Short-Term Plasticity Explains Irregular Persistent Activity in Working Memory Tasks

Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing circuit mechanisms can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing mechanistic accounts, and critical points to resolve are firing rate tuning and shared noise modulations by E/I ratio alterations through NMDAR blockade, and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.

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Circuit mechanisms for the maintenance and manipulation of information in working memory

10.1101/305714 ◽

2018 ◽

Cited By ~ 7

Author(s):

Nicolas Y. Masse ◽

Guangyu R. Yang ◽

H. Francis Song ◽

Xiao-Jing Wang ◽

David J. Freedman

Keyword(s):

Working Memory ◽

Neuronal Activity ◽

Short Term Memory ◽

Current Debate ◽

Memory Representation ◽

Short Term ◽

Short Term Plasticity ◽

Hidden States ◽

Dependent Tasks ◽

External Stimuli

SummaryRecently it has been proposed that information in short-term memory may not always be stored in persistent neuronal activity, but can be maintained in “activity-silent” hidden states such as synaptic efficacies endowed with short-term plasticity (STP). However, working memory involves manipulation as well as maintenance of information in the absence of external stimuli. In this work, we investigated working memory representation using recurrent neural network (RNN) models trained to perform several working memory dependent tasks. We found that STP can support the short-term maintenance of information provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent neuronal activity naturally emerges from learning, and the amount of persistent neuronal activity scales with the degree of manipulation required. These results shed insight into the current debate on working memory encoding, and suggest that persistent neural activity can vary markedly between tasks used in different experiments.

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Altered short-term plasticity within the working memory neural network: Is it neuroticism or is it depression?

Human Brain Mapping ◽

10.1002/hbm.23116 ◽

2016 ◽

Vol 37 (4) ◽

pp. 1512-1513 ◽

Cited By ~ 1

Author(s):

Renzo Bianchi ◽

Eric Laurent

Keyword(s):

Neural Network ◽

Working Memory ◽

Short Term ◽

Short Term Plasticity

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Theoretical distinction between functional states in working memory and their corresponding neural states

10.31234/osf.io/n37su ◽

2020 ◽

Cited By ~ 1

Author(s):

Mark G. Stokes ◽

Paul S. Muhle-Karbe ◽

Nicholas E. Myers

Keyword(s):

Working Memory ◽

Neural Activity ◽

Potential Role ◽

Neural Mechanisms ◽

Persistent Activity ◽

Short Term ◽

Functional Roles ◽

Cognitive States ◽

Neurophysiological Mechanisms ◽

Functional States

Working memory (WM) is important for guiding behaviour, but not always immediately. Here we define a WM item that is currently relevant for guiding behaviour as the functionally ‘active’ item; whereas items maintained in WM, but not immediately relevant to behaviour, are functionally ‘latent’. Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for ‘activity-silent’ mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, it is tempting to associate functionally active and latent cognitive states of WM with persistent- activity and activity-silent neural mechanisms, respectively. In this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity vs activity-silent WM.

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Stability of working memory in continuous attractor networks under the control of short-term plasticity

10.1101/424515 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alexander Seeholzer ◽

Moritz Deger ◽

Wulfram Gerstner

Keyword(s):

Working Memory ◽

Cortical Neurons ◽

Broad Class ◽

Theoretical Description ◽

Memory Retention ◽

Synaptic Connections ◽

Short Term ◽

Short Term Plasticity ◽

Continuous State ◽

Biological Substrate

AbstractContinuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. However, it currently remains unclear to which degree these two short-term plasticity mechanisms interact, what their combined quantitative effect on working memory stability is, and whether these effects persist in neuronal networks with spike-based transmission. Here, we present a comprehensive description of the effects of short-term plasticity on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to spiking and rate-based models alike, accurately describes the slow dynamics of stored memory positions in separate processes of diffusion due to spiking variability and drift due to sparse connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory establishes links to experiments: we are able to predict the sensitivity of continuous working memory to distractor inputs and place constraints on network and synapse properties necessary to implement stable working memory.Author summaryThe ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby ”pinning down” memorized positions. Here, we present a general theory that accurately predicts the extent of this ”pinning down” by short-term plasticity in a broad class of biologically plausible models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a direct and novel theoretical link from the microscopic substrate of working memory – neurons and synaptic connections – to observable behavioral correlates. This allows us to constrain properties of cortical networks that are currently hard to assess experimentally, which we hope will help guide future theoretical and experimental work.

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Constraints on Persistent Activity in a Biologically Detailed Network Model of the Prefrontal Cortex with Heterogeneities

10.1101/645663 ◽

2019 ◽

Author(s):

Joachim Hass ◽

Salva Ardid ◽

Jason Sherfey ◽

Nancy Kopell

Keyword(s):

Working Memory ◽

Prefrontal Cortex ◽

Network Model ◽

Homeostatic Plasticity ◽

Data Driven ◽

Synaptic Connections ◽

Persistent Activity ◽

Short Term ◽

Cell Assemblies ◽

Homogeneity Assumption

AbstractPersistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. Our model predicts that persistent activity is recovered if the heterogeneity in the activity of individual interneurons is diminished, which could be achieved by a homeostatic plasticity mechanism. Such a plasticity scheme could also compensate the heterogeneities in the synaptic weights and short-term plasticity when applied to the inhibitory synapses. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Furthermore, the model predicts that a network that exhibits persistent activity is not able to dynamically produce intrinsic in vivo-like irregular activity at the same time, because heterogeneous synaptic connections are required for these dynamics. Thus, the background noise in such a network must either be produced by external input or constitutes an entirely different state of the network, which is brought about, e.g., by neuromodulation.Author summaryTo operate effectively in a constantly changing world, it is crucial to keep relevant information in mind for short periods of time. This ability, called working memory, is commonly assumed to rest on reverberating activity among members of cell assemblies. While effective in reproducing key results of working memory, most cell assembly models rest on major simplifications such as using the same parameters for all neurons and synapses, i.e., assuming homogeneity in these parameters. Here, we show that this homogeneity assumption is necessary for persistent activity to arise, specifically for inhibitory interneurons and synapses. Using a strongly data-driven network model of the prefrontal cortex, we show that the heterogeneities in the above parameters that are implied by in vitro studies prevent persistent activity. When homogeneity is imposed on inhibitory neurons and synapses, persistent activity is recovered. We propose that the homogeneity constraints can be implemented in the brain by means of homeostatic plasticity, a form of learning that keeps the activity of a network in a constant, homeostatic state. The model makes a number of predictions for biological networks, including a structural separation of networks responsible for generating persistent activity and spontaneous, noise-like activity.

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