Gabriele Scheler

Gabriele Scheler

In this position paper, we present a biological account of neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We believe that traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. In standard accounts, a neuronal network consists of a network of neurons connected by adaptive transmission links. Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. In contrast to this, we propose a paradigm switch from a synapse-centric model to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by associative coupling strength. There is filtering and selection of signals for processing and storage. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure.

Gabriele Scheler

In this position paper, we present biological detail about neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We believe that traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. In standard accounts, a neuronal network consists of a network of neurons connected by adaptive transmission links. The adaptation of these transmission links is overly simplified in the standard model of short-term and long-term potentiation or depression assuming weight adaptation according to use. We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane). Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each neuron in the horizontal network has its own internal memory. Transmission and memory are separate, not linked by strict use-dependence. There is filtering and selection of signals for processing and storage. Not every transmission event leaves a trace. This is a conceptual advance over synaptic weight models. The neuron is a self-programming device, rather than a transfer function determined by input. A new approach to neural modeling is better able to capture experimental evidence than synapse-centric models.

Gabriele Scheler, Jean-Marc Fellous

Recent electrophysiological experiments have shown that dopamine (D1) modulation of pyramidal cells in prefrontal cortex reduces spike frequency adaptation and enhances NMDA transmission. Using four models, from multicompartmental to integrate and fire, we examine the effects of these modulations on sustained (delay) activity in a reverberatory network. We find that D1 modulation may enable robust network bistability yielding selective reverberation among cells that code for a particular item or location. We further show that the tuning curve of such cells is sharpened, and that signal-to-noise ratio is increased. We postulate that D1 modulation affects the tuning of "memory fields" and yield efficient distributed dynamic representations.

Gabriele Scheler

In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.