Amit defines cell assemblies aslocal cortical neuron populationswith strong internal connections. However, Hebb himself proposed that cell assemblies are distributed over different cortical areas (nonlocal ortranscortical assemblies). We review evidence from cognitive neuroscience and neuropsychology supporting the assumption that cell assemblies are transcortical.

A distinction should be made between the formation of stimulus-driven associations and cognitive concepts. To test the learning mode of a neural network, we propose a simple and classic input-output test: the discrimination shift task. Feed-forward PDP models appear to form stimulus-driven associations. A Hopfield network should be extended to apply the test.

Amit's Hebbian model risks being overexplanatory, since it does not depend on specific physiological modelling of cortical ANNs, but concentrates on those phenomena which are modelled by a large class of ANNs. While offering a strong demonstration of the presence of Hebb's “cell assemblies,” it does not offer an equal account of Hebb's “phase sequence” concept.

Amit's work addresses a critical issue in cognitive science: the structure of neural representations. The use of Hebbian cell assemblies is a positive step, and we now need to consider its role in a larger cognitive theory. When considering the dynamics of a system built out of attractors, a more limited version of reverberation becomes necessary.

Recurrent excitation is experimentally well documented in cortical populations. It provides for intracortical excitatory biases that linearize negative feedback interactions and induce macroscopic state transitions during perception. The concept of the local neighborhood should be expanded to spatial patterns as the basis for perception, in which large areas of cortex are bound into cooperative behavior with near-silent columns as important as active columns revealed by unit recording.

This commentary questions the target articles inferences from a limited set of empirical data to support this model and conceptual scheme. Especially questionable is the attribution of internal representation properties to an assembly of cells in a discrete cortical module firing at a discrete attractor frequency. Alternative inferences are drawn from cortical cooling and cell-firing data that point to the internal representation as a broad and specific cortical network defined by cortico-cortical connectivity. Active memory, it is proposed, consists in the (...) sustained activation of the component neuron populations of the network. (shrink)

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...) there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged,diversity.Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into:modality-specificrepresentations, instantiated on perceptual similarity;multimodalrepresentations, instantiated primarily on the discovery of spatiotemporal contiguity; andcategoricalrepresentations, instantiated primarily on social interaction. These elements serve as the building blocks ofcomplex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e.,predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. (shrink)

Every immediate memory is unique; it is therefore unlikely to consist of an attractor or even a combination of attractors. In the present state of knowledge about the chemistry of synaptic transmission, there is no reason to look beyond neurons that directly receive sensory afferents for the afterdischarges that correspond to active memories.

The cell assembly as a simple attractor cannot explain many cognitive phenomena. It must be a highly structured network that can sustain highly structured excitation patterns. Moreover, a cell assembly must be more widely distributed in space than on a square millimeter.

The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks (ANN), but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.

All recent memory theories of hippocampal function have incorporated the idea that the hippocampus is required to process items only of some qualitatively specifiahle kind, and is not required to process items of some complementary set. In contrast, it is now proposed that the hippocampus is needed to process stimuli of all kinds, but only when there is a need to associate those stimuli with other events that are temporally discontiguous. In order to form or use temporally discontiguous associations, it (...) is essential to maintain some memory of the first component until the second component has occurred. When the temporal gap to he spanned is small, and the number of items to be temporarily retained is low, a limited-capacity, short-term store is sufficient to allow associations to be formed. Such a store is presumed to operate in parallel with the hippocampus in normal animals. Hippocampal damage disrupts a much higher-capacity store that has a slower decay rate, and so leaves animals with only a very limited ability to form temporally discontiguous associations. Hippocampal damage, however, is not held to affect the long-term storage of associations of any kind, if they can be formed. Analyses of both new and existing data are presented to show that by classifying tasks in terms of the need to use a temporary memory store to retain temporally discontiguous information one can cut right across existing classifications as well as achieve a better fit to the data. The hippocampus thus seems best described as a high-capacity, intermediate-term memory store. (shrink)