Calcium and Dopamine Increase PKA Activity and DARPP-32 Phosphorylation

Maria Lindskog — 2007-08-24T16:10:11-00:00

PLoS Computational Biology, Vol. 2, No. 9. (1 September 2006), e119.

Reinforcement learning theorizes that strengthening of synaptic connections in medium spiny neurons of the striatum occurs when glutamatergic input (from cortex) and dopaminergic input (from substantia nigra) are received simultaneously. Subsequent to learning, medium spiny neurons with strengthened synapses are more likely to fire in response to cortical input alone. This synaptic plasticity is produced by phosphorylation of AMPA receptors, caused by phosphorylation of various signalling molecules. A key signalling molecule is the phosphoprotein DARPP-32, highly expressed in striatal medium spiny neurons. DARPP-32 is regulated by several neurotransmitters through a complex network of intracellular signalling pathways involving cAMP (increased through dopamine stimulation) and calcium (increased through glutamate stimulation). Since DARPP-32 controls several kinases and phosphatases involved in striatal synaptic plasticity, understanding the interactions between cAMP and calcium, in particular the effect of transient stimuli on DARPP-32 phosphorylation, has major implications for understanding reinforcement learning. We developed a computer model of the biochemical reaction pathways involved in the phosphorylation of DARPP-32 on Thr34 and Thr75. Ordinary differential equations describing the biochemical reactions were implemented in a single compartment model using the software XPPAUT. Reaction rate constants were obtained from the biochemical literature. The first set of simulations using sustained elevations of dopamine and calcium produced phosphorylation levels of DARPP-32 similar to that measured experimentally, thereby validating the model. The second set of simulations, using the validated model, showed that transient dopamine elevations increased the phosphorylation of Thr34 as expected, but transient calcium elevations also increased the phosphorylation of Thr34, contrary to what is believed. When transient calcium and dopamine stimuli were paired, PKA activation and Thr34 phosphorylation increased compared with dopamine alone. This result, which is robust to variation in model parameters, supports reinforcement learning theories in which activity-dependent long-term synaptic plasticity requires paired glutamate and dopamine inputs.

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience)

Eugene Izhikevich — 2007-06-18T12:16:32-00:00

(01 November 2006)

In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum--or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Dopamine D1 receptor actions in layers V-VI rat prefrontal cortex neurons in vitro: modulation of dendritic-somatic signal integration.

CR Yang — 2007-05-03T09:33:40-00:00

J Neurosci, Vol. 16, No. 5. (1 March 1996), pp. 1922-1935.

The ionic mechanisms by which dopamine (DA) regulates the excitability of layers V-VI prefrontal cortex (PFC) output neurons (including those that project to the nucleus accumbens) were investigated in rat brain slices using in vitro intracellular recording techniques. DA or the D1 receptor agonist SKF38393, but not the D2 agonist quinpirole, reduced the first spike latency and lowered the firing threshold of the PFC neurons in response to depolarizing current pulses. This was accomplished by enhancing the duration of a tetradotoxinsensitive, slowly inactivating Na+ current and attenuating a slowly inactivating, outwardly rectifying, dendrotoxin-sensitive K+ current. Furthermore, D1 receptor stimulation attenuated high-threshold Ca2+ spike(s) (HTS) evoked primarily from the apical dendrites of these PFC neurons. Taken together, these data suggest that D1 receptor stimulation on layers V-VI pyramidal PFC neurons: (1) restricts the effects of inputs to the apical dendrites of these neurons by attenuating the dendritic HTS-mediated amplification of such inputs; and (2) potentiates the influence of local inputs from neighboring deep layers V-VI neurons by enhancing the slowly inactivating Na+ current and attenuating the slowly inactivating K+ current. By influencing the input/output characteristics of PFC-->NAc neurons, DA may play an important role in the processes through which PFC signals are translated into action.

A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex.

JA Varela — 2007-04-30T14:00:39-00:00

J Neurosci, Vol. 17, No. 20. (15 October 1997), pp. 7926-7940.

Cortical synapses exhibit several forms of short-term plasticity, but the contribution of this plasticity to visual response dynamics is unknown. In part, this is because the simple patterns of stimulation used to probe plasticity in vitro do not correspond to patterns of activity that occur in vivo. We have developed a method of quantitatively characterizing short-term plasticity at cortical synapses that permits prediction of responses to arbitrary patterns of stimulation. Synaptic responses were recorded intracellularly as EPSCs and extracellularly as local field potentials in layer 2/3 of rat primary visual cortical slices during stimulation of layer 4 with trains of electrical stimuli containing random mixtures of frequencies. Responses exhibited complex dynamics that were well described by a simple three-component model consisting of facilitation and two forms of depression, a stronger form that decayed exponentially with a time constant of several hundred milliseconds and a weaker, but more persistent, form that decayed with a time constant of several seconds. Parameters obtained from fits to one train were used to predict accurately responses to other random and constant frequency trains. Control experiments revealed that depression was not caused by a decrease in the effectiveness of extracellular stimulation or by a buildup of inhibition. Pharmacological manipulations of transmitter release and postsynaptic sensitivity suggested that both forms of depression are mediated presynaptically. These results indicate that firing evoked by visual stimuli is likely to cause significant depression at cortical synapses. Hence synaptic depression may be an important determinant of the temporal features of visual cortical responses.

Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex.

CM Hempel — 2007-04-30T13:54:48-00:00

J Neurophysiol, Vol. 83, No. 5. (May 2000), pp. 3031-3041.

Short-term synaptic plasticity, in particular short-term depression and facilitation, strongly influences neuronal activity in cerebral cortical circuits. We investigated short-term plasticity at excitatory synapses onto layer V pyramidal cells in the rat medial prefrontal cortex, a region whose synaptic dynamic properties have not been systematically examined. Using intracellular and extracellular recordings of synaptic responses evoked by stimulation in layers II/III in vitro, we found that short-term depression and short-term facilitation are similar to those described previously in other regions of the cortex. In addition, synapses in the prefrontal cortex prominently express augmentation, a longer lasting form of short-term synaptic enhancement. This consists of a 40-60% enhancement of synaptic transmission which lasts seconds to minutes and which can be induced by stimulus trains of moderate duration and frequency. Synapses onto layer III neurons in the primary visual cortex express substantially less augmentation, indicating that this is a synapse-specific property. Intracellular recordings from connected pairs of layer V pyramidal cells in the prefrontal cortex suggest that augmentation is a property of individual synapses that does not require activation of multiple synaptic inputs or neuromodulatory fibers. We propose that synaptic augmentation could function to enhance the ability of a neuronal circuit to sustain persistent activity after a transient stimulus. This idea is explored using a computer simulation of a simplified recurrent cortical network.

Neural networks with dynamic synapses.

M Tsodyks — 2007-04-30T13:50:23-00:00

Neural Comput, Vol. 10, No. 4. (15 May 1998), pp. 821-835.

Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze different regimes of synaptic transmission and demonstrate that dynamic synapses transmit different aspects of the presynaptic activity depending on the average presynaptic frequency. The model also allows for derivation of mean-field equations, which govern the activity of large, interconnected networks. We show that the dynamics of synaptic transmission results in complex sets of regular and irregular regimes of network activity.

Model for a robust neural integrator.

AA Koulakov — 2006-10-06T11:04:58-00:00

Nat Neurosci, Vol. 5, No. 8. (August 2002), pp. 775-782.

Integrator circuits in the brain show persistent firing that reflects the sum of previous excitatory and inhibitory inputs from external sources. Integrator circuits have been implicated in parametric working memory, decision making and motor control. Previous work has shown that stable integrator function can be achieved by an excitatory recurrent neural circuit, provided synaptic strengths are tuned with extreme precision (better than 1% accuracy). Here we show that integrator circuits can function without fine tuning if the neuronal units have bistable properties. Two specific mechanisms of bistability are analyzed, one based on local recurrent excitation, and the other on the voltage-dependence of the NMDA (N-methyl-D-aspartate) channel. Neither circuit requires fine tuning to perform robust integration, and the latter actually exploits the variability of neuronal conductances.

Stability of the memory of eye position in a recurrent network of conductance-based model neurons.

HS Seung — 2007-04-05T11:03:04-00:00

Neuron, Vol. 26, No. 1. (April 2000), pp. 259-271.

Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic manner: below a threshold position the neuron is silent, and above it the firing rate is linearly related to position. Both the threshold and linear slope vary from neuron to neuron. We have reproduced this behavior in a biophysically plausible network model. Persistence depends on precise tuning of the strength of synaptic feedback, and a relatively long synaptic time constant improves the robustness to mistuning.

Bistability with Hysteresis in the Activity of Vasopressin Cells

Sabatier — 2007-01-08T06:33:38-00:00

Journal of Neuroendocrinology, Vol. 19, No. 2. (February 2007), pp. 95-101.

Canonical Neural Models

Frank Hoppensteadt — 2007-03-21T14:01:10-00:00

(2001)

The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics.

Robert C Cannon — 2006-09-18T09:33:38-00:00

PLoS Comput Biol, Vol. 2, No. 8. (25 August 2006)

Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. Sophisticated statistical and computational tools have been developed to study the ion channel structure-function relationship, but this work is rarely incorporated into the models used for single neurons or small networks. The disjunction is partly a matter of convention. Structure-function studies typically use a single Markov model for the whole channel whereas until recently whole-cell modeling software has focused on serial, independent, two-state subunits that can be represented by the Hodgkin-Huxley equations. More fundamentally, there is a difference in purpose that prevents models being easily reused. Biophysical models are typically developed to study one particular aspect of channel gating in detail, whereas neural modelers require broad coverage of the entire range of channel behavior that is often best achieved with approximate representations that omit structural features that cannot be adequately constrained. To bridge the gap so that more recent channel data can be used in neural models requires new computational infrastructure for bringing together diverse sources of data to arrive at best-fit models for whole-cell modeling. We review the current state of channel modeling and explore the developments needed for its conclusions to be integrated into whole-cell modeling.

Transient high-frequency firing in a coupled-oscillator model of the mesencephalic dopaminergic neuron.

AS Kuznetsov — 2007-02-20T16:11:05-00:00

J Neurophysiol, Vol. 95, No. 2. (February 2006), pp. 932-947.

Dopaminergic neurons of the midbrain fire spontaneously at rates <10/s and ordinarily will not exceed this range even when driven with somatic current injection. When driven at higher rates, these cells undergo spike failure through depolarization block. During spontaneous bursting of dopaminergic neurons in vivo, bursts related to reward expectation in behaving animals, and bursts generated by dendritic application of N-methyl-d-aspartate (NMDA) agonists, transient firing attains rates well above this range. We suggest a way such high-frequency firing may occur in response to dendritic NMDA receptor activation. We have extended the coupled oscillator model of the dopaminergic neuron, which represents the soma and dendrites as electrically coupled compartments with different natural spiking frequencies, by addition of dendritic AMPA (voltage-independent) or NMDA (voltage-dependent) synaptic conductance. Both soma and dendrites contain a simplified version of the calcium-potassium mechanism known to be the mechanism for slow spontaneous oscillation and background firing in dopaminergic cells. The compartments differ only in diameter, and this difference is responsible for the difference in natural frequencies. We show that because of its voltage dependence, NMDA receptor activation acts to amplify the effect on the soma of the high-frequency oscillation of the dendrites, which is normally too weak to exert a large influence on the overall oscillation frequency of the neuron. During the high-frequency oscillations that result, sodium inactivation in the soma is removed rapidly after each action potential by the hyperpolarizing influence of the dendritic calcium-dependent potassium current, preventing depolarization block of the spike mechanism, and allowing high-frequency spiking.

Electrical coupling between model midbrain dopamine neurons: effects on firing pattern and synchrony.

AO Komendantov — 2007-02-20T16:10:15-00:00

J Neurophysiol, Vol. 87, No. 3. (March 2002), pp. 1526-1541.

The role of gap junctions between midbrain dopamine (DA) neurons in mechanisms of firing pattern generation and synchronization has not been well characterized experimentally. We modified a multi-compartment model of DA neuron by adding a spike-generating mechanism and electrically coupling the dendrites of two such neurons through gap junctions. The burst-generating mechanism in the model neuron results from the interaction of a N-methyl-D-aspartate (NMDA)-induced current and the sodium pump. The firing patterns exhibited by the two model neurons included low frequency (2-7 Hz) spiking, high-frequency (13-20 Hz) spiking, irregular spiking, regular bursting, irregular bursting, and leader/follower bursting, depending on the parameter values used for the permeability for NMDA-induced current and the conductance for electrical coupling. All of these firing patterns have been observed in physiological neurons, but a systematic dependence of the firing pattern on the covariation of these two parameters has not been established experimentally. Our simulations indicate that electrical coupling facilitates NMDA-induced burst firing via two mechanisms. The first can be observed in a pair of identical cells. At low frequencies (low NMDA), as coupling strength was increased, only a transition from asynchronous to synchronous single-spike firing was observed. At high frequencies (high NMDA), increasing the strength of the electrical coupling in an identical pair resulted in a transition from high-frequency single-spike firing to burst firing, and further increases led to synchronous high-frequency spiking. Weak electrical coupling destabilizes the synchronous solution of the fast spiking subsystems, and in the presence of a slowly varying sodium concentration, the desynchronized spiking solution leads to bursts that are approximately in phase with spikes that are not in phase. Thus this transitional mechanism depends critically on action potential dynamics. The second mechanism for the induction of burst firing requires a heterogeneous pair that is, respectively, too depolarized and too hyperpolarized to burst. The net effect of the coupling is to bias at least one cell into an endogenously burst firing regime. In this case, action potential dynamics are not critical to the transitional mechanism. If electrical coupling is indeed more prominent in vivo due to basal level of modulation of gap junctions in vivo, these results may indicate why NMDA-induced burst firing is easier to observe in vivo as compared in vitro.

Coupled oscillator model of the dopaminergic neuron of the substantia nigra.

CJ Wilson — 2007-02-20T16:05:51-00:00

J Neurophysiol, Vol. 83, No. 5. (May 2000), pp. 3084-3100.

Calcium imaging using fura-2 and whole cell recording revealed the effective location of the oscillator mechanism on dopaminergic neurons of the substantia nigra, pars compacta, in slices from rats aged 15-20 days. As previously reported, dopaminergic neurons fired in a slow rhythmic single spiking pattern. The underlying membrane potential oscillation survived blockade of sodium currents with TTX and was enhanced by blockade of voltage-sensitive potassium currents with TEA. Calcium levels increased during the subthreshold depolarizing phase of the membrane potential oscillation and peaked at the onset of the hyperpolarizing phase as expected if the pacemaker potential were due to a low-threshold calcium current and the hyperpolarizing phase to calcium-dependent potassium current. Calcium oscillations were synchronous in the dendrites and soma and were greater in the dendrites than in the soma. Average calcium levels in the dendrites overshot steady-state levels and decayed over the course of seconds after the oscillation was resumed after having been halted by hyperpolarizing currents. Average calcium levels in the soma increased slowly, taking many cycles to achieve steady state. Voltage clamp with calcium imaging revealed the voltage dependence of the somatic calcium current without the artifacts of incomplete spatial voltage control. This showed that the calcium current had little or no inactivation and was half-maximal at -40 to -30 mV. The time constant of calcium removal was measured by the return of calcium to resting levels and depended on diameter. The calcium sensitivity of the calcium-dependent potassium current was estimated by plotting the slow tail current against calcium concentration during the decay of calcium to resting levels at -60 mV. A single compartment model of the dopaminergic neuron consisting of a noninactivating low-threshold calcium current, a calcium-dependent potassium current, and a small leak current reproduced most features of the membrane potential oscillations. The same currents much more accurately reproduced the calcium transients when distributed uniformly along a tapering cable in a multicompartment model. This model represented the dopaminergic neuron as a set of electrically coupled oscillators with different natural frequencies. Each frequency was determined by the surface area to volume ratio of the compartment. This model could account for additional features of the dopaminergic neurons seen in slices, such as slow adaptation of oscillation frequency and may produce irregular firing under different coupling conditions.

Modeling functions of striatal dopamine modulation in learning and planning.

RE Suri — 2007-02-14T11:47:59-00:00

Neuroscience, Vol. 103, No. 1. (2001), pp. 65-85.

The activity of midbrain dopamine neurons is strikingly similar to the reward prediction error of temporal difference reinforcement learning models. Experimental evidence and simulation studies suggest that dopamine neuron activity serves as an effective reinforcement signal for learning of sensorimotor associations in striatal matrisomes. In the current study, we simulate dopamine neuron activity with the extended temporal difference model of Pavlovian learning and examine the influences of this signal on medium spiny neurons in striatal matrisomes. The modeled influences include transient membrane effects of dopamine D(1) receptor activation, dopamine-dependent long-term adaptations of corticostriatal transmission, and effects of dopamine on rhythmic fluctuations of the membrane potential between an elevated "up-state" and a hyperpolarized "down-state". The most dominant activity in the striatal matrisomes is assumed to elicit behaviors via projections from the basal ganglia to the thalamus and the cortex. This "standard model" performs successfully when tested for sensorimotor learning and goal-directed behavior (planning). To investigate the contributions of our model assumptions to learning and planning, we test the performance of several model variants that lack one of these mechanisms. These simulations show that the adaptation of the dopamine-like signal is necessary for sensorimotor learning and planning. Sensorimotor learning requires dopamine-dependent long-term adaptation of corticostriatal transmission. Lack of dopamine-like novelty responses decreases the number of exploratory acts, which impairs planning capabilities. The model loses its planning capabilities if the dopamine-like signal is simulated with the original temporal difference model, because the original temporal difference model does not form novel associative chains. Transient membrane effects of the dopamine-like signal on striatal firing substantially shorten the reaction time in the planning task. The capability for planning is improved by influences of dopamine on the durations of membrane potential fluctuations and by manipulations that prolong the reaction time of the model. These results suggest that responses of dopamine neurons to conditioned stimuli contribute to sensorimotor reward learning, novelty responses of dopamine neurons stimulate exploration, and transient dopamine membrane effects are important for planning.

Dynamic Gain Control of Dopamine Delivery in Freely Moving Animals

Read Montague — 2007-01-29T14:07:34-00:00

J. Neurosci., Vol. 24, No. 7. (18 February 2004), pp. 1754-1759.

Activity changes in a large subset of midbrain dopamine neurons fulfill numerous assumptions of learning theory by encoding a prediction error between actual and predicted reward. This computational interpretation of dopaminergic spike activity invites the important question of how changes in spike rate are translated into changes in dopamine delivery at target neural structures. Using electrochemical detection of rapid dopamine release in the striatum of freely moving rats, we established that a single dynamic model can capture all the measured fluctuations in dopamine delivery. This model revealed three independent short-term adaptive processes acting to control dopamine release. These short-term components generalized well across animals and stimulation patterns and were preserved under anesthesia. The model has implications for the dynamic filtering interposed between changes in spike production and forebrain dopamine release. 10.1523/JNEUROSCI.4279-03.2004

Reward, motivation, and reinforcement learning.

P Dayan — 2006-03-10T14:10:15-00:00

Neuron, Vol. 36, No. 2. (10 October 2002), pp. 285-298.

There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concerning the impact of motivational manipulations, show that these models are unreasonable. We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role.

Representation and Timing in Theories of the Dopamine System

Nathaniel Daw — 2006-05-25T16:20:43-00:00

Neural Comp., Vol. 18, No. 7. (1 July 2006), pp. 1637-1677.

Although the responses of dopamine neurons in the primate midbrain are well characterized as carrying a temporal difference (TD) error signal for reward prediction, existing theories do not offer a credible account of how the brain keeps track of past sensory events that may be relevant to predicting future reward. Empirically, these shortcomings of previous theories are particularly evident in their account of experiments in which animals were exposed to variation in the timing of events. The original theories mispredicted the results of such experiments due to their use of a representational device called a tapped delay line. Here we propose that a richer understanding of history representation and a better account of these experiments can be given by considering TD algorithms for a formal setting that incorporates two features not originally considered in theories of the dopaminergic response: partial observability (a distinction between the animal's sensory experience and the true underlying state of the world) and semi-Markov dynamics (an explicit account of variation in the intervals between events). The new theory situates the dopaminergic system in a richer functional and anatomical context, since it assumes (in accord with recent computational theories of cortex) that problems of partial observability and stimulus history are solved in sensory cortex using statistical modeling and inference and that the TD system predicts reward using the results of this inference rather than raw sensory data. It also accounts for a range of experimental data, including the experiments involving programmed temporal variability and other previously unmodeled dopaminergic response phenomena, which we suggest are related to subjective noise in animals' interval timing. Finally, it offers new experimental predictions and a rich theoretical framework for designing future experiments.

Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons.

G Buzsáki — 2006-11-30T15:27:51-00:00

Trends Neurosci, Vol. 27, No. 4. (April 2004), pp. 186-193.

The performance of the brain is constrained by wiring length and maintenance costs. The apparently inverse relationship between number of neurons in the various interneuron classes and the spatial extent of their axon trees suggests a mathematically definable organization, reminiscent of 'small-world' or scale-free networks observed in other complex systems. The wiring-economy-based classification of cortical inhibitory interneurons is supported by the distinct physiological patterns of class members in the intact brain. The complex wiring of diverse interneuron classes could represent an economic solution for supporting global synchrony and oscillations at multiple timescales with minimum axon length.

Network Synchrony in the Nucleus Accumbens In Vivo

Yukiori Goto — 2006-11-29T12:31:18-00:00

J. Neurosci., Vol. 21, No. 12. (15 June 2001), pp. 4498-4504.

Nucleus accumbens neurons show membrane potential fluctuations between a very negative resting membrane potential and periodical plateau depolarizations. Because action potential firing occurs only during the depolarized state, the control of transitions between states is important for information processing within this region, with an impact on accumbens-related behaviors. It has been proposed that ensembles of active neurons in the nucleus accumbens could be based on a population of cells depolarizing simultaneously into the UP state. In this study, in vivo intracellular recordings from accumbens neurons were performed simultaneously with local field potential recordings to examine whether the nucleus accumbens can exhibit synchronization of membrane potential states in a population of neurons. These simultaneous recordings indicated that local field potential shifts occurred synchronously with transitions to the UP state. Furthermore, manipulations that evoked prolonged plateau depolarizations also evoked field potentials of similar duration. Such signals likely occurred because of simultaneous membrane potential changes in a population of neurons. Together with our previous studies, these results suggest that membrane potential states in the nucleus accumbens can be synchronized by synaptic inputs from the hippocampus.

Computational models of the basal ganglia

Andrew Gillies — 2006-11-02T11:17:48-00:00

Movement Disorders, Vol. 15, No. 5. (2000), pp. 762-770.

Computer simulation studies and mathematical analysis of models of the basal ganglia are being used increasingly to explore theories of basal ganglia function. We review the implications of these new models for a general understanding of basal ganglia function in normal as well as in diseased brains. The focus is on their functional similarities rather than on the details of mathematical methodologies and simulation techniques. Most of the models suggest a vital role for the basal ganglia in learning. Although this interest in learning is partly driven by experimental results associating the acute firing of dopamine cells with reward prediction in monkeys, some of the models have preceded the electrophysiological results. An- other common theme of the models is selection. In this case, the striatum is seen as detecting and selecting cortical contexts for access to basal ganglia output. Although the behavioral consequences of this selection are hard to define, the models provide frameworks within which to explore these ideas empirically. This provides a means of refining our understanding of basal ganglia function and to consider dysfunction within the new logical frameworks.

The computational role of dopamine D1 receptors in working memory.

D Durstewitz — 2006-10-30T13:36:32-00:00

Neural Netw, Vol. 15, No. 4-6. (l 2002), pp. 561-572.

The prefrontal cortex (PFC) is essential for working memory, which is the ability to transiently hold and manipulate information necessary for generating forthcoming action. PFC neurons actively encode working memory information via sustained firing patterns. Dopamine via D1 receptors potently modulates sustained activity of PFC neurons and performance in working memory tasks. In vitro patch-clamp data have revealed many different cellular actions of dopamine on PFC neurons and synapses. These effects were simulated using realistic networks of recurrently connected assemblies of PFC neurons. Simulated D1-mediated modulation led to a deepening and widening of the basins of attraction of high (working memory) activity states of the network, while at the same time background activity was depressed. As a result, self-sustained activity was more robust to distracting stimuli and noise. In this manner, D1 receptor stimulation might regulate the extent to which PFC network activity is focused on a particular goal state versus being open to new goals or information unrelated to the current goal.

Cerebellar aminergic neuromodulation: towards a functional understanding.

N Schweighofer — 2006-10-27T16:34:08-00:00

Brain Res Brain Res Rev, Vol. 44, No. 2-3. (March 2004), pp. 103-116.

Although a number of neuromodulators influence the cerebellar circuitry, their functions remain largely unknown. By reviewing and combining results from data-driven and theory-driven studies, we attempt to provide an integrated systems view of cerebellar neuromodulation. First, we review the short- and long-term effects of neuromodulators on the cerebellar circuitry. Second, we review recent theories of the cerebellum and show that a number of modulatory signals are needed for powerful cerebellar learning and control. Finally, we attempt to match each theoretically derived modulatory signal with a specific neuromodulator. In particular, we propose that serotonin controls the 'responsibility' of each cerebellar unit (or microcomplex) in cerebellar learning and control; norepinephrine gates unsupervised learning in the cerebellar cortex; dopamine enhances goal-oriented cerebellar learning; and, finally, acetylcholine controls the speed of supervised learning in Purkinje cells.

Network reset: a simplified overarching theory of locus coeruleus noradrenaline function.

S Bouret — 2006-10-27T16:33:15-00:00

Trends Neurosci, Vol. 28, No. 11. (November 2005), pp. 574-582.

Unraveling the functional role of neuromodulatory systems has been a major challenge for cognitive neuroscience, giving rise to theories ranging from a simple role in vigilance to complex models concerning decision making, prediction errors or unexpected uncertainty. A new, simplified and overarching theory of noradrenaline function is inspired by an invertebrate model: neuromodulators in crustacea abruptly interrupt activity in neural networks and reorganize the elements into new functional networks determining the behavioral output. Analogously in mammals, phasic activation of noradrenergic neurons of the locus coeruleus in time with cognitive shifts could provoke or facilitate dynamic reorganization of target neural networks, permitting rapid behavioral adaptation to changing environmental imperatives. Detailed analysis and discussion of extensive electrophysiological data from the locus coeruleus of rats and monkeys in controlled behavioral situations is provided here to support this view. This simplified 'new look' at locus coeruleus noradrenaline function redirects the challenge of understanding neuromodulatory systems towards their target networks, particularly to the dynamics of their interactions and how they organize adaptive behavior.

Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex

Daniel Durstewitz — 2006-10-27T15:27:34-00:00

J Neurophysiol, Vol. 83, No. 3. (1 March 2000), pp. 1733-1750.

Durstewitz, Daniel, Jeremy K. Seamans, and Terrence J. Sejnowski. Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex. J. Neurophysiol. 83: 1733-1750, 2000. The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na+ and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABAA conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABAA antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.

Dynamical Basis of Irregular Spiking in NMDA-Driven Prefrontal Cortex Neurons.

Daniel Durstewitz — 2006-09-19T23:28:26-00:00

Cereb Cortex (1 June 2006)

Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. Here we report that highly irregular firing can be induced through a combination of NMDA- and dopamine D1 receptor agonists applied to adult PFC neurons in vitro. The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, while neurons with clearly mono- or bimodal distributions fired much more regularly. Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. The nonlinearity of NMDA conductances was crucial for inducing this regime. NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.

Diversity within a Birdsong

Rodrigo Laje — 2006-10-17T18:41:22-00:00

Physical Review Letters, Vol. 89, No. 28. (27 December 2002), 288102.

We present a model for the activities of neural circuits in a nucleus found in the brains of songbirds: the robust nucleus of the archistriatum (RA). This is a fore brain song control nucleus responsible for the phasic and precise neural signals driving vocal and respiratory motor neurons during singing. Driving a physical model of the avian vocal organ with the signals generated by the neural model; we produce synthetic songs. This allows us to show that certain connectivity architectures in the RA give rise to a wide range of different vocalizations under simple excitatory instructions.

Evaluating theories of bird song learning: implications for future directions

D Margoliash — 2006-10-17T18:39:39-00:00

Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, Vol. V188, No. 11. (1 December 2002), pp. 851-866.

Dynamical model of birdsong maintenance and control

Henry Abarbanel — 2006-10-17T18:38:44-00:00

Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), Vol. 70, No. 5. (2004)

The neuroethology of song learning, production, and maintenance in songbirds presents interesting similarities to human speech. We have developed a biophysical model of the manner in which song could be maintained in adult songbirds. This model may inform us about the human counterpart to these processes. In songbirds, signals generated in nucleus High Vocal center (HVc) follow a direct route along a premotor pathway to the robust nucleus of the archistriatum (RA) as well as an indirect route to RA through the anterior forebrain pathway (AFP): the neurons of RA are innervated from both sources. HVc expresses very sparse bursts of spikes having interspike intervals of about 2 ms. The expressions of these bursts arrive at the RA with a time difference T50±10 ms between the two pathways. The observed combination of AMPA and NMDA receptors at RA projection neurons suggests that long-term potentiation and long-term depression can both be induced by spike timing plasticity through the pairing of the HVc and AFP signals. We present a dynamical model that stabilizes this synaptic plasticity through a feedback from the RA to the AFP using known connections. The stabilization occurs dynamically and is absent when the RAAFP connection is removed. This requires a dynamical selection of T. The model does this, and T lies within the observed range. Our model represents an illustration of a functional consequence of activity-dependent plasticity directly connected with neuroethological observations. Within the model the parameters of the AFP, and thus the magnitude of T, can also be tuned to an unstable regime. This means that destabilization might be induced by neuromodulation of the AFP.

Birdsong: models and mechanisms.

TW Troyer — 2006-10-17T18:36:57-00:00

Curr Opin Neurobiol, Vol. 11, No. 6. (December 2001), pp. 721-726.

Recent studies have provided important information concerning the neural signals that subserve vocal learning in songbirds: advanced signal processing techniques are beginning to clarify the behavioral trajectories followed by developing birds; single-unit physiology in behaving animals is providing important clues about sensory and motor representations during learning; in vitro whole-cell recordings are revealing patterns of synaptic communication; and experimental alterations in song behavior have advanced our understanding of specific structure-function relationships. The construction of theoretical and computational models will be crucial in integrating such disparate experimental results.

Neuromuscular control of vocalizations in birdsong: A model

Rodrigo Laje — 2006-10-17T18:28:57-00:00

Physical Review E, Vol. 65, No. 5. (20 May 2002), 051921.

We present a dynamical model of the processes involved in birdsong production; relating qualitatively its parameters with biological ones. In this way; we intend to unify the activity patterns of the muscles controlling the vocal organ with the resulting vocalization. With relatively simple paths in the parameter space of our model; we reproduce experimental recordings of the Chingolo sparrow ( Zonotrichia capensis ).

Similar network activity from disparate circuit parameters

Astrid Prinz — 2004-12-06T02:31:57-00:00

Nature Neuroscience, Vol. 7, No. 12. (21 November 2004), 1345.

Structure and visualization of high-dimensional conductance spaces.

AL Taylor — 2006-10-17T13:38:44-00:00

J Neurophysiol, Vol. 96, No. 2. (August 2006), pp. 891-905.

Neurons, and realistic models of neurons, typically express several different types of voltage-gated conductances. These conductances are subject to continual regulation. Therefore it is essential to understand how changes in the conductances of a neuron affect its intrinsic properties, such as burst period or delay to firing after inhibition of a particular duration and magnitude. Even in model neurons, it can be difficult to visualize how the intrinsic properties vary as a function of their underlying maximal conductances. We used a technique, called clutter-based dimension reordering (CBDR), which enabled us to visualize intrinsic properties in high-dimensional conductance spaces. We applied CBDR to a family of models with eight different types of voltage- and calcium-dependent channels. CBDR yields images that reveal structure in the underlying conductance space. CBDR can also be used to visualize the results of other types of analysis. As examples, we use CBDR to visualize the results of a connected-components analysis, and to visually evaluate the results of a separating-hyperplane (i.e., linear classifier) analysis. We believe that CBDR will be a useful tool for visualizing the conductance spaces of neuronal models in many systems.

Variability, compensation and homeostasis in neuron and network function

Eve Marder — 2006-06-22T20:21:43-00:00

Nature Reviews Neuroscience, Vol. 7, No. 7. (July 2006), pp. 563-574.

Global Structure, Robustness, and Modulation of Neuronal Models

Mark Goldman — 2006-10-12T12:20:07-00:00

J. Neurosci., Vol. 21, No. 14. (15 July 2001), pp. 5229-5238.

The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. We vary the maximal conductance parameters of the model neuron and, for each set of parameters tested, characterize the activity pattern generated by the cell as silent, tonically firing, or bursting. Within the parameter space of the five maximal conductances of the model, we find directions, representing concerted changes in multiple conductances, along which the basic pattern of neural activity does not change. In other directions, relatively small concurrent changes in a few conductances can induce transitions between these activity patterns. The global structure of the conductance-space maps implies that neuromodulators that alter a sensitive set of conductances will have powerful, and possibly state-dependent, effects. Other modulators that may have no direct impact on the activity of the neuron may nevertheless change the effects of such direct modulators via this state dependence. Some of the results and predictions arising from the model studies are replicated and verified in recordings of stomatogastric ganglion neurons using the dynamic clamp.

Cellular, synaptic and network effects of neuromodulation.

E Marder — 2006-01-13T19:17:16-00:00

Neural Netw, Vol. 15, No. 4-6. (l 2002), pp. 479-493.

All network dynamics emerge from the complex interaction between the intrinsic membrane properties of network neurons and their synaptic connections. Nervous systems contain numerous amines and neuropeptides that function to both modulate the strength of synaptic connections and the intrinsic properties of network neurons. Consequently network dynamics can be tuned and configured in different ways, as a function of the actions of neuromodulators. General principles of the organization of modulatory systems in nervous systems include: (a) many neurons and networks are multiply modulated, (b) there is extensive convergence and divergence in modulator action, and (c) some modulators may be released extrinsically to the modulated circuit, while others may be released by some of the circuit neurons themselves, and act intrinsically. Some of the computational consequences of these features of modulator action are discussed.

Prefrontal cortex and flexible cognitive control: rules without symbols.

NP Rougier — 2005-08-17T06:17:45-00:00

Proc Natl Acad Sci U S A, Vol. 102, No. 20. (17 May 2005), pp. 7338-7343.

Human cognitive control is uniquely flexible and has been shown to depend on prefrontal cortex (PFC). But exactly how the biological mechanisms of the PFC support flexible cognitive control remains a profound mystery. Existing theoretical models have posited powerful task-specific PFC representations, but not how these develop. We show how this can occur when a set of PFC-specific neural mechanisms interact with breadth of experience to self organize abstract rule-like PFC representations that support flexible generalization in novel tasks. The same model is shown to apply to benchmark PFC tasks (Stroop and Wisconsin card sorting), accurately simulating the behavior of neurologically intact and frontally damaged people.