Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems

The principle of constraint-induced therapy is widely practiced in rehabilitation. In hemiplegic cerebral palsy (CP) with impaired contralateral corticospinal projection due to unilateral injury, function improves after imposing a temporary constraint on limbs from the less affected hemisphere. This type of partially-reversible impairment in motor control by early brain injury bears a resemblance to the experience-dependent plastic acquisition and modification of neuronal response selectivity in the visual cortex. Previously, such mechanism was modeled within the framework of BCM (Bienenstock-Cooper-Munro) theory, a rate-based synaptic modification theory. Here, we demonstrate a minimally complex yet sufficient neural network model which provides a fundamental explanation for inter-hemispheric competition using a simplified spike-based model of information transmission and plasticity. We emulate the restoration of function in hemiplegic CP by simulating the competition between cells of the ipsilateral and contralateral corticospinal tracts. We use a high-speed hardware neural simulation to provide realistic numbers of spikes and realistic magnitudes of synaptic modification. We demonstrate that the phenomenon of constraint-induced partial reversal of hemiplegia can be modeled by simplified neural descending tracts with 2 layers of spiking neurons and synapses with spike-timing-dependent plasticity (STDP). We further demonstrate that persistent hemiplegia following unilateral cortical inactivation or deprivation is predicted by the STDP-based model but is inconsistent with BCM model. Although our model is a highly simplified and limited representation of the corticospinal system, it offers an explanation of how constraint as an intervention can help the system to escape from a suboptimal solution. This is a display of an emergent phenomenon from the synaptic competition.

Shifting hippocampal excitation/inhibition balance modifies despair-like behavior in mice

Despair is a core symptom of depressive disorders. However, little is known about the neural circuits mediating despair and how they are modified by antidepressants. Here we show that the balance between excitatory and inhibitory neurotransmission (E/I balance) in the hippocampus affects behavioral despair in mice. Reduced interneuron density, knockdown of Gabrg2 or DREADD-mediated suppression of interneuron activity resulted in disinhibition of CA1 neurons and anti-despair-like behaviors in mice. Conversely, pharmacological and chemogenetic potentiation of GABAergic transmission in CA1 neurons rapidly induced despair-like behaviors. Disinhibition induced by the GABAAR antagonist pentylenetetrazol produced transient antidepressant effects without BDNF elevation in the hippocampus, while ketamine exhibited rapid and sustained antidepressant effects, but the latter was sensitive to the TrkB receptor blocker ANA-12. These results suggest that rapid disinhibition and BDNF-induced long-lasting synaptic modification leads to enhanced E/I balance, which may contribute to acute and sustained behavioral effects of rapid-acting antidepressants.

Distinct Laminar Requirements for NMDA Receptors in Experience-Dependent Visual Cortical Plasticity

Primary visual cortex (V1) is the locus of numerous forms of experience-dependent plasticity. Restricting visual stimulation to one eye at a time has revealed that many such forms of plasticity are eye-specific, indicating that synaptic modification occurs prior to binocular integration of thalamocortical inputs. A common feature of these forms of plasticity is the requirement for NMDA receptor (NMDAR) activation in V1. We therefore hypothesized that NMDARs in cortical layer 4 (L4), which receives the densest thalamocortical input, would be necessary for all forms of NMDAR-dependent and input-specific V1 plasticity. We tested this hypothesis in awake mice using a genetic approach to selectively delete NMDARs from L4 principal cells. We found, unexpectedly, that both stimulus-selective response potentiation and potentiation of open-eye responses following monocular deprivation (MD) persist in the absence of L4 NMDARs. In contrast, MD-driven depression of deprived-eye responses was impaired in mice lacking L4 NMDARs, as was L4 long-term depression in V1 slices. Our findings reveal a crucial requirement for L4 NMDARs in visual cortical synaptic depression, and a surprisingly negligible role for them in cortical response potentiation. These results demonstrate that NMDARs within distinct cellular subpopulations support different forms of experience-dependent plasticity.

Suppression of GABAergic neurons through D2-like receptor secures efficient conditioning in Drosophila aversive olfactory learning

The GABAergic system serves as a vital negative modulator in cognitive functions, such as learning and memory, while the mechanisms governing this inhibitory system remain to be elucidated. In Drosophila, the GABAergic anterior paired lateral (APL) neurons mediate a negative feedback essential for odor discrimination; however, their activity is suppressed by learning via unknown mechanisms. In aversive olfactory learning, a group of dopaminergic (DA) neurons is activated on electric shock (ES) and modulates the Kenyon cells (KCs) in the mushroom body, the center of olfactory learning. Here we find that the same group of DA neurons also form functional synaptic connections with the APL neurons, thereby emitting a suppressive signal to the latter through Drosophila dopamine 2-like receptor (DD2R). Knockdown of either DD2R or its downstream molecules in the APL neurons results in impaired olfactory learning at the behavioral level. Results obtained from in vivo functional imaging experiments indicate that this DD2R-dependent DA-to-APL suppression occurs during odor-ES conditioning and discharges the GABAergic inhibition on the KCs specific to the conditioned odor. Moreover, the decrease in odor response of the APL neurons persists to the postconditioning phase, and this change is also absent in DD2R knockdown flies. Taken together, our findings show that DA-to-GABA suppression is essential for restraining the GABAergic inhibition during conditioning, as well as for inducing synaptic modification in this learning circuit. Such circuit mechanisms may play conserved roles in associative learning across species.

Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling

Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel function for prolonged plasticity induction, as well as a more typical function for synaptic weight change based on the relative timing of input-output spike pairs. Consideration for system stability is incorporated with weight-dependent synaptic modification. Next, we formalize an estimation technique using a generalized multilinear model (GMLM) structure with basis function expansion. The weight-dependent synaptic modification adds a nonlinearity to the model, which is addressed with an iterative unconstrained optimization approach. Finally, we demonstrate successful model estimation on simulated spiking data and show that all model functions can be estimated accurately with this method across a variety of simulation parameters, such as number of inputs, output firing rate, input firing type, and simulation time. Since this approach requires only naturally generated spikes, it can be readily applied to behaving animal studies to characterize the underlying mechanisms of learning and memory.

An Online Structural Plasticity Rule for Generating Better Reservoirs

In this letter, we propose a novel neuro-inspired low-resolution online unsupervised learning rule to train the reservoir or liquid of liquid state machines. The liquid is a sparsely interconnected huge recurrent network of spiking neurons. The proposed learning rule is inspired from structural plasticity and trains the liquid through formating and eliminating synaptic connections. Hence, the learning involves rewiring of the reservoir connections similar to structural plasticity observed in biological neural networks. The network connections can be stored as a connection matrix and updated in memory by using address event representation (AER) protocols, which are generally employed in neuromorphic systems. On investigating the pairwise separation property, we find that trained liquids provide 1.36 [Formula: see text] 0.18 times more interclass separation while retaining similar intraclass separation as compared to random liquids. Moreover, analysis of the linear separation property reveals that trained liquids are 2.05 [Formula: see text] 0.27 times better than random liquids. Furthermore, we show that our liquids are able to retain the generalization ability and generality of random liquids. A memory analysis shows that trained liquids have 83.67 [Formula: see text] 5.79 ms longer fading memory than random liquids, which have shown 92.8 [Formula: see text] 5.03 ms fading memory for a particular type of spike train inputs. We also throw some light on the dynamics of the evolution of recurrent connections within the liquid. Moreover, compared to separation-driven synaptic modification', a recently proposed algorithm for iteratively refining reservoirs, our learning rule provides 9.30%, 15.21%, and 12.52% more liquid separations and 2.8%, 9.1%, and 7.9% better classification accuracies for 4, 8, and 12 class pattern recognition tasks, respectively.

DYT1 dystonia increases risk taking in humans

It has been difficult to link synaptic modification to overt behavioral changes. Rodent models of DYT1 dystonia, a motor disorder caused by a single gene mutation, demonstrate increased long-term potentiation and decreased long-term depression in corticostriatal synapses. Computationally, such asymmetric learning predicts risk taking in probabilistic tasks. Here we demonstrate abnormal risk taking in DYT1 dystonia patients, which is correlated with disease severity, thereby supporting striatal plasticity in shaping choice behavior in humans.