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A cell assembly model of sequential memory
- In Neural Networks, 2007. IJCNN 2007. International Joint Conference on
, 2007
"... Abstract—Perception, prediction and generation of sequences is a fundamental aspect of human behavior and depends on the ability to detect serial order. This paper presents a plausible model of sequential memory at the neurological level based on the theory of cell assemblies. The basic idea is that ..."
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Abstract—Perception, prediction and generation of sequences is a fundamental aspect of human behavior and depends on the ability to detect serial order. This paper presents a plausible model of sequential memory at the neurological level based on the theory of cell assemblies. The basic idea is that sequences in the brain are represented by cell assemblies. Each item of the sequence and the sequential association between the items are represented by cell assemblies. Simulation results show that the model is capable of recognizing and discriminating multiple sequences stored in memory. The cell assemblies that represent the sequential association between two items are activated if these items occur in the input in the correct order. These sequence detecting cell assemblies form the basis of this model. A simulation presenting 100 stored sequences and 100 not stored recognizes perfectly 90 % of the time with no false positives.
A temporally asymmetric Hebbian network for sequential working memory
- Proc. of the Int’l Conf. on Cognitive Modeling
, 2010
"... Recurrent connections combined with the appropriate dynamics enable oscillatory neural networks to produce rhythmic activity patterns. Such oscillatory activity can represent multiple stored patterns simultaneously, rather than the single pattern of a fixed-point network. However, retrieving these s ..."
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Recurrent connections combined with the appropriate dynamics enable oscillatory neural networks to produce rhythmic activity patterns. Such oscillatory activity can represent multiple stored patterns simultaneously, rather than the single pattern of a fixed-point network. However, retrieving these stored patterns in the same order as they were seen has proven challenging. In this paper we modify a recently developed simple oscillatory memory capable of storing temporal sequences so that it will now retrieve remembered items in the same order presented. This was achieved through the use of a temporally asymmetric weight matrix. The network is still capable of matching the recall performance of human subjects, reproducing the recency effect they exhibit in working memory tasks and displaying similar position-specific recall rates. We conclude that augmenting simple oscillatory neural network models with temporally asymmetric synaptic connections substantially improves their ability to match human short term memory properties.
Temporal kernel recurrent neural networks
- Neural Networks
"... Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedback connections. An RNN has a set of units, each taking a real value in each timestep, and a set of weighted connections between its units. The input units are set by the environment and the output uni ..."
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Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedback connections. An RNN has a set of units, each taking a real value in each timestep, and a set of weighted connections between its units. The input units are set by the environment and the output units are
The Ordinal Serial Encoding Model: Serial Memory in Spiking Neurons
"... any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades ..."
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any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture. iii Acknowledgements
Neuroevolution results in emergence of short-term memory in multi-goal environment
- In Proceeding of the GECCO ’13, GECCO ’13
, 2013
"... ABSTRACT Animals behave adaptively in environments with multiple competing goals. Understanding of mechanisms underlying such goal-directed behavior remains a challenge for neuroscience as well as for adaptive system and machine learning research. To address this problem we developed an evolutionar ..."
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ABSTRACT Animals behave adaptively in environments with multiple competing goals. Understanding of mechanisms underlying such goal-directed behavior remains a challenge for neuroscience as well as for adaptive system and machine learning research. To address this problem we developed an evolutionary model of adaptive behavior in a multi-goal stochastic environment. The proposed neuroevolutionary algorithm is based on neuron's duplication as a basic mechanism of agent's recurrent neural network development. Results of simulations demonstrate that in the course of evolution agents acquire the ability to store the short-term memory and use it in behavior with alternative actions. We found that evolution discovered two mechanisms for short-term memory. The first mechanism is integration of sensory signals and ongoing internal neural activity, resulting in emergence of cell groups specialized on alternative actions. And the second mechanism is slow neurodynamical process that makes possible to encode the previous behavioral choice.
Positional and temporal clustering in serial order memory
"... The well-known finding that errors in serial recall tend to be clustered around the position of the target item has bolstered positional-coding theories of serial order memory. Here we show that this well-known effect is confounded with the tendency for the item recalled prior to an error to come fr ..."
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The well-known finding that errors in serial recall tend to be clustered around the position of the target item has bolstered positional-coding theories of serial order memory. Here we show that this well-known effect is confounded with the tendency for the item recalled prior to an error to come from the correct list position. When the standard analysis of positional clustering is conditioned on the positional-accuracy of the previously recalled item, one can see very strong effects of temporal contiguity, with errors being distributed near the position of the item following the prior recall. We show that a simple associative chaining model with asymmetric neighboring, remote associations, and a primacy gradient can account for both the standard positional clustering effect and the contiguity effect demonstrated here. Using the same parameters, the model produces reasonable serial position curves and captures the change in these curves across study-test trials as well as the gains and losses of item and order information across trials. Keywords: Serial recall, serial order, association, clustering. Associative chaining and positional coding consistute the two classic models of serial order memory. Although as-sociative chaining was the implicit theory in Ebbinghaus’
The Divergent-Reconvergent Model of Serial Order Encoding and Retrieval
"... This paper presents a new connectionist model for serial order encoding and retrieval. It is based on a divergent-reconvergent structure, which encodes a list of items as a superimposed distributed representation of them, where early items are coded by more active units than late ones due to lateral ..."
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This paper presents a new connectionist model for serial order encoding and retrieval. It is based on a divergent-reconvergent structure, which encodes a list of items as a superimposed distributed representation of them, where early items are coded by more active units than late ones due to lateral inhibition. Thus, early items are more active than late ones. Retrieval is based on the activation gradient: select the most active item, and then inhibit it to allow the retrieval of the next item. Side effects of this structure can give rise to the primacy effect, the recency effect and similarity effects, similar to those found in human immediate serial recall. The divergent-reconvergent structure strikingly resembles what is found in the basal ganglia. This model may provide a new computational and functional account for the sequencing function of the basal ganglia or other brain areas which may implement it.
COMMENTS A Fundamental Limitation of the Conjunctive Codes Learned in PDP Models of Cognition: Comment on Botvinick and Plaut (2006)
"... A central claim shared by most recent models of short-term memory (STM) is that item knowledge is coded independently from order in long-term memory (LTM; e.g., the letter A is coded by the same representational unit whether it occurs at the start or end of a sequence). Serial order is computed by d ..."
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A central claim shared by most recent models of short-term memory (STM) is that item knowledge is coded independently from order in long-term memory (LTM; e.g., the letter A is coded by the same representational unit whether it occurs at the start or end of a sequence). Serial order is computed by dynamically binding these item codes to a separate representation of order. By contrast, Botvinick and Plaut (2006) developed a parallel distributed processing (PDP) model of STM that codes for item-order information conjunctively, such that the same letter in different positions is coded differently in LTM. Their model supports a wide range of memory phenomena, and critically, STM is better for lists that include high, as opposed to low, sequential dependencies (e.g., bigram effects). Models with contextindependent item representations do not currently account for sequential effects. However, we show that their PDP model is too sensitive to these effects. A modified version of the model does better but still fails in important respects. The successes and failures can be attributed to a fundamental constraint associated with context-dependent representations. We question the viability of conjunctive coding schemes to support STM and take these findings as problematic for the PDP approach to cognition more generally.
1 Forgetting in memory models:
"... Arguments against trace decay and consolidation failure ..."
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Arguments against trace decay and consolidation failure
© The Author(s) 2011 Reprints and permission:
"... action activates a mental representation of its corresponding sensory effects, even if these effects are irrelevant or not intended in the present context (Hoffmann, Sebald, & Stöcker, ..."
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action activates a mental representation of its corresponding sensory effects, even if these effects are irrelevant or not intended in the present context (Hoffmann, Sebald, & Stöcker,
