Flutter discrimination: neural codes,perception, memory and decision making

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.     

A new neural network architecture with associative memory, pruning and order-sensitive learning

A new paradigm of neural network architecture is proposed that works as associative memory along with capabilities of pruning and order-sensitive learning. The network has a composite structure wherein each node of the network is a Hopfield network by itself. The Hopfield network employs an order-sensitive learning technique and converges to user-specified stable states without having any spurious states. This is based on geometrical structure of the network and of the energy function. The network is so designed that it allows pruning in binary order as it progressively carries out associative memory retrieval. The capacity of the network is 2n, where n is the number of basic nodes in the network. The capabilities of the network are demonstrated by experimenting on three different application areas, namely a Library Database, a Protein Structure Database and Natural Language Understanding.     

On the behavior of some associative neural networks

Since Hopfield published his work on an associative memory model, a large number of works have studied the model from several angles and showed in particular its weaknesses, and presented ways to overcome them. Most of the proposed solutions seem to us however not biologically plausible. In this paper we present a simple statistical analysis of two networks similar to the Hopfield net, and show that the usage of positive feedback enhances the net recognizing capability without jeopardizing the stability. We also describe a layered parallel network composed of modules, each module being a modified Hopfield net. We finally present computer simulation results to support our analytical findings. The most important principles of this network are supported by data from the world of neurobiology.     

An associative memory that can form hypotheses: a phase-coded neural network

 Nonlinear associative memories as realized, e.g., by Hopfield nets are characterized by attractor-type dynamics. When fed with a starting pattern, they converge to exactly one of the stored patterns which is supposed to be most similar. These systems cannot render hypotheses of classification, i.e., render several possible answers to a given classification problem. Inspired by von der Malsburg’s correlation theory of brain function, we extend conventional neural network architectures by introducing additional dynamical variables. Assuming an oscillatory time structure of neural firing, i.e., the existence of neural clocks, we assign a so-called phase to each formal neuron. The phases explicitly describe detailed correlations of neural activities neglected in conventional neural network architectures. Implementing this extension into a simple self-organizing network based on a feature map, we present an associative memory that actually is capable of forming hypotheses of classification. Received: 6 December 1993/Accepted in revised form: 14 July 1994     

A self-organizing neural network with a function of associative memory: Feedback-type cognitron

Previously, one of the authors proposed a new hypothesis on the organization of synaptic connections, and constructed a model of self-organizing multi-layered neural network cognitron (Fukushima, 1975). the cognitron consists of a number of neural layers with similar structure connected in a cascade one after another. We have modified the structure of the cognitron, and have developed a new network having an ability of associative memory. The new network, named a feedback-type cognitron, has not only the feedforward connections as in the conventional cognitron, but also modifiable feedback connections from the last-layer cells to the front-layer ones. This network has been simulated on a digital computer. If several stimulus patterns are repeatedly presented to the network, the interconnections between the cells are gradually organized. The feedback connections, as well as the conventional feedforward ones, are self-organized depending on the characteristies of the externally presented stimulus patterns. After adequate number of stimulus presentations, each cell usually acquires the selective responsiveness to one of the stimulus patterns which have been frequently given. That is, every different stimulus pattern becomes to elicit an individual response to the network. After the completion of the self-organization, several stimulus patterns are presented to the network, and the responses are observed. Once a stimulus is given to the network, the signal keeps circulating in the network even after cutting off the stimulus, and the response gradually changes. Even though an imperfect or an ambiguous pattern is presented, the response usually converges to one of the patterns which have been frequently given during the process of self-organization. In some cases, however, a new pattern which has never been presented before, emerges. It is seen that this feedback-type cognitron has characteristics quite similar to some functions of the brain, such as the associative recall of memory, or the creation of a new idea by intuition.     

Sleep modulates the neural substrates of both spatial and contextual memory consolidation

It is known that sleep reshapes the neural representations that subtend the memories acquired while navigating in a virtual environment. However, navigation is not process-pure, as manifold learning components contribute to performance, notably the spatial and contextual memory constituents. In this context, it remains unclear whether post-training sleep globally promotes consolidation of all of the memory components embedded in virtual navigation, or rather favors the development of specific representations. Here, we investigated the effect of post-training sleep on the neural substrates of the consolidation of spatial and contextual memories acquired while navigating in a complex 3D, naturalistic virtual town. Using fMRI, we mapped regional cerebral activity during various tasks designed to tap either the spatial or the contextual memory component, or both, 72 h after encoding with or without sleep deprivation during the first post-training night. Behavioral performance was not dependent upon post-training sleep deprivation, neither in a natural setting that engages both spatial and contextual memory processes nor when looking more specifically at each of these memory representations. At the neuronal level however, analyses that focused on contextual memory revealed distinct correlations between performance and neuronal activity in frontal areas associated with recollection processes after post-training sleep, and in the parahippocampal gyrus associated with familiarity processes in sleep-deprived participants. Likewise, efficient spatial memory was associated with posterior cortical activity after sleep whereas it correlated with parahippocampal/medial temporal activity after sleep deprivation. Finally, variations in place-finding efficiency in a natural setting encompassing spatial and contextual elements were associated with caudate activity after post-training sleep, suggesting the automation of navigation. These data indicate that post-training sleep modulates the neural substrates of the consolidation of both the spatial and contextual memories acquired during virtual navigation.     

Interfering with theories of sleep and memory: sleep, declarative memory, and associative interference

Mounting behavioral evidence in humans supports the claim that sleep leads to improvements in recently acquired, nondeclarative memories. Examples include motor-sequence learning; visual-discrimination learning; and perceptual learning of a synthetic language. In contrast, there are limited human data supporting a benefit of sleep for declarative (hippocampus-mediated) memory in humans (for review, see). This is particularly surprising given that animal models (e.g.,) and neuroimaging studies (e.g.,) predict that sleep facilitates hippocampus-based memory consolidation. We hypothesized that we could unmask the benefits of sleep by challenging the declarative memory system with competing information (interference). This is the first study to demonstrate that sleep protects declarative memories from subsequent associative interference, and it has important implications for understanding the neurobiology of memory consolidation.     

Stability of dead zone bidirectional associative memory neural networks involving time delays

A mathematical model describing the dynamical interactions of bidirectional associative memory networks involving transmission delays is considered. The influence of a dead zone or a zone of noactivation on the global stability is investigated and various easily verifiable sets of sufficient conditions are established. The asymptotic nature of solutions when the given system of equations does not possess an equilibrium pattern is discussed.     

The neural substrates of memory suppression: a FMRI exploration of directed forgetting

The directed forgetting paradigm is frequently used to determine the ability to voluntarily suppress information. However, little is known about brain areas associated with information to forget. The present study used functional magnetic resonance imaging to determine brain activity during the encoding and retrieval phases of an item-method directed forgetting recognition task with neutral verbal material in order to apprehend all processing stages that information to forget and to remember undergoes. We hypothesized that regions supporting few selective processes, namely recollection and familiarity memory processes, working memory, inhibitory and selection processes should be differentially activated during the processing of to-be-remembered and to-be-forgotten items. Successful encoding and retrieval of items to remember engaged the entorhinal cortex, the hippocampus, the anterior medial prefrontal cortex, the left inferior parietal cortex, the posterior cingulate cortex and the precuneus; this set of regions is well known to support deep and associative encoding and retrieval processes in episodic memory. For items to forget, encoding was associated with higher activation in the right middle frontal and posterior parietal cortex, regions known to intervene in attentional control. Items to forget but nevertheless correctly recognized at retrieval yielded activation in the dorsomedial thalamus, associated with familiarity-based memory processes and in the posterior intraparietal sulcus and the anterior cingulate cortex, involved in attentional processes.     

Visual perception and imagery: A new molecular hypothesis

Here, we put forward a redox molecular hypothesis about the natural biophysical substrate of visual perception and visual imagery. This hypothesis is based on the redox and bioluminescent processes of neuronal cells in retinotopically organized cytochrome oxidase-rich visual areas. Our hypothesis is in line with the functional roles of reactive oxygen and nitrogen species in living cells that are not part of haphazard process, but rather a very strict mechanism used in signaling pathways. We point out that there is a direct relationship between neuronal activity and the biophoton emission process in the brain. Electrical and biochemical processes in the brain represent sensory information from the external world. During encoding or retrieval of information, electrical signals of neurons can be converted into synchronized biophoton signals by bioluminescent radical and non-radical processes. Therefore, information in the brain appears not only as an electrical (chemical) signal but also as a regulated biophoton (weak optical) signal inside neurons.     

Acetylcholine and associative memory in the piriform cortex

The significance of cholinergic modulation for associative memory performance in the piriform cortex was examined in a study combining cellular neurophysiology in brain slices with realistic biophysical network simulations. Three different physiological effects of acetylcholine were identified at the single-cell level: suppression of neuronal adaptation, suppression of synaptic transmission in the intrinsic fibers layer, and activity-dependent increase in synaptic strength. Biophysical simulations show how these three effects are joined together to enhance learning and recall performance of the cortical network. Furthermore, our data suggest that activity-dependent synaptic decay during learning is a crucial factor in determining learning capability of the cortical network. Accordingly, it is predicted that acetylcholine should also enhance long-term depression in the piriform cortex.     

On the storage capacity of an associative memory with randomly distributed storage elements

In an associative memory with randomly distributed storage elements at least 0.05 bit per storage element can be stored.     

Visual associative memory and the orientation-contingent color after-effect

The traditional explanation of the McCollough effect (ME) by selective adaptation of single detectors selective to color and orientation suffers from a number of inconsistencies: 1) the ME lasts much longer (from several days up to 3 months) than the ordinary adaptation, the decay of the effect being completely arrested by night sleep or occluding the eye for a long time; 2) the strength of the ME practically does not depend on the intensity of adapting light; and 3) a set of related pattern-contingent after-effects discovered later required for such an explanation new detectors, specific for other patterns. These properties can be explained, however, in the framework of associative memory and novelty filters. A computational model has been developed, which consists of 1) an input layer of two (left and right eyes) square matrices with two analog receptors (red and green) in each pixel, 2) an isomorphic associative neural layer, each analog neuron being synaptically connected with all receptors of both eyes, and 3) an output layer (novelty filter). The modification of synaptic efficacies conforms to the Hebb learning rule. The function of the model was examined by simulation. After a few presentations of colored gratings, the model displays the ME that is slowly destroyed by subsequent presentations of random pictures. With a sufficiently large receptor matrix, the effect lasts a thousand times longer than the period of adaptation. Continuous darkness does not change the strength of the effect. Like in real ME, the model does not display interocular transfer. The model can account for different pattern-contingent color after-effects without assuming any predetermined specific detectors. Such detectors are constructed in the course of adaptation to specific stimuli (gratings).     

Synaptic mechanisms of associative memory in the amygdala

Do associative learning and synaptic long-term potentiation (LTP) depend on the same cellular mechanisms? Recent work in the amygdala reveals that LTP and Pavlovian fear conditioning induce similar changes in postsynaptic AMPA-type glutamate receptors and that occluding these changes by viral-mediated overexpression of a dominant-negative GluR1 construct attenuates both LTP and fear memory in rats. Novel forms of presynaptic plasticity in the lateral nucleus may also contribute to fear memory formation, bolstering the connection between synaptic plasticity mechanisms and associative learning and memory.     

A model of associative memory in the brain

A model of associative memory for time varying spatial patterns is proposed and simulated on a digital computer. This is a network composed of many neuron-like elements, and shows an ability for associative memory similar to that of the brain.Suppose a number of sequences of spatial patterns are presented to this network, for example, 12345, ABC, and so on. Then, these patterns are memorized in the network. After that, if any part of one of these sequences, say 23, is presented to the circuit, the rest of the sequence, 45, is recalled following to it. It resembles to such a situation — if we hear a part of a melody which we have memorized in the past, the rest of the melody is recalled even after it is stopped half-way. Although the recalled patterns are not always 100% correct, they are not completely destroyed even if the presented patterns are imperfect.     

A neural model of surface perception: lightness, anchoring, and filling-in

A neural model is proposed of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models clarify how the brain can compute relative contrast. The anchored Filling-In Lightness Model (aFILM) clarifies how the brain 'anchors' lightness percepts to determine an absolute lightness scale that uses the full dynamic range of neurons. The model quantitatively simulates lightness anchoring properties (Articulation, Insulation, Configuration, Area Effect) and other lightness data (discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, Craik-O'Brien-Cornsweet illusion). The model clarifies how retinal processing stages achieve light adaptation and spatial contrast adaptation, and how cortical processing stages fill-in surface lightness using long-range horizontal connections that are gated by boundary signals. The new filling-in mechanism runs 1000 times faster than diffusion mechanisms of previous filling-in models.     

Trace feature map: a model of episodic associative memory

An approach to episodic associative memory is presented, which has several desirable properties as a human memory model. The design is based on topological feature map representation of data. An ordinary feature map is a classifier, mapping an input vector onto a topologically meaningful location on the map. A trace feature map, in addition, creates a memory trace on that location. The traces can be stored episodically in a single presentation, and retrieved with a partial cue. Nearby traces overlap, which results in plausible memory interference behavior. Performance degrades gracefully as the memory is overloaded. More recent traces are easier to recall as are traces that are unique in the memory.This research was supported in part by an ITA Foundation grant and by fellowships from the Academy of Finland, the Emil Aaltonen Foundation and the Foundation for the Advancement of Technology (Finland) when the author was at UCLA     

Towards understanding of the cortical network underlying associative memory

Declarative knowledge and experiences are represented in the association cortex and are recalled by reactivation of the neural representation. Electrophysiological experiments have revealed that associations between semantically linked visual objects are formed in neural representations in the temporal and limbic cortices. Memory traces are created by the reorganization of neural circuits. These regions are reactivated during retrieval and contribute to the contents of a memory. Two different types of retrieval signals are suggested as follows: automatic and active. One flows backward from the medial temporal lobe during the automatic retrieval process, whereas the other is conveyed as a top-down signal from the prefrontal cortex to the temporal cortex during the active retrieval process. By sending the top-down signal, the prefrontal cortex manipulates and organizes to-be-remembered information, devises strategies for retrieval and monitors the outcome. To further understand the neural mechanism of memory, the following two complementary views are needed: how the multiple cortical areas in the brain-wide network interact to orchestrate cognitive functions and how the properties of single neurons and their synaptic connections with neighbouring neurons combine to form local circuits and to exhibit the function of each cortical area. We will discuss some new methodological innovations that tackle these challenges.