A theory of rate coding control by intrinsic plasticity effects

In vivo low-intensity magnetic pulses durably alter neocortical neuron excitability and spontaneous activity

KEY POINTS

Repetitive transcranial magnetic stimulation (rTMS) is a promising technique to alleviate neurological and psychiatric disorders caused by alterations in cortical activity. Our knowledge of the cellular mechanisms underlying rTMS-based therapies remains limited. We combined in vivo focal application of low-intensity rTMS (LI-rTMS) to the rat somatosensory cortex with intracellular recordings of subjacent pyramidal neurons to characterize the effects of weak magnetic fields at single cell level. Ten minutes of LI-rTMS delivered at 10 Hz reliably evoked action potentials in cortical neurons during the stimulation period, and induced durable attenuation of their intrinsic excitability, synaptic activity, and spontaneous firing. These results help us better understand the mechanisms of weak magnetic stimulation and should allow optimizing the effectiveness of stimulation protocols for clinical use.

ABSTRACT

Magnetic brain stimulation is a promising treatment for neurological and psychiatric disorders. However, a better understanding of its effects at the individual neuron level is essential to improve its clinical application. We combined focal low-intensity repetitive transcranial magnetic stimulation (LI-rTMS) to the rat somatosensory cortex with intracellular recordings of subjacent pyramidal neurons in vivo. Continuous 10 Hz LI-rTMS reliably evoked firing at ∼4-5 Hz during the stimulation period and induced durable attenuation of synaptic activity and spontaneous firing in cortical neurons, through membrane hyperpolarization and a reduced intrinsic excitability. However, inducing firing in individual neurons by repeated intracellular current injection did not reproduce LI-rTMS effects on neuronal properties. These data provide novel understanding of mechanisms underlying magnetic brain stimulation showing that, in addition to inducing biochemical plasticity, even weak magnetic fields can activate neurons and enduringly modulate their excitability. Abstract figure legend We examined by means of in vivo intracellular recordings in the rodent the effects of low-intensity (10 mT) repetitive transcranial magnetic stimulation (LI-rTMS) on the functional properties of primary somatosensory cortex pyramidal neurons. After a baseline period, during which cortical spontaneous activity and excitability were measured (Pre), LI-rTMS was applied at 10 Hz for 10 minutes. Despite their low intensity, magnetic pulses reliably evoked action potentials in cortical neurons. Ten minutes of LI-rTMS induced a progressive and long-lasting hyperpolarization of the neuronal membrane and a marked decrease in cell firing rate (Post). This was associated with an altered intrinsic neuronal excitability, characterized by reduced membrane input resistance and increased minimal current required to induce neuronal firing. A portion of this figure was created with biorender.com. This article is protected by copyright. All rights reserved.

Inhibitory control of frontal metastability sets the temporal signature of cognition

Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

Loss of homeoprotein Msx1 and Msx2 leading to athletic and kinematic impairment related to the increasing neural excitability of neurons in aberrant neocortex in mice

Although homeoproteins Msx1 and Msx2, the cell-specific transcription regulators, have been proven to play multiple roles in the embryogenesis of bone, muscle and tooth, the functions and mechanisms of Msx1 and Msx2 in the development of the central nervous system of mice after birth are not clear because of the death of Msx1 and Msx1/2 germline-deleted embryo at late gestation of mouse. In current research, Nestin-Cre mice was introduced to generate the central nervous system-specific knockout mice (Nestin-Cre;Msx1,Msx2^fl/fl). We found that besides the falling of the body mass and the brain volume, the cortical tissue sections and staining showed the decreasing thickness of layer II-IV and declining number of vertebral cells in layer V resulting from Msx1/2 deletion. In addition, electrophysiological tests revealed the aberrant action potential parameters of deep pyramidal neurons in Nestin-Cre;Msx1,2^fl/fl mice, which may be related with the ethology impairment displayed in further experiments. We discovered Nestin-Cre;Msx1,2^fl/fl mice had severe impairment in their athletic ability and kinematic learning ability in rotate test, and exhibited hyperactivity in open-field test. Above all, our results revealed that deletion of homeoproteins Msx1 and Msx2 could lead to behavioral disorders and suggested that Msx1 and Msx2 played a crucial role in regulating the development and function of the neocortex. In addition, our current research provided a new mouse model for understanding the pathogenesis of human central nervous system disease.

Tune it in: mechanisms and computational significance of neuron-autonomous plasticity

The activity of a neural network is a result of synaptic signals that convey the communication between neurons and neuron-based intrinsic currents that determine the neuron’s input-output transfer function. Ample studies have demonstrated that cell-based excitability, and in particular intrinsic excitability, is modulated by learning and that these modifications play a key role in learning-related behavioral changes. The field of cell-based plasticity is largely growing, and it entails numerous experimental findings that demonstrate a large diversity of currents that are affected by learning. The diverse effect of learning on the neuron’s excitability emphasizes the need for a framework under which cell-based plasticity can be categorized to enable the assessment of the computational roles of the intrinsic modifications. We divide the domain of cell-based plasticity into three main categories, where the first category entails the currents that mediate the passive properties and single-spike generation, the second category entails the currents that mediate spike frequency adaptation, and the third category entails a novel learning-induced mechanism where all excitatory and inhibitory synapses double their strength. Curiously, this elementary division enables a natural categorization of the computational roles of these learning-induced plasticities. The computational roles are diverse and include modification of the neuronal mode of action, such as bursting, prolonged, and fast responsive; attention-like effect where the signal detection is improved; transfer of the network into an active state; biasing the competition for memory allocation; and transforming an environmental cue into a dominant cue and enabling a quicker formation of new memories.

Conditional Bistability, a Generic Cellular Mnemonic Mechanism for Robust and Flexible Working Memory Computations

Persistent neural activity, the substrate of working memory, is thought to emerge from synaptic reverberation within recurrent networks. However, reverberation models do not robustly explain the fundamental dynamics of persistent activity, including high-spiking irregularity, large intertrial variability, and state transitions. While cellular bistability may contribute to persistent activity, its rigidity appears incompatible with persistent activity labile characteristics. Here, we unravel in a cellular model a form of spike-mediated conditional bistability that is robust and generic. and provides a rich repertoire of mnemonic computations. Under asynchronous synaptic inputs of the awakened state, conditional bistability generates spiking/bursting episodes, accounting for the irregularity, variability, and state transitions characterizing persistent activity. This mechanism has likely been overlooked because of the subthreshold input it requires, and we predict how to assess it experimentally. Our results suggest a reexamination of the role of intrinsic properties in the collective network dynamics responsible for flexible working memory.SIGNIFICANCE STATEMENT This study unravels a novel form of intrinsic neuronal property: conditional bistability. We show that, thanks to its conditional character, conditional bistability favors the emergence of flexible and robust forms of persistent activity in PFC neural networks, in opposition to previously studied classical forms of absolute bistability. Specifically, we demonstrate for the first time that conditional bistability (1) is a generic biophysical spike-dependent mechanism of layer V pyramidal neurons in the PFC and that (2) it accounts for essential neurodynamical features for the organization and flexibility of PFC persistent activity (the large irregularity and intertrial variability of the discharge and its organization under discrete stable states), which remain unexplained in a robust fashion by current models.

Physical Therapy for Neurological Conditions in Geriatric Populations

With more of the world’s population surviving longer, individuals often face age-related neurology disorders and decline of function that can affect lifestyle and well-being. Despite neurophysiological changes affecting the brain function and structure, the aged brain, in some degree, can learn and relearn due to neuroplasticity. Recent advances in rehabilitation techniques have produced better functional outcomes in age-related neurological conditions. Physical therapy (PT) of the elderly individual focuses in particular on sensory–motor impairments, postural control coordination, and prevention of sarcopenia. Geriatric PT has a significant influence on quality of life, independent living, and life expectancy. However, in many developed and developing countries, the profession of PT is underfunded and understaffed. This article provides a brief overview on (a) age-related disease of central nervous system and (b) the principles, approaches, and doctrines of motor skill learning and point out the most common treatment models that PTs use for neurological patients.

Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons

Intense spiking response of a memory-pattern is believed to play a crucial role both in normal learning and pathology, where it can create biased behavior. We recently proposed a novel model for memory amplification where the simultaneous two-fold increase of all excitatory (AMPAR-mediated) and inhibitory (GABA_AR-mediated) synapses in a sub-group of cells that constitutes a memory-pattern selectively amplifies this memory. Here we confirm the cellular basis of this model by validating its major predictions in four sets of experiments, and demonstrate its induction via a whole-cell transduction mechanism. Subsequently, using theory and simulations, we show that this whole-cell two-fold increase of all inhibitory and excitatory synapses functions as an instantaneous and multiplicative amplifier of the neurons’ spiking. The amplification mechanism acts through multiplication of the net synaptic current, where it scales both the average and the standard deviation of the current. In the excitation-inhibition balance regime, this scaling creates a linear multiplicative amplifier of the cell’s spiking response. Moreover, the direct scaling of the synaptic input enables the amplification of the spiking response to be synchronized with rapid changes in synaptic input, and to be independent of previous spiking activity. These traits enable instantaneous real-time amplification during brief elevations of excitatory synaptic input. Furthermore, the multiplicative nature of the amplifier ensures that the net effect of the amplification is large mainly when the synaptic input is mostly excitatory. When induced on all cells that comprise a memory-pattern, these whole-cell modifications enable a substantial instantaneous amplification of the memory-pattern when the memory is activated. The amplification mechanism is induced by CaMKII dependent phosphorylation that doubles the conductance of all GABA_A and AMPA receptors in a subset of neurons. This whole-cell transduction mechanism enables both long-term induction of memory amplification when necessary and extinction when not further required.

Amplifying the strength of a neuronal assembly that underlies a behavioral choice can lead to a particularly long lasting dominant memory. We report experimental and theoretical evidence for a long-term mechanism that amplifies the response of a neuronal assembly which we termed “memory amplification mechanism”. The amplification mechanism is mediated by doubling the strength of all inhibitory and all excitatory synapses in the cell and is induced by whole-cell phosphorylation of all inhibitory and excitatory synaptic receptors in a subset of cells, via a process that is distinct from memory formation. Computationally, the inherent scaling of both excitation and inhibition yields a robust and stable amplifier of the neuron’s response. When such an amplifier is induced in a set of cells that compose a memory-pattern, it can selectively amplify the response of this memory. The memory amplification mechanism is independent from associative learning. Thus, while associative learning forms a memory that encodes new associations, the amplification mechanism can promote an already formed memory to a dominant memory.

Differential effects of static and dynamic inputs on neuronal excitability

The intrinsic excitability of neurons is known to be dynamically regulated by activity-dependent plasticity and homeostatic mechanisms. Such processes are commonly analyzed in the context of input-output functions that describe how neurons fire in response to constant levels of current. However, it is not well understood how changes of excitability as observed under static inputs translate to the function of the same neurons in their natural synaptic environment. Here we performed a computational study and hybrid experiments on rat bed nucleus of stria terminalis neurons to compare the two scenarios. The inward rectifying Kir current (IKir) and the hyperpolarization-activated cation current (Ih) were found to be considerably more effective in regulating the firing under synaptic inputs than under static stimuli. This prediction was experimentally confirmed by dynamic-clamp insertion of a synthetic inwardly rectifying Kir current into the biological neurons. At the same time, ionic currents that activate with depolarization were more effective regulating the firing under static inputs. When two intrinsic currents are concurrently altered such as those under homeostatic regulation, the effects in firing responses under static vs. dynamic inputs can be even more contrasting. Our results show that plastic or homeostatic changes of intrinsic membrane currents can shape the current step responses of neurons and their firing under synaptic inputs in a differential manner.

Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks

Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.