Adaptation occurs in a variety of forms in all sensory systems, motivating the question: what is its purpose? A productive approach has been to hypothesize that adaptation helps neural systems to efficiently encode stimuli whose statistics vary in time. To encode efficiently, a neural system must change its coding strategy, or computation, as the distribution of stimuli changes. Information theoretic methods allow this efficient coding hypothesis to be tested quantitatively. Empirically, adaptive processes occur over a wide range of timescales. On short timescales, underlying mechanisms include the contribution of intrinsic nonlinearities. Over longer timescales, adaptation is often power-law-like, implying the coexistence of multiple timescales in a single adaptive process. Models demonstrate that this can result from mechanisms within a single neuron.

Neural systems adapt to changes in stimulus statistics. However, it is not known how stimuli with complex temporal dynamics drive the dynamics of adaptation and the resulting firing rate. For single neurons, it has often been assumed that adaptation has a single time scale. Here, we show that single rat neocortical pyramidal neurons adapt with a time scale that depends on the time scale of changes in stimulus statistics. This multiple time scale adaptation is consistent with fractional order differentiation, such that the neuron’s firing rate is a fractional derivative of slowly varying stimulus parameters. Biophysically, even though neuronal fractional differentiation effectively yields adaptation with many time scales, we find that its implementation requires only a few, properly balanced known adaptive mechanisms. Fractional differentiation provides single neurons with a fundamental and general computation that can contribute to efficient information processing, stimulus anticipation, and frequency independent phase shifts of oscillatory neuronal firing.

Coupling of cerebral blood flow (CBF) and cerebral metabolic rate for oxygen (CMRO2) in physiologically activated brain states remains the subject of debates. Recently it was suggested that CBF is tightly coupled to oxidative metabolism in a nonlinear fashion. As part of this hypothesis, mathematical models of oxygen delivery to the brain have been described in which disproportionately large increases in CBF are necessary to sustain even small increases in CMRO2 during activation. We have explored the coupling of CBF and oxygen delivery by using two complementary methods. First, a more complex mathematical model was tested that differs from those recently described in that no assumptions were made regarding tissue oxygen level. Second, [ 15 O] water CBF positron emission tomography (PET) studies in nine healthy subjects were conducted during states of visual activation and hypoxia to examine the relationship of CBF and oxygen delivery. In contrast to previous reports, our model showed adequate tissue levels of oxygen could be maintained without the need for increased CBF or oxygen delivery. Similarly, the PET studies demonstrated that the regional increase in CBF during visual activation was not affected by hypoxia. These findings strongly indicate that the increase in CBF associated with physiological activation is regulated by factors other than local requirements in oxygen. It was long assumed that changes in cerebral blood flow (CBF) and in the cerebral metabolic rate of oxygen (CMRO 2 ) are tightly coupled in both resting and active brain states. This assumption resulted from the premises that the brain needs oxygen, that CBF is a main homeostatic factor for oxygen supply regulation, and that oxygen availability should be adjusted to meet tissue needs (1). It is known that the brain needs an abundant supply of oxygen and that, at rest, 80-92% of its ATP comes from oxidative metabolism of glucose. Early studies by Kety and Schmidt (2) and Cohen et al. (3) demonstrated that resting CBF does change with hypoxia and hyperoxia, thereby suggesting that CBF regulates oxygen delivery, although it was noted that blood oxygen levels but not tissue oxygen levels likely triggered these CBF changes (1). Therefore, if the cerebral oxygen supply was closely regulated to match tissue demands, then functional activation, which implies the need for additional ATP and oxygen, should cause a coupled increase in both CBF and CMRO 2 . However, two positron emission tomography (PET) studies conducted by Fox et al. (4,5) revealed that in humans large, stimulus-induced increases in CBF (Ϸ30% and 50%) were accompanied by only small increases in CMRO 2 (Ϸ5%). Others using PET and functional MRI confirmed these findings (6-10). The data indicated that, during short-term functional activation, CBF and CMRO 2 are not directly coupled.Recently, several reports using theoretical models suggested that the apparent uncoupling of CMRO 2 and CBF might actually be a tight nonlinear coupling. Mathematical models of oxygen delivery to the brai...

The posterior medial parietal cortex and left prefrontal cortex (PFC) have both been implicated in the recollection of past episodes. In a previous study, we found the posterior precuneus and left lateral inferior frontal cortex to be activated during episodic source memory retrieval. This study further examines the role of posterior precuneal and left prefrontal activation during episodic source memory retrieval using a similar source memory paradigm but with longer latency between encoding and retrieval. Our results suggest that both the precuneus and the left inferior PFC are important for regeneration of rich episodic contextual associations and that the precuneus activates in tandem with the left inferior PFC during correct source retrieval. Further, results suggest that the left ventro-lateral frontal region/ frontal operculum is involved in searching for task-relevant information (BA 47) and subsequent monitoring or scrutiny (BA 44/45) while regions in the dorsal inferior frontal cortex are important for information selection (BA 45/46).

The role of irregular cortical firing in neuronal computation is still debated, and it is unclear how signals carried by fluctuating synaptic potentials are decoded by downstream neurons. We examined in vitro frequency versus current ( f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. Studies in the somatosensory cortex show that L5 neurons become insensitive to input fluctuations as input mean increases and that their f-I response becomes linear. In contrast, our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level.

Adaptive processes over many timescales endow neurons with sensitivity to stimulus changes over a similarly wide range of scales. Although spike timing of single neurons can precisely signal rapid fluctuations in their inputs, the mean firing rate can convey information about slower-varying properties of the stimulus. Here, we investigate the firing rate response to a slowly varying envelope of whisker motion in two processing stages of the rat vibrissa pathway. The whiskers of anesthetized rats were moved through a noise trajectory with an amplitude that was sinusoidally modulated at one of several frequencies. In thalamic neurons, we found that the rate response to the stimulus envelope was also sinusoidal, with an approximately frequency-independent phase advance with respect to the input. Responses in cortex were similar but with a phase shift that was about three times larger, consistent with a larger amount of rate adaptation. These response properties can be described as a linear transformation of the input for which a single parameter quantifies the phase shift as well as the degree of adaptation. These results are reproduced by a model of adapting neurons connected by synapses with short-term plasticity, showing that the observed linear response and phase lead can be built up from a network that includes a sequence of nonlinear adapting elements. Our study elucidates how slowly varying envelope information under passive stimulation is preserved and transformed through the vibrissa processing pathway.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Conclusions | Based on several million athlete exposures, this meta-analysis found no association between concussion incidence at mild altitude compared with at sea level. The myth that higher altitude reduces concussion incidence in athletes is not supported.