Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken

Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model.

How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an "activity set point" in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well as intrinsic conductances can be regulated to make a self-assembling central pattern generator network; thus, network-level homeostasis can emerge from cell-autonomous regulation rules. Finally, we demonstrate that the outcome of homeostatic regulation depends on the complement of ion channels expressed in cells: in some cases, loss of specific ion channels can be compensated; in others, the homeostatic mechanism itself causes pathological loss of function.Charles A. King TrustThis is the final version of the article. It first appeared from Cell Press (Elsevier) via http://dx.doi.org/10.1016/j.neuron.2014.04.002

Emergence of sequential dynamical invariants in central pattern generators from auto-organized constraints in their sequence time intervals

Motor coordination by the nervous system involves generating and coordinating muscle sequential activity. Such activity must be robust to produce effective motion that relies on patterned motor events ordered in a specific sequence and, at the same time, flexible in timing and duration to adapt to intrinsic and environmental circumstances. Motor neural circuits produce the signaling that muscles use for their sequential action. Even in rhythmic repetitive motion such as walking, breathing and chewing, variability in the timing of the events that build the sequence can be observed within the overall ordered rhythm of the motion. In this context, central pattern generators (CPGs) are key neural circuits to study the cycle-by-cycle balance between the robustness of the sequential order of neural events and the irregularity of the intracycle intervals that shape a rhythmic sequence. Recently, the presence of sequential dynamical invariants in the form of robust cycle-by-cycle relationships between specific sequence time intervals was unveiled in living CPGs. Variability of other sequence intervals remains unrelated and coexists with the presence of the invariants. Sequential dynamical invariants have been proposed to underlie the cycle-by-cycle CPG coordination. In this work, we used a minimal CPG circuit building block of two interacting inhibitory model neurons to assess the emergence of coordinated variability in sequential neural activity. We studied the conditions that produce sequential dynamical invariants in the circuit using intrinsically irregular neuron models and symmetric and asymmetric network topologies. We quantified the circuit irregularity and related it to the presence of invariants under different configurations. We found the conditions to propagate and sustain coordinated variability between time intervals, and thus our study illustrates the auto-organized constraint-based mechanisms for motor sequence coordination that shape sequential dynamical invariants. This is the first model study in which sequential dynamical invariants are observed considering only the intrinsic variability of CPG neurons without external stimuli. Finally, we discuss possible applications of this research in the context of autonomous robotics and artificial intelligencePID2021-122347NB-I0

Balanced Plasticity and Stability of the Electrical Properties of a Molluscan Modulatory Interneuron after Classical Conditioning: A Computational Study

The Cerebral Giant Cells (CGCs) are a pair of identified modulatory interneurons in the Central Nervous System of the pond snail Lymnaea stagnalis with an important role in the expression of both unconditioned and conditioned feeding behavior. Following single-trial food-reward classical conditioning, the membrane potential of the CGCs becomes persistently depolarized. This depolarization contributes to the conditioned response by facilitating sensory cell to command neuron synapses, which results in the activation of the feeding network by the conditioned stimulus. Despite the depolarization of the membrane potential, which enables the CGGs to play a key role in learning-induced network plasticity, there is no persistent change in the tonic firing rate or shape of the action potentials, allowing these neurons to retain their normal network function in feeding. In order to understand the ionic mechanisms of this novel combination of plasticity and stability of intrinsic electrical properties, we first constructed and validated a Hodgkin-Huxley-type model of the CGCs. We then used this model to elucidate how learning-induced changes in a somal persistent sodium and a delayed rectifier potassium current lead to a persistent depolarization of the CGCs whilst maintaining their firing rate. Including in the model an additional increase in the conductance of a high-voltage-activated calcium current allowed the spike amplitude and spike duration also to be maintained after conditioning. We conclude therefore that a balanced increase in three identified conductances is sufficient to explain the electrophysiological changes found in the CGCs after classical conditioning

Homeostatic Scaling of Excitability in Recurrent Neural Networks

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity

Binocular Encoding in the Damselfly Pre-motor Target Tracking System.

Akin to all damselflies, Calopteryx (family Calopterygidae), commonly known as jewel wings or demoiselles, possess dichoptic (separated) eyes with overlapping visual fields of view. In contrast, many dragonfly species possess holoptic (dorsally fused) eyes with limited binocular overlap. We have here compared the neuronal correlates of target tracking between damselfly and dragonfly sister lineages and linked these changes in visual overlap to pre-motor neural adaptations. Although dragonflies attack prey dorsally, we show that demoiselles attack prey frontally. We identify demoiselle target-selective descending neurons (TSDNs) with matching frontal visual receptive fields, anatomically and functionally homologous to the dorsally positioned dragonfly TSDNs. By manipulating visual input using eyepatches and prisms, we show that moving target information at the pre-motor level depends on binocular summation in demoiselles. Consequently, demoiselles encode directional information in a binocularly fused frame of reference such that information of a target moving toward the midline in the left eye is fused with information of the target moving away from the midline in the right eye. This contrasts with dragonfly TSDNs, where receptive fields possess a sharp midline boundary, confining responses to a single visual hemifield in a sagittal frame of reference (i.e., relative to the midline). Our results indicate that, although TSDNs are conserved across Odonata, their neural inputs, and thus the upstream organization of the target tracking system, differ significantly and match divergence in eye design and predatory strategies. VIDEO ABSTRACT

Preserving axosomatic spiking features despite diverse dendritic morphology

Throughout the nervous system, cells belonging to a certain electrical class (e-class)-sharing high similarity in firing response properties-may nevertheless have widely variable dendritic morphologies. To quantify the effect of this morphological variability on the firing of layer 5 thick-tufted pyramidal cells (TTCs), a detailed conductance-based model was constructed for a three-dimensional reconstructed exemplar TTC. The model exhibited spike initiation in the axon and reproduced the characteristic features of individual spikes, as well as of the firing properties at the soma, as recorded in a population of TTCs in young Wistar rats. When using these model parameters over the population of 28 three-dimensional reconstructed TTCs, both axonal and somatic ion channel densities had to be scaled linearly with the conductance load imposed on each of these compartments. Otherwise, the firing of model cells deviated, sometimes very significantly, from the experimental variability of the TTC e-class. The study provides experimentally testable predictions regarding the coregulation of axosomatic membrane ion channels density for cells with different dendritic conductance load, together with a simple and systematic method for generating reliable conductance-based models for the whole population of modeled neurons belonging to a particular e-class, with variable morphology as found experimentally