Biology subjects

Gidon, A.

Publications and source records attributed to Gidon, A..

3 recordsLinked to original sources

bioRxiv2021

Biological complexity facilitates tuning of the neuronal parameter space

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability. Significance statementOver the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands of searching for viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with fewer ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for this improved distribution of valid model parameters, we propose that this may generalise to evolutionary selection in other complex biological systems. In briefStudying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but because of their complexity. HighlightsO_LI15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour. C_LIO_LIBut knocking out ion channels can be compensated only in the full model. C_LIO_LIRandom sampling leads to ~ 6% solutions in full but only ~ 1% in reduced model. C_LIO_LILaw of large numbers generalises our observations to other complex biological systems. C_LI

neuroscience↗

bioRxiv2021

Burst coding despite unimodal interval distributions

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how identifiable spike-timing patterns have to be to preserve potent transmission of information. Should we expect that neurons avoid ambiguous patterns that are neither clearly bursts nor isolated spikes? We addressed these questions using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were also unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

neuroscience↗

bioRxiv2020

Mitochondrial dysfunction in skeletal muscle of fukutin deficient mice is resistant to exercise- and AICAR-induced rescue

Disruptions in the dystrophin-glycoprotein complex (DGC) are clearly the primary basis underlying various forms of muscular dystrophies and dystroglycanopathies, but the cellular consequences of DGC disruption are still being investigated. Mitochondrial abnormalities are becoming an apparent consequence and contributor to dystrophy disease pathology. Herein, we demonstrate that muscle-specific deletion of the fukutin gene [Myf5/fktn-KO mice (KO)], a model of secondary dystroglycanopathy, results in ~30% lower muscle strength (P<0.001) and 16% lower mitochondrial function (P=0.002) compared to healthy littermate controls (LM). We also observed ~80% lower PGC-1 signaling (P=0.004), a primary transcription factor for mitochondrial biogenesis, in KO mice that likely contributes to the mitochondrial defects. PGC-1 is post-translationally regulated via phosphorylation by AMPK. Treatment with the AMPK agonist AICAR (5-aminoimidazole-4-carboxamide ribonucleotide) failed to rescue mitochondrial deficits in KO mice (P=0.458) but did have beneficial (~30% greater) effects on recovery of muscle contractility following injury in both LM and KO mice compared to saline treatment (P=0.006). The beneficial effects of AMPK stimulation via AICAR on muscle function may be partially explained by AMPKs other role of regulating skeletal muscle autophagy, a cellular process critical for clearance of damaged and/or dysfunctional organelles. Two primary conclusions can be drawn from this data, 1) fukutin deletion produces intrinsic muscular metabolic defects that likely contribute to dystroglycanopathy disease pathology, and 2) AICAR treatment accelerates recovery of muscle function following injury suggesting AMPK signaling as a possible target for therapeutic strategies.

physiology↗