Response inhibition is the ability to override a planned or an already initiated response. It is the hallmark of executive control as its deficits favour impulsive behaviours, which may be ...detrimental to an individual's life. This article reviews behavioural and computational guises of response inhibition. It focuses only on inhibition of oculomotor responses. It first reviews behavioural paradigms of response inhibition in eye movement research, namely the countermanding and antisaccade paradigms, both proven to be useful tools for the study of response inhibition in cognitive neuroscience and psychopathology. Then, it briefly reviews the neural mechanisms of response inhibition in these two behavioural paradigms. Computational models that embody a hypothesis and/or a theory of mechanisms underlying performance in both behavioural paradigms as well as provide a critical analysis of strengths and weaknesses of these models are discussed. All models assume the race of decision processes. The decision process in each paradigm that wins the race depends on different mechanisms. It has been shown that response latency is a stochastic process and has been proven to be an important measure of the cognitive control processes involved in response stopping in healthy and patient groups. Then, the inhibitory deficits in different brain diseases are reviewed, including schizophrenia and obsessive-compulsive disorder. Finally, new directions are suggested to improve the performance of models of response inhibition by drawing inspiration from successes of models in other domains.
This article is part of the themed issue ‘Movement suppression: brain mechanisms for stopping and stillness’.
Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There ...are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.
Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally ...control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Spike timing dependent plasticity (STDP) has been demonstrated in various neural systems of many animals. It has been shown that STDP depends on the target and the location of the synapse and is ...dynamically regulated by the activity of adjacent synapses, the presence of postsynaptic calcium, presynaptic GABA inhibition or the action of neuromodulators. Recent experimental evidence has reported that the profile of STDP in the CA1 pyramidal neuron can be classified into two types depending on its dendritic location: (1) A symmetric STDP profile in the proximal to the soma dendrites, and (2) an asymmetric one in the distal dendrites. Bicuculline application revealed that GABA
A is responsible for the symmetry of the STDP curve. We investigate via computer simulations how GABA
A shapes the STDP profile in the CA1 pyramidal neuron dendrites when it is driven by excitatory spike pairs (doublets). The model constructed uses calcium as the postsynaptic signaling agent for STDP and is shown to be consistent with classical long-term potentiation (LTP) and long-term depression (LTD) induced by several doublet stimulation paradigms in the absence of inhibition. Overall, simulation results provide computational evidence for the first time that the switch between the symmetrical and the asymmetrical STDP operational modes is indeed due to GABA inhibition. Furthermore, gamma frequency inhibition and not theta one is responsible for the transition from asymmetry-to-symmetry. The resulted symmetrical STDP profile is centered at +10 ms with two distinct LTD tails at −10 and +40 ms. Finally, the asymmetry-to-symmetry transition is strongly dependent on the strength (conductance) of inhibition and its relative onset with respect to pre- and postsynaptic spike stimulation.
•Delays in neuronal activities in EC and hippocampus are longer than 70–80ms.•A model of DG, CA3 and CA1 regions identifies the mechanisms of such long delays.•Delays in DG and CA3 are due to theta ...modulated somatic and axonic inhibition.•Delays in CA1 are due to theta modulated inhibition and increased dendritic excitability.•Model proposes functional roles of various forms of inhibition in DG, CA3 and CA1.
A recent experimental study (Mizuseki, Sirota, Pastalkova, & Buzsaki, 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. A model of the dentate gyrus (DG), CA3 and CA1 microcircuits is presented that uses biophysical representations of the major cell types including granule cells, CA3 and CA1 pyramidal cells (PCs) and six types of interneurons: basket cells (BCs), axo-axonic cells (AACs), bistratified cells (BSCs), oriens lacunosum-moleculare cells (OLMs), mossy cells (MCs) and hilar perforant path associated cells (HC). Inputs to the network came from the entorhinal cortex (EC) (layers 2 and 3) and the medial septum (MS). The model simulates accurately the timing of firing of different hippocampal cells with respect to the theta rhythm. The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition. The model further predicts that the phase at which the CA1 PCs fire with respect to the theta rhythm is determined primarily by their increased dendritic excitability caused by the decrease of the axial resistance and the A-type K+ conductance along their dendritic trunk. The model predicted latencies by which the DG, CA3 and CA1 principal cells fire are inline with the experimental evidence. Finally, the model proposes functional roles for the different inhibitory interneurons in the retrieval of the memory pattern by the DG, CA3 and CA1 networks. The model makes a number of predictions, which can be tested experimentally, thus leading to a better understanding of the biophysical computations in the hippocampus.
Memory, the process of encoding, storing, and maintaining information over time to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the ...biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study, we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size, and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.
In this position paper, I outline the caveats of the current artificial intelligence (AI) field driven by deep learning (DL) and large data volumes. Although AI/DL has demonstrated huge potential and ...attracted huge investments globally, it encounters big problems – it not only need to collect huge datasets and spend enormous time and resources to be trained on them, but also the trained system cannot deal effectively with any never encountered before (novel) data. From a human perspective, any current AI/DL system is completely unintelligent. It is only able to represent information but have no awareness of what this information means. I propose as an alternative the Neuromorphic Cognitive Learning Systems (NCLS), intimate imitations of animal and human brains, able to address the AI/DL limitations and achieve true artificial general intelligence. Similar to human and animal brains NCLS are unparalleled in their ability to rapidly, and on their own, adapt and learn from changing and unexpected environmental contingencies with very limited resources. I describe how NCLS driven AI inspired by human/animal brains can pave the way to new computing technologies with the potential to revolutionize the industry, economy and society. It is my strong belief that NCLS investigations will have major impact to real-time autonomous systems to achieve human-like intelligence capabilities.
In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on a hybrid of an explanatory biophysical model—the Tomgro model, and a machine learning ...model called CNN-RNN. The Tomgro and CNN-RNN models are calibrated/trained for predicting tomato yields while different fusion approaches (linear, Bayesian, neural network, random forest and gradient boosting) are exploited for fusing the prediction result of individual models for obtaining the final prediction results. The experimental results have shown that the model fusion approach achieves more accurate prediction results than the explanatory biophysical model or the machine learning model. Moreover, out of different model fusion approaches, the neural network one produced the most accurate tomato prediction results, with means and standard deviations of root mean square error (RMSE), r2-coefficient, Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) being 17.69 ± 3.47 g/m2, 0.9995 ± 0.0002, 0.9989 ± 0.0004 and 0.1791 ± 0.6837, respectively.
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