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In this paper our previous model of activity-dependent synaptic modification is extended and applied to a model neuron with active dendrites and is used in computer simulations to examine in detail the dependence of synaptic modifications on the interval between the onset of excitatory postsynaptic potentials (EPSPs) and postsynaptic action potentials (APs). The EPSP amplitude is increased when the action potentials occur within 20 ms after EPSPs and is reduced when the action potentials occur within 20 ms before EPSPs. Furthermore, the absolute value of changes in the EPSP amplitude tends to increase as the interval between APs and EPSPs decreases. A learning rule for synaptic modifications described in this paper may, hence, further generalize the Hebbian rule which requires conjunctive presynaptic and postsynaptic activity for synaptic modification to occur. Functional roles for such a generalized Hebbian rule are also considered.

High-frequency deep brain stimulation (HF-DBS) of the subthalamic nucleus (STN), globus pallidus interna (GPi) and globus pallidus externa (GPe) are often considered as effective methods for the treatment of Parkinson’s disease (PD). However, the stimulation of a single nucleus by HF-DBS can cause specific physical damage, produce side effects and usually consume more electrical energy. Therefore, we use a biophysically-based model of basal ganglia-thalamic circuits to explore more effective stimulation patterns to reduce adverse effects and save energy. In this paper, we computationally investigate the combined DBS of two nuclei with the phase deviation between two stimulation waveforms (CDBS). Three different stimulation combination strategies are proposed, i.e., STN and GPe CDBS (SED), STN and GPi CDBS (SID), as well as GPi and GPe CDBS (GGD). Resultantly, it is found that anti-phase CDBS is more effective in improving parkinsonian dynamical properties, including desynchronization of neurons and the recovery of the thalamus relay ability. Detailed simulation investigation shows that anti-phase SED and GGD are superior to SID. Besides, the energy consumption can be largely reduced by SED and GGD (72.5% and 65.5%), compared to HF-DBS. These results provide new insights into the optimal stimulation parameter and target choice of PD, which may be helpful for the clinical practice.

Periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity. These kinds of oscillations always accompany with some spontaneous firing in up state. Our previous theoretical studies mainly looked at the subthreshold up and down transitions and characteristics of up and down dynamics. In this paper, we focus on suprathreshold spontaneous firing of up and down transitions based on improved network model and its stimulations. The simulated results indicate that fast sodium current is critical to the generation of spontaneous neural firing. While persistent sodium current plays a part in spontaneous fluctuation. Both intrinsic fast and persistent sodium dynamics influence spontaneous firing rate and synchronous activity in up and down behavior. Meanwhile, blocking excitatory synaptic transmission decreases neural firing and reveals spontaneous firing. These simulated results are basically in accordance with experimental results. Through the observation and analysis of the findings, we prove the validity of the model so we can further adopt this model to study other properties and characteristics of the network, laying the foundation for further work on cortex activity.

The efficacy of deep brain stimulation (DBS) for Parkinson’s disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia–thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.

Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural structure is able to cope concurrently with a plethora of behaviours. Simulation results and robotic experiments are reported and discussed.

Synchronous behaviour of neurons is both beneficial and detrimental to the neural code. On the one extreme, synchronous firing activity is well known to be a symptom of epileptic seizures, whilst on the other synchrony provides a mechanism for coordinating brain activity. This paper briefly reviews some current thinking with regard to synchrony, and outlines some experiments with LIF neurons that harness near-synchronous states for processing biologically-realistic sensory stimuli. Inspired by the topology of neurons in the cochlear nucleus, laterally connected leaky integrate and fire neurons, operating in near-synchronous states, are investigated for their ability to reduce noisy spikes and increase spectral contrast of auditory stimuli. Two connectivity parameters, referred to as connection length and neighbourhood radius, are introduced to configure lateral inhibitory connectivity to generate this neural behaviour. Information-theoretic principles are then employed to quantify the information retained by the coding, and then this is compared to the information retained by the various output topologies.

This paper presents results on a computational study of how multi-site stimulation of the subthalamic nucleus (STN), within the basal ganglia, can improve the fidelity of thalamocortical (TC) relay in a parkinsonian network model. In the absence of stimulation, the network model generates activity featuring synchronized bursting by clusters of neurons in the STN and internal segment of the globus pallidus (GPi), as occurs experimentally in parkinsonian states. This activity yields rhythmic inhibition from GPi to TC neurons, which compromises TC relay of excitatory inputs. We incorporate two types of multi-site STN stimulation into the network model. One stimulation paradigm features coordinated reset pulses that are on for different subintervals of each period at different sites. The other is based on a filtered version of the local field potential recorded from the STN population. Our computational results show that both types of stimulation significantly diminish TC relay errors; the former reduces the rhythmicity of the net GPi input to TC neurons and the latter reduces, but does not eliminate, STN activity. Both types of stimulation represent promising directions for possible therapeutic use with Parkinson's disease patients.

This paper presents a preliminary analysis of the neuroscience knowledge domain, and an application of cluster analysis to identify topics in neuroscience. A collection of posters presented at the Society for Neuroscience (SfN) Annual Meeting in 2006 is first explored by viewing existing topics and poster sessions using multidimensional scaling. Based on the Vector Space Model, several Term Spaces were built on the basis of a set of terms extracted from the posters' abstracts and titles, and a set of free keywords assigned to the posters by their authors. The ensuing Term Spaces were compared from the point of view of retrieving the genuine category titles. Topics were extracted from the abstracts of posters by clustering the documents using a bisecting k-means algorithm and selecting the most salient terms for each cluster by ranking. The terms extracted as topic descriptors were evaluated by comparing them to existing titles assigned to thematic categories defined by human experts in neuroscience. A comparison of two approaches for terms ranking (Document Frequency and Log-Entropy) resulted in better performance of the Log-Entropy scores, allowing to retrieve 31.0% of original title terms in clustered documents (and 37.1% in original thematic categories).

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup. ispartof: Neural networks : the official journal of the International Neural Network Society vol:21 issue:4 pages:577-83 ispartof: location:United States status: published

"Wind-up", a condition related to chronic pain, is a form of plasticity in spinal dorsal horn that can be observed during electrical stimulation of pain receptors at low frequencies (0.3-3 Hz). In this paper, we present a computational model to explain several aspects of wind-up. The core of this model is the interplay of spike-time-dependent plasticity (STDP), short-term synaptic plasticity (STP), and different propagation velocities of the three afferent fibers (C, Adelta, and Abeta). We utilize Izhikevich's simple spiking neuron to model a dorsal horn neuron (DHN) of the spinal cord. To achieve the expected results, the model parameters need to adapt to the frequency response which is motivated by biological results. The adaptation is performed by a genetic algorithm (GA), and the resulting optimized values interestingly lie in biological ranges. Based on the proposed model, we suggest that STP may be the origin of the band-pass behavior of wind-up between 0.3 and 3 Hz; while the STDP-based long-term plasticity can be responsible for the synaptic potentiation leading to wind-up, or similar phenomena such as central sensitization. Understanding the mechanisms underlying wind-up generation might allow clarification of the molecular mechanisms of pain signaling and development of strategies, such as transcutaneous electrical nerve stimulation (TENS), for pain treatment.

How does the brain learn to balance between reactive and planned behaviors? The basal ganglia (BG) and frontal cortex together allow animals to learn planned behaviors that acquire rewards when prepotent reactive behaviors are insufficient. This paper proposes a new model, called TELOS, to explain how laminar circuitry of the frontal cortex, exemplified by the frontal eye fields, interacts with the BG, thalamus, superior colliculus, and inferotemporal and parietal cortices to learn and perform reactive and planned eye movements. The model is formulated as fourteen computational hypotheses. These specify how strategy priming and action planning (in cortical layers III, Va and VI) are dissociated from movement execution (in layer Vb), how the BG help to choose among and gate competing plans, and how a visual stimulus may serve either as a movement target or as a discriminative cue to move elsewhere. The direct, indirect and hyperdirect pathways through the BG are shown to enable complex gating functions, including deferred execution of selected plans, and switching among alternative sensory-motor mappings. Notably, the model can learn and gate the use of a What-to-Where transformation that enables spatially invariant object representations to selectively excite spatially coded movement plans. Model simulations show how dopaminergic reward and non-reward signals guide monkeys to learn and perform saccadic eye movements in the fixation, single saccade, overlap, gap, and delay (memory-guided) saccade tasks. Model cell activation dynamics quantitatively simulate seventeen established types of dynamics exhibited by corresponding real cells during performance of these tasks.

In the temporal difference model of primate dopamine neurons, their phasic activity reports a prediction error for future reward. This model is supported by a wealth of experimental data. However, in certain circumstances, the activity of the dopamine cells seems anomalous under the model, as they respond in particular ways to stimuli that are not obviously related to predictions of reward. In this paper, we address two important sets of anomalies, those having to do with generalization and novelty. Generalization responses are treated as the natural consequence of partial information; novelty responses are treated by the suggestion that dopamine cells multiplex information about reward bonuses, including exploration bonuses and shaping bonuses. We interpret this additional role for dopamine in terms of the mechanistic attentional and psychomotor effects of dopamine, having the computational role of guiding exploration.

An essential question raised after the observation of highly variable bursting activity in individual neurons of Central Pattern Generators (CPGs) is how an assembly of such cells can cooperatively act to produce regular signals to motor systems. It is well known that some neurons in the lobster stomatogastric ganglion have a highly irregular spiking-bursting behavior when they are synaptically isolated from any connection in the CPG. Experimental recordings show that periodic stimuli on a single neuron can regulate its firing activity. Other evidence demonstrates that specific chemical and/or electrical synapses among neurons also induce the regularization of the rhythms. In this paper we present a modeling study in which a slow subcellular dynamics, the exchange of calcium between an intracellular store and the cytoplasm, is responsible for the origin and control of the irregular spiking-bursting activity. We show this in simulations of single cells under periodic driving and in minimal networks where the cooperative activity can induce regularization. While often neglected in the description of realistic neuron models, subcellular processes with slow dynamics may play an important role in information processing and short-term memory of spiking-bursting neurons.

Pattern completion in a neural network model of the thalamus and a biologically plausible model of synaptic plasticity are the key concepts used in this paper for analyzing some cognitive disorders that involve hallucinations of several kinds: visual hallucinations in the Charles Bonnet syndrome and psychedelic drugs consumption, somatic hallucination in phantom limbs, cognitive hallucinations in schizophrenia and even in multiple personality disorders. All these types of hallucinations are proposed to be the result of a pattern completion dynamics performed in thalamic deafferented areas. Effective treatments of some of these disorders involve peripheral stimulation jointly with a central inhibition so that the neural circuits generating the disorders are depressed according to the proposed model of synaptic plasticity.

The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI, functional Magnetic Resonance Imaging) to the underpinning neural networks. Models tied to human brain imaging data often focus on a few “boxes” based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple brain regions. For analysis directly at the level of such data, a schema-based model may be most appropriate. To further address neurophysiological data, the Synthetic PET imaging method uses computational models of biological neural circuitry based on animal data to predict and analyze the results of human PET studies. This technique makes use of the hypothesis that rCBF (regional cerebral blood flow) is correlated with the integrated synaptic activity in a localized brain region. We also describe the possible extension of the Synthetic PET method to fMRI. The second half of the paper then exemplifies this general research program with two case studies, one on visuo-motor processing for control of grasping (Section 3 in which the focus is on Synthetic PET) and the imitation of motor skills (Sections 4 and 5, with a focus on Synthetic fMRI). Our discussion of imitation pays particular attention to data on the mirror system in monkey (neural circuitry which allows the brain to recognize actions as well as execute them). Finally, Section 6 outlines the immense challenges in integrating models of different portions of the nervous system which address detailed neurophysiological data from studies of primates and other species; summarizes key issues for developing the methodology of Synthetic Brain Imaging; and shows how comparative neuroscience and evolutionary arguments will allow us to extend Synthetic Brain Imaging even to language and other cognitive functions for which few or no animal data are available.

The respiratory motor system is a specialized musculoskeletal system that is controlled by a small assembly of neuronal clusters in the brainstem. Its prime function is to maintain CO(2), O(2), and pH homeostasis in arterial circulation through the motor act of breathing. A longstanding dilemma is that during muscular exercise homeostatic regulation occurs automatically without any apparent feedback or feedforward signals, whereas the homeostasis is readily abolished by exogenous chemical challenge. Recently, it has been proposed that these seemingly incongruous behaviors of the respiratory controller may be a manifestation of self-tuning adaptive control. This hypothesis is supported in part by recent discoveries of various memory systems in the brainstem controller including short- and long-term potentiation and depression of synaptic transmission as well as the dramatic abolishment of homeostatic regulation in mutant mice with targeted genetic disruption of the NMDA receptors. In this paper, we propose a model of self-tuning homeostatic regulation based upon a synaptic adaptation rule-Hebbian covariance learning rule-that has been suggested to underlie many forms of learning and memory in the higher brain. We show that such an adaptation rule may be a useful neuronal substrate for reinforcement learning in optimization tasks. The model demonstrates how spontaneous oscillations and/or random-like fluctuations of neural activity may be exploited by the controller to adaptively regulate and optimize motor output. Such a self-tuning neural control paradigm, which operates without the need for any internal model of the external environment, is generally applicable to a class of steady-state optimal regulation problems with infinite time horizon. An interesting implication of the present results is that brain intelligent control can occur at a subconscious level without the need for voluntary intervention from the higher brain. Copyright 1996 Elsevier Science Ltd.

Relatively simple invertebrate animals exhibit an impressive variety of learning and memory behaviour including such associative learning tasks as classical or Pavlovian conditioning. The cellular machinery responsible for this behaviour is beginning to be understood by neurophysiologists in certain well-studied species such as the marine mollusc Aplysia. We describe analog CMOS circuits which can be used to qualitatively mimic the mechanisms of associative learning in this creature. In particular we model the synaptic plasticity and activity-dependent facilitation in sensorimotor synapses responsible for classical conditioning of the gill withdrawal reflex. The same circuits implement the nonassociative learning mechanisms of habituation and sensitization. We also discuss considerations in abstracting from the biological mechanisms when developing artificial neural systems. The specific circuits for Aplysia described in this paper are suggestive of ways to implement VLSI area-efficient associative learning circuits in general, especially those which perform in situ temporal associations on multiple synaptic inputs.