Alister U. Nicol

Dynamics of a Memory Trace: Effects of Sleep on Consolidation

BACKGROUND There is evidence that sleep is important for memory consolidation, but the underlying neuronal changes are not well understood. We studied the effect of sleep modulation on memory and on neuronal activity in a memory system of the domestic chick brain after the learning process of imprinting. Neurons in this system become, through imprinting, selectively responsive to a training (imprinting) stimulus and so possess the properties of a memory trace. RESULTS The proportion of neurons responsive to the training stimulus reaches a maximum the day after training. We demonstrate that sleep is necessary for this maximum to be achieved, that sleep stabilizes the initially unstable, selective responses of neurons to the imprinting stimulus, and that for sleep to be effective, it must occur during a particular period of time after training. During this period, there is a time-dependent increase in EEG activity in the 5-6 Hz band, that is, in the lower range of the theta bandwidth. The effects of sleep disturbance on consolidation cannot be attributed to fatigue or to stress. CONCLUSIONS We establish that long-term trace consolidation requires sleep within a restricted period shortly after learning. Undisturbed sleep is necessary for the stabilization of long-term memory, measured at the behavioral and neuronal levels, and of long-term but not short-term neuronal responsiveness to the training stimulus.

Tracking memory's trace

There is strong converging evidence that the intermediate and medial part of the hyperstriatum ventrale of the chick brain is a memory store for information acquired through the learning process of imprinting. Neurons in this memory system come, through imprinting, to respond selectively to the imprinting stimulus (IS) neurons and so possess the properties of a memory trace. Therefore, the responses of the intermediate and medial part of the hyperstriatum ventrale neurons to a visual imprinting stimulus were determined before, during, and after training. Of the total recorded population, the proportions of IS neurons shortly after each of two 1-h training sessions were significantly higher (approximately 2 times) than the pretraining proportion. However, ≈4.5 h later this proportion had fallen significantly and did not differ significantly from the pretraining proportion. Nevertheless, ≈21.5 h after the end of training, the proportion of IS neurons was at its highest (approximately 3 times the pretraining level). No significant fluctuations occurred in the proportions of neurons responding to the alternative stimulus. In addition, nonmonotonic changes were found commonly in the activity of 230 of the neurons tracked individually from before training to shortly after the end of training. Thus the pattern of change in responsiveness both at the population level and at the level of individual neurons was highly nonmonotonic. Such a pattern of change is not consistent with simple models of memory based on synaptic strengthening to asymptote. A model is proposed that accounts for the changes in the population responses to the imprinting stimulus in terms of changes in the responses of individual neurons.

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

BackgroundHow oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes.ResultsFollowing learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance.ConclusionsFace discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.

Spike sorting based upon machine learning algorithms (SOMA)

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

The recognition memory of imprinting: biochemistry and electrophysiology

A restricted part of the intermediate and medial part of the hyperstriatum ventrale (IMHV) of the domestic chick forebrain is pivotal to the learning process of imprinting and is probably the site at which information about an imprinting stimulus is stored. A range of learning-related changes occur in the IMHV between 1 and 24 h after training. The earliest change described is in Fos-like immunoreactivity. There follow changes in phosphorylation of the protein kinase C substrate MARCKS, morphological changes in axospinous synapses, an increase in NMDA receptor number and increases in amounts of the major isoforms of the neural cell adhesion molecule and clathrin heavy chain. All but the change in Fos-immunopositivity occurs in the left, but not the right, IMHV. Insufficient nitric oxide synthase is available in the IMHV to support the hypothesis that nitric oxide is a retrograde messenger contributing to the effect on Fos-like immunoreactivity. In chicks anaesthetised approximately 24 h after imprinting training, the spontaneous mean neuronal firing rate is related to a preference score (a measure of learning). In unanaesthetised chicks 24 h after training, the responsiveness of some IMHV neurons is biassed specifically towards the imprinting stimulus.The responses of other neurons in the IMHV generalise across some features of the training stimulus, such as form or colour. Some neurons in the IMHV of unanaesthetised chicks are responsive to the distance of an imprinting stimulus from the chick; distance-sensitive neurons can be distinguished from distance-insensitive neurones by the action potential shape.

The correlation determinant in tests for synchronization in neuronal spike data

We present a statistical approach to the identification of correlated activity in multineuron spike data, based on the value of the correlation determinant. This approach is not compromised by the lack of independence often encountered in this kind of data. We illustrate our method by applying it both to simulated data and to data recorded from neurons in a forebrain region (intermediate medial mesopallium, IMM) of the behaving domestic chick and simultaneously from the corresponding contralateral region. There is no direct anatomical connection between the two sites, and the validity of this technique is strongly supported by the observation that when the test indicates significantly correlated activity for neurons within either hemisphere, this correlation is greatly reduced, and ultimately obliterated, by serial incorporation of activity from neurons in the opposite hemisphere. Since the value of individual correlation coefficients allied to the Bonferroni correction is often used as a diagnostic tool, we also present comparisons of that approach with our correlation determinant approach.

Is neuronal encoding of subject-object distance dependent on learning?

Recordings were made in the intermediate and medial part of the hyperstriatum ventrale of behaving domestic chicks which had been imprinted (trained) by being exposed to a training stimulus. Neurons were tested for responsiveness to the training stimulus and to an alternative stimulus at each of three distances (d = 0.5 m, 1 m, 2 m) from the chick. For responses to the training stimulus 24/78 (31%) responsive neurons were d-sensitive, i.e. responses changed with distance. For responses to the alternative stimulus, a similar proportion of neurons was d-sensitive (16/57, 28%). Six d-invariant neurons responded similarly at each distance: four to the training and two to the alternative stimulus. Thus no effect of learning on d-sensitive or d-invariant neuronal responsiveness was found.