Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity.

Neuromodulators have been shown to alter the temporal profile of short-term synaptic plasticity (STP); however, the computational function of this neuromodulation remains unexplored. Here, we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained recurrent neural networks that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. Neuromodulation of STP produced temporal and spatial scaling of the learned dynamics and enhanced temporal or spatial generalization compared to standard training of the synaptic weights in the absence of STP. The model also accounted for the results of two experimental studies involving flexible sensorimotor timing. Neuromodulation of STP provides a unified and biologically plausible mechanism to control the temporal and spatial scales of neural dynamics and sensorimotor behaviors.

Multiplexing working memory and time in the trajectories of neural networks.

Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

Neural population clocks: Encoding time in dynamic patterns of neural activity.

The ability to predict and prepare for near- and far-future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing, the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that, on the scale of seconds, timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represents in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Encoding time in neural dynamic regimes with distinct computational tradeoffs.

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise-and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

Neural Sequences as an Optimal Dynamical Regime for the Readout of Time.

Converging evidence suggests that the brain encodes time through dynamically changing patterns of neural activity, including neural sequences, ramping activity, and complex spatiotemporal dynamics. However, the potential computational significance and advantage of these different regimes have remained unaddressed. We combined large-scale recordings and modeling to compare population dynamics between premotor cortex and striatum in mice performing a two-interval timing task. Conventional decoders revealed that the dynamics within each area encoded time equally well; however, the dynamics in striatum exhibited a higher degree of sequentiality. Analysis of premotor and striatal dynamics, together with a large set of simulated prototypical dynamical regimes, revealed that regimes with higher sequentiality allowed a biologically constrained artificial downstream network to better read out time. These results suggest that, although different strategies exist for encoding time in the brain, neural sequences represent an ideal and flexible dynamical regime for enabling downstream areas to read out this information.

Synaptic Excitatory-Inhibitory Balance Underlying Efficient Neural Coding.

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Synaptic E-I Balance Underlies Efficient Neural Coding.

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Odor Experience Facilitates Sparse Representations of New Odors in a Large-Scale Olfactory Bulb Model.

Prior odor experience has a profound effect on the coding of new odor inputs by animals. The olfactory bulb, the first relay of the olfactory pathway, can substantially shape the representations of odor inputs. How prior odor experience affects the representation of new odor inputs in olfactory bulb and its underlying network mechanism are still unclear. Here we carried out a series of simulations based on a large-scale realistic mitral-granule network model and found that prior odor experience not only accelerated formation of the network, but it also significantly strengthened sparse responses in the mitral cell network while decreasing sparse responses in the granule cell network. This modulation of sparse representations may be due to the increase of inhibitory synaptic weights. Correlations among mitral cells within the network and correlations between mitral network responses to different odors decreased gradually when the number of prior training odors was increased, resulting in a greater decorrelation of the bulb representations of input odors. Based on these findings, we conclude that the degree of prior odor experience facilitates degrees of sparse representations of new odors by the mitral cell network through experience-enhanced inhibition mechanism.

Effects of SKF83959 on the excitability of hippocampal CA1 pyramidal neurons: a modeling study.

AIM: 3-Methyl-6-chloro-7,8-hydroxy-1-(3-methylphenyl)-2,3,4,5-tetrahydro-1H-3-benzazepine (SKF83959) have been shown to affect several types of voltage-dependent channels in hippocampal pyramidal neurons. The aim of this study was to determine how modulation of a individual type of the channels by SKF83959 contributes to the overall excitability of CA1 pyramidal neurons during either direct current injections or synaptic activation. METHODS: Rat hippocampal slices were prepared. The kinetics of voltage-dependent Na(+) channels and neuronal excitability and depolarization block in CA1 pyramidal neurons were examined using whole-cell recording. A realistic mathematical model of hippocampal CA1 pyramidal neuron was used to simulate the effects of SKF83959 on neuronal excitability. RESULTS: SKF83959 (50 µmol/L) shifted the inactivation curve of Na(+) current by 10.3 mV but had no effect on the activation curve in CA1 pyramidal neurons. The effects of SKF83959 on passive membrane properties, including a decreased input resistance and depolarized resting potential, predicted by our simulations were in agreement with the experimental data. The simulations showed that decreased excitability of the soma by SKF83959 (examined with current injection at the soma) was only observed when the membrane potential was compensated to the control levels, whereas the decreased dendritic excitability (examined with current injection at the dendrite) was found even without membrane potential compensation, which led to a decreased number of action potentials initiated at the soma. Moreover, SKF83959 significantly facilitated depolarization block in CA1 pyramidal neurons. SKF83959 decreased EPSP temporal summation and, of physiologically greater relevance, the synaptic-driven firing frequency. CONCLUSION: SKF83959 decreased the excitability of CA1 pyramidal neurons even though the drug caused the membrane potential depolarization. The results may reveal a partial mechanism for the drug's anti-Parkinsonian effects and may also suggest that SKF83959 has a potential antiepileptic effect.

Molecular modeling of the 3D structure of 5-HT(1A)R: discovery of novel 5-HT(1A)R agonists via dynamic pharmacophore-based virtual screening.

The serotonin receptor subtype 1A (5-HT(1A)R) has been implicated in several neurological conditions, and potent 5-HT(1A)R agonists have therapeutic potential for the treatment of depression, anxiety, schizophrenia, and Parkinson's disease. In the present study, a homology model of 5-HT(1A)R was built based on the latest released high-resolution crystal structure of the ß2AR in its active state (PDB: 3SN6). A dynamic pharmacophore model, which takes the receptor flexibility into account, was constructed, validated, and applied to our dynamic pharmacophore-based virtual screening approach with the aim to identify potential 5-5-HT(1A)R agonists. The obtained hits were subjected to 55-HT(1A)R binding and functional assays, and 10 compounds with medium or high K(i) and EC50 values were identified. Among them, FW01 (K(i) = 51.9 nM, EC50 = 7 nM) was evaluated as the strongest agonist for 5-HT(1A)R. The active 5-HT(1A)R model and dynamic pharmacophore model obtained from this study can be used for future discovery and design of novel 5-HT(1A)R agonists. Also, by integrating all computational and available experimental data, a stepwise 5-HT(1A)R signal transduction model induced by agonist FW01 was proposed.

SKF83959 suppresses excitatory synaptic transmission in rat hippocampus via a dopamine receptor-independent mechanism.

Dopamine (DA) profoundly modulates excitatory synaptic transmission and synaptic plasticity in the brain. In the present study the effects of SKF83959, the selective agonist of phosphatidylinositol (PI)-linked D(1) -like receptor, on the excitatory synaptic transmission were investigated in rat hippocampus. SKF83959 (10-100 µM) reversibly suppressed the field excitatory postsynaptic potential (fEPSP) elicited by stimulating the Schaffer's collateral-commissural fibers in CA1 area of hippocampal slices. However, the inhibition was not blocked by the D(1) receptor antagonist SCH23390, the D(2) receptor antagonist raclopride, the 5-HT(2A/2C) receptor antagonist mesulergine, or the α(1) -adrenoceptor antagonist prazosin. In addition, SKF83959 inhibited the afferent volley and significantly reduced the paired-pulse facilitation ratios. In dissociated hippocampal CA1 pyramidal neurons, SKF83959 had no detectable effect on glutamate-induced currents but potently inhibited voltage-activated Na(+) current (IC50 value = 26.9 ± 1.0 µM), which was not blocked by SCH23390 or by intracellular dialysis of GDP-ß-S. These results demonstrate that SKF83959 suppressed the excitatory synaptic transmission in hippocampal CA1 area, which was independent of D(1) -like receptor. The mechanism underlying the effect could be mainly inhibition of Na(+) channel in the afferent fibers. The suppression of excitatory synaptic transmission and the Na(+) channel by SKF83959 may contribute to its therapeutic benefits in Parkinson's disease.

Identification of N-propylnoraporphin-11-yl 5-(1,2-dithiolan-3-yl)pentanoate as a new anti-Parkinson's agent possessing a dopamine D2 and serotonin 5-HT1A dual-agonist profile.

A series of new aporphine analogues (aporlogues) were synthesized bearing a C-, N-, or O-linkage at the C11 position. Lipoic ester (-)-15 was identified as a full agonist at the dopamine D(2) and serotonin 5-HT(1A) receptors with K(i) values of 174 and 66 nM, respectively. It elicited antiparkinsonian action on Parkinsin's disease (PD) rats with minor dyskinesia. Chronic use of (-)-15 reduced L-DOPA-induced dyskinesia (LID) without attenuating the antiparkinsonian effect. These results suggest that 5-HT(1A) and D(2) dual-receptor agonist (-)-15 may present a novel candidate drug in the treatment of PD and LID.