Synaptic connectivity defines groups of neurons that engage in correlated activity during specific functional tasks. These co-active groups of neurons form ensembles, the operational units involved in, for example, sensory perception, motor coordination and memory (then called an engram). Traditionally, ensemble formation has been thought to occur via strengthening of synaptic connections via long-term potentiation (LTP) as a plasticity mechanism. This synaptic theory of memory arises from the learning rules formulated by Hebb and is consistent with many experimental observations. Here, we propose, as an alternative, that the intrinsic excitability of neurons and its plasticity constitute a second, non-synaptic mechanism that could be important for the initial formation of ensembles. Indeed, enhanced neural excitability is widely observed in multiple brain areas subsequent to behavioral learning. In cortical structures and the amygdala, excitability changes are often reported as transient, even though they can last tens of minutes to a few days. Perhaps it is for this reason that they have been traditionally considered as modulatory, merely supporting ensemble formation by facilitating LTP induction, without further involvement in memory function (memory allocation hypothesis). We here suggest-based on two lines of evidence-that beyond modulating LTP allocation, enhanced excitability plays a more fundamental role in learning. First, enhanced excitability constitutes a signature of active ensembles and, due to it, subthreshold synaptic connections become suprathreshold in the absence of synaptic plasticity (iceberg model). Second, enhanced excitability promotes the propagation of dendritic potentials toward the soma and allows for enhanced coupling of EPSP amplitude (LTP) to the spike output (and thus ensemble participation). This permissive gate model describes a need for permanently increased excitability, which seems at odds with its traditional consideration as a short-lived mechanism. We propose that longer modifications in excitability are made possible by a low threshold for intrinsic plasticity induction, suggesting that excitability might be on/off-modulated at short intervals. Consistent with this, in cerebellar Purkinje cells, excitability lasts days to weeks, which shows that in some circuits the duration of the phenomenon is not a limiting factor in the first place. In our model, synaptic plasticity defines the information content received by neurons through the connectivity network that they are embedded in. However, the plasticity of cell-autonomous excitability could dynamically regulate the ensemble participation of individual neurons as well as the overall activity state of an ensemble.

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired \"self-repairing\" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural \"evolution.\" We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors\' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.

Intrinsic plasticity, a fundamental process enabling neurons to modify their intrinsic properties, plays a crucial role in shaping neuronal input-output function and is implicated in various neurological and psychiatric disorders. Despite its importance, the underlying molecular mechanisms of intrinsic plasticity remain poorly understood. In this study, a new ubiquitin ligase adaptor, protein tyrosine phosphatase receptor type N (PTPRN), is identified as a regulator of intrinsic neuronal excitability in the context of temporal lobe epilepsy. PTPRN recruits the NEDD4 Like E3 Ubiquitin Protein Ligase (NEDD4L) to NaV1.2 sodium channels, facilitating NEDD4L-mediated ubiquitination, and endocytosis of NaV1.2. Knockout of PTPRN in hippocampal granule cells leads to augmented NaV1.2-mediated sodium currents and higher intrinsic excitability, resulting in increased seizure susceptibility in transgenic mice. Conversely, adeno-associated virus-mediated delivery of PTPRN in the dentate gyrus region decreases intrinsic excitability and reduces seizure susceptibility. Moreover, the present findings indicate that PTPRN exerts a selective modulation effect on voltage-gated sodium channels. Collectively, PTPRN plays a significant role in regulating intrinsic excitability and seizure susceptibility, suggesting a potential strategy for precise modulation of NaV1.2 channels\' function.

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

Despite its uniform appearance, the cerebellar cortex is highly heterogeneous in terms of structure, genetics and physiology. Purkinje cells (PCs), the principal and sole output neurons of the cerebellar cortex, can be categorized into multiple populations that differentially express molecular markers and display distinctive physiological features. Such features include action potential rate, but also their propensity for synaptic and intrinsic plasticity. However, the precise molecular and genetic factors that correlate with the differential physiological properties of PCs remain elusive. In this article, we provide a detailed overview of the cellular mechanisms that regulate PC activity and plasticity. We further perform a pathway analysis to highlight how molecular characteristics of specific PC populations may influence their physiology and plasticity mechanisms.

To perform computations with the efficiency necessary for animal survival, neocortical microcircuits must be capable of reconfiguring in response to experience, while carefully regulating excitatory and inhibitory connectivity to maintain stable function. This dynamic fine-tuning is accomplished through a rich array of cellular homeostatic plasticity mechanisms that stabilize important cellular and network features such as firing rates, information flow, and sensory tuning properties. Further, these functional network properties can be stabilized by different forms of homeostatic plasticity, including mechanisms that target excitatory or inhibitory synapses, or that regulate intrinsic neuronal excitability. Here we discuss which aspects of neocortical circuit function are under homeostatic control, how this homeostasis is realized on the cellular and molecular levels, and the pathological consequences when circuit homeostasis is impaired. A remaining challenge is to elucidate how these diverse homeostatic mechanisms cooperate within complex circuits to enable them to be both flexible and stable.

Magnetic fields are being used for detailed anatomical and functional examination of the human brain. In addition, evidence for their efficacy in treatment of brain dysfunctions is accumulating. Transcranial static magnetic field stimulation (tSMS) is a recently developed technique for noninvasively modifying brain functions. In tSMS, a strong and small magnet when placed over the skull can temporarily suppress brain functions. Its modulatory effects persist beyond the time of stimulation. However, the neurophysiological mechanisms underlying tSMS-induced plasticity remain unclear. Here, using acute motor cortical slice preparation obtained from male C57BL/6N mice, we show that tSMS alters the intrinsic electrical properties of neurons by altering the activity of chloride (Cl-) channels in neurons. Exposure of mouse pyramidal neurons to a static magnetic field (SMF) at a strength similar to human tSMS temporarily decreased their excitability and induced transient neuronal swelling. The effects of SMF were blocked by DIDS and GlyH-101, but not by NPPB, consistent with the pharmacological profile of SLC26A11, a transporter protein with Cl- channel activity. Whole-cell voltage-clamp recordings of the GlyH-101-sensitive Cl- current component showed significant enhancement of the component at both subthreshold and depolarized membrane potentials after SMF application, resulting in shunting inhibition and reduced repetitive action potential (AP) firing at the respective potentials. Thus, this study provides the first neurophysiological evidence for the inhibitory effect of tSMS on neuronal activity and advances our mechanistic understanding of noninvasive human neuromodulation.

Bidirectional homeostatic plasticity allows neurons and circuits to maintain stable firing in the face of developmental or learning-induced perturbations. In the primary visual cortex (V1), upward firing rate homeostasis (FRH) only occurs during active wake (AW) and downward during sleep, but how this behavioral state-dependent gating is accomplished is unknown. Here, we focus on how AW enables upward FRH in V1 of juvenile Long Evans rats. A major difference between quiet wake (QW), when upward FRH is absent, and AW, when it is present, is increased cholinergic (ACh) tone, and the main cholinergic projections to V1 arise from the horizontal diagonal band of the basal forebrain (HDB ACh). We therefore chemogenetically inhibited HDB ACh neurons while inducing upward homeostatic compensation using direct activity-suppression in V1. We found that synaptic scaling up and intrinsic homeostatic plasticity, two important cellular mediators of upward FRH, were both impaired when HDB ACh neurons were inhibited. Most strikingly, HDB ACh inhibition flipped the sign of intrinsic plasticity so that it became anti-homeostatic, and this effect was phenocopied by knockdown of the M1 ACh receptor in V1, indicating that this modulation of intrinsic plasticity is the result of direct actions of ACh within V1. Finally, we found that upward FRH induced by visual deprivation was completely prevented by HDB ACh inhibition. Together, our results show that HDB ACh modulation is a key enabler of upward homeostatic plasticity and FRH, and more broadly suggest that neuromodulatory inputs can segregate upward and downward homeostatic plasticity into distinct behavioral states.

This article supports the relevance of modeling new bioinspired properties in rate-coding artificial neurons, focusing on fundamental neural properties rarely implemented thus far in artificial neurons, such as intrinsic plasticity, the metaplasticity of synaptic strength, and the lateral inhibition of neighborhood neurons. All these properties are bioinspired through empirical models developed by neurologists, and this in turn contributes to taking perceptrons to a higher potential level. Metaplasticity and intrinsic plasticity are different levels of plasticity and are believed by neurologists to have fundamental roles in memory and learning and therefore in the performance of neurons. Assuming that information about stimuli is contained in the firing rate of the connections among biological neurons, several models of artificial implementation have been tested. Analyzing their results and comparing them with learning and performance of state-of-the-art models, relevant advances are made in the context of the developing Industrial Revolution 4.0 based on advances in Machine Learning, and they may even initiate a new generation of artificial neural networks. As an example, a single-layer perceptron that includes the proposed advances is successfully trained to perform the XOR function, called the Competitive Perceptron, which is a new bioinspired artificial neuronal model with the potential of non-linear separability, continuous learning, and scalability, which is suitable to build efficient Deep Networks, overcoming the basic limitations of traditional perceptrons that have challenged scientists for half a century.

What physiological role does a slow hyperpolarization-activated ion channel with mixed cation selectivity play in the fast world of neuronal action potentials that are driven by depolarization? That puzzling question has piqued the curiosity of physiology enthusiasts about the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which are widely expressed across the body and especially in neurons. In this review, we emphasize the need to assess HCN channels from the perspective of how they respond to time-varying signals, while also accounting for their interactions with other co-expressing channels and receptors. First, we illustrate how the unique structural and functional characteristics of HCN channels allow them to mediate a slow negative feedback loop in the neurons that they express in. We present the several physiological implications of this negative feedback loop to neuronal response characteristics including neuronal gain, voltage sag and rebound, temporal summation, membrane potential resonance, inductive phase lead, spike triggered average, and coincidence detection. Next, we argue that the overall impact of HCN channels on neuronal physiology critically relies on their interactions with other co-expressing channels and receptors. Interactions with other channels allow HCN channels to mediate intrinsic oscillations, earning them the \"pacemaker channel\" moniker, and to regulate spike frequency adaptation, plateau potentials, neurotransmitter release from presynaptic terminals, and spike initiation at the axonal initial segment. We also explore the impact of spatially non-homogeneous subcellular distributions of HCN channels in different neuronal subtypes and their interactions with other channels and receptors. Finally, we discuss how plasticity in HCN channels is widely prevalent and can mediate different encoding, homeostatic, and neuroprotective functions in a neuron. In summary, we argue that HCN channels form an important class of channels that mediate a diversity of neuronal functions owing to their unique gating kinetics that made them a puzzle in the first place.