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.

Memory from the dynamics of intrinsic membrane currents

Almost all theoretical and experimental studies of the mechanisms underlying learning and memory focus on synaptic efficacy and make the implicit assumption that changes in synaptic efficacy are both necessary and sufficient to account for learning and memory. However, network dynamics depends on the complex interaction between intrinsic membrane properties and synaptic strengths and time courses. Furthermore, neuronal activity itself modifies not only synaptic efficacy but also the intrinsic membrane properties of neurons. This paper presents examples demonstrating that neurons with complex temporal dynamics can provide short-term “memory” mechanisms that rely solely on intrinsic neuronal properties. Additionally, we discuss the potential role that activity may play in long-term modification of intrinsic neuronal properties. While not replacing synaptic plasticity as a powerful learning mechanism, these examples suggest that memory in networks results from an ongoing interplay between changes in synaptic efficacy and intrinsic membrane properties.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.