Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model

Compte, Albert; Brunel, Nicolas; Goldman‐Rakic, Patricia S.; Wang, Xiao Jing

doi:10.1093/cercor/10.9.910

Cited by 1,091 publications

(1,419 citation statements)

References 77 publications

Supporting

Mentioning

1,338

Contrasting

Unclassified

Order By: Relevance

“…Our simulation results are complementary from those of Wang (2003, 2004): they showed how the network dynamics is influenced by the NMDA/AMPA ratio, and how the update of the HD signal by external cues can take the form of either a continuous rotation of the network state or a discontinuous jump to the new HD depending on the distance between the old and new HDs and the strength of the external cue (consistent with previous studies in networks with simpler architectures, e.g. Compte et al, 2000). Here, we have shown how the network dynamics is influenced by the mutual inhibitory connections in DTN, and how the offset in the projections from DTN to LMN control the saturation velocity above which the system is no longer able to integrate accurately.…”

Section: Discussionsupporting

confidence: 83%

“…Continuous attractor network models (Amari, 1977;Ermentrout, 1998) have been proposed to account for selectivity properties of sensory (Ben-Yishai et al, 1995;Somers et al, 1995;Hansel and Sompolinsky, 1998) and motor (Lukashin and Georgopoulos, 1993) systems, for maintenance of a continuous variable in working memory in prefrontal and parietal cortices (Camperi and Wang, 1998;Compte et al, 2000;Laing and Chow, 2001;Gutkin et al, 2001), and for properties of hippocampal place cells (Tsodyks and Sejnowski, 1995;Samsonovich and McNaughton, 1997;Redish and Touretzky, 1997;Battaglia and Treves, 1998;Kali and Dayan, 2000).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System

2005

View full text Add to dashboard Cite

Abstract. Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.

show abstract

Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System

2005

View full text Add to dashboard Cite

show abstract

“…In this context, it is important to note that other researchers have probed the stability properties of bump attractors using a more biophysical style of neural modeling (Compte, Brunel, Goldman-Rakic & Wang, 2000;Wang, 2001). In our view, both approaches demonstrate that stable peaks of activation provide a viable neural mechanism for the formation of "working" memories.…”

Section: The Dft Is Grounded By Neural Principlesmentioning

confidence: 99%

“…4 and 5). Such drift is a consequence of the local excitation/lateral inhibition form of neural interaction in the DFT (see also Compte, et al, 2000).…”

Section: Comparing the Dft To Other Modelsmentioning

confidence: 99%

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Simmering¹,

Schutte²,

Spencer³

2008

Brain Research

115

View full text Add to dashboard Cite

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks-the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity-generating novel, testable predictions-and generality-spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

show abstract

“…Models of cortical networks have attempted to generate activity comparable to experiments, and several types of models were proposed, ranging from integrate and fire networks (Amit and Brunel, 1997;Brunel, 2000) up to conductance-based network models (Compte et al, 2000;Timofeev et al, 2000;Vogels and Abbott, 2005;Kumar et al, in press). In particular, a recent study (Vogels and Abbott, 2005) provided relatively small networks ('10,000 neurons) displaying self-sustained activity, which were used to investigate the effect of ''internal dynamics'' on signal propagation.…”

Section: Introductionmentioning

confidence: 99%

Activated cortical states: Experiments, analyses and models

Boustani

Pospischil

Rudolph-Lilith

et al. 2007

Journal of Physiology-Paris

View full text Add to dashboard Cite

In awake animals, the cerebral cortex displays an ''activated'' state, with distinct characteristics compared to other states like slow-wave sleep or anesthesia. These characteristics include a sustained depolarized membrane potential (V m ) and irregular firing activity. In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analysis performed in awake cat association cortex. These analyses show that cortical activity is characterized by an apparent Poisson-distributed stochastic dynamics, both at the single-cell and population levels, and that single cells display a high-conductance state dominated by inhibition. We next overview computational models of the ''awake'' cortex, and perform the same analyses as in the experiments. Many properties identified experimentally are indeed reproduced by models, such as depolarized V m , irregular firing with apparent Poisson statistics, and the determinant role of inhibitory fluctuations on spiking. However, other features are not well reproduced, such as firing statistics and the conductance state of the membrane, suggesting that the network state displayed by models is not entirely correct. We also show how networks can approach a correct conductance state, suggesting ways by which future models will generate activity fully consistent with experimental data.

show abstract

Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model

Cited by 1,091 publications

References 77 publications

A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System

A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Activated cortical states: Experiments, analyses and models

Contact Info

Product

Resources

About