Modelling the Effect of Dorsal Raphe Serotonin Neurons on Patience for Future Rewards

Serotonin is a neurotransmitter that is implicated in many basic human functions and behaviours and is closely associated with happiness, depression and reward processing. In particular it appears to be involved in suppressing responses to distracting stimuli while waiting for a delayed reward. Here we present a system level model of the limbic system which is able to generate a serotonin (5-hydroxytryptamine [5HT]) signal so that a simulated animal waits for a delayed reward. We propose that the 5HT signal is computed by a network involving the medial Orbital Frontal Cortex (mOFC), medial Pre Frontal Cortex (mPFC), Dorsal Raphe Nucleus (DRN)and the Nucleus Accumbens Core (NAcc). The serotonin signal encodes pre-reward liking, motivation throughout the trial and delayed reward waiting. We have successfully replicated the behaviour and dynamics of laboratory studies. With the help of this model we can predict that low levels of serotonin indirectly cause less encountered rewards because the animal gives up too early

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution

Biologically inspired temporal sequence learning

We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation

Fast and robust learning by reinforcement signals: explorations in the insect brain

We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses

Learning with naturalistic odor representations in a dynamic model of the Drosophila olfactory system

Many odor receptors in the insect olfactory system are broadly tuned, yet insects can form associative memories that are odor-specific. The key site of associative olfactory learning in insects, the mushroom body, contains a population of Kenyon Cells (KCs) that form sparse representations of odor identity and enable associative learning of odors by mushroom body output neurons (MBONs). This architecture is well suited to odor-specific associative learning if KC responses to odors are uncorrelated with each other, however it is unclear whether this hold for actual KC representations of natural odors. We introduce a dynamic model of the Drosophila olfactory system that predicts the responses of KCs to a panel of 110 natural and monomolecular odors, and examine the generalization properties of associative learning in model MBONs. While model KC representations of odors are often quite correlated, we identify mechanisms by which odor-specific associative learning is still possible

The emotional gatekeeper: a computational model of attentional selection and suppression through the pathway from the amygdala to the inhibitory thalamic reticular nucleus

In a complex environment that contains both opportunities and threats, it is important for an organism to flexibly direct attention based on current events and prior plans. The amygdala, the hub of the brain's emotional system, is involved in forming and signaling affective associations between stimuli and their consequences. The inhibitory thalamic reticular nucleus (TRN) is a hub of the attentional system that gates thalamo-cortical signaling. In the primate brain, a recently discovered pathway from the amygdala sends robust projections to TRN. Here we used computational modeling to demonstrate how the amygdala-TRN pathway, embedded in a wider neural circuit, can mediate selective attention guided by emotions. Our Emotional Gatekeeper model demonstrates how this circuit enables focused top-down, and flexible bottom-up, allocation of attention. The model suggests that the amygdala-TRN projection can serve as a unique mechanism for emotion-guided selection of signals sent to cortex for further processing. This inhibitory selection mechanism can mediate a powerful affective 'framing' effect that may lead to biased decision-making in highly charged emotional situations. The model also supports the idea that the amygdala can serve as a relevance detection system. Further, the model demonstrates how abnormal top-down drive and dysregulated local inhibition in the amygdala and in the cortex can contribute to the attentional symptoms that accompany several neuropsychiatric disorders.R01MH057414 - NIMH NIH HHS; R01 MH057414 - NIMH NIH HHS; R01 MH101209 - NIMH NIH HHS; R01NS024760 - NINDS NIH HHS; R01MH101209 - NIMH NIH HHS; R01 NS024760 - NINDS NIH HH

Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future.

Spike-timing-dependent synaptic plasticity (STDP) is a leading cellular model for behavioral learning and memory with rich computational properties. However, the relationship between the millisecond-precision spike timing required for STDP and the much slower timescales of behavioral learning is not well understood. Neuromodulation offers an attractive mechanism to connect these different timescales, and there is now strong experimental evidence that STDP is under neuromodulatory control by acetylcholine, monoamines, and other signaling molecules. Here, we review neuromodulation of STDP, the underlying mechanisms, functional implications, and possible involvement in brain disorders.BBSR