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

Dual Effect of Beta-Amyloid on α7 and α4β2 Nicotinic Receptors Controlling the Release of Glutamate, Aspartate and GABA in Rat Hippocampus

BACKGROUND: We previously showed that beta-amyloid (Aβ), a peptide considered as relevant to Alzheimer's Disease, is able to act as a neuromodulator affecting neurotransmitter release in absence of evident sign of neurotoxicity in two different rat brain areas. In this paper we focused on the hippocampus, a brain area which is sensitive to Alzheimer's Disease pathology, evaluating the effect of Aβ (at different concentrations) on the neurotransmitter release stimulated by the activation of pre-synaptic cholinergic nicotinic receptors (nAChRs, α4β2 and α7 subtypes). Particularly, we focused on some neurotransmitters that are usually involved in learning and memory: glutamate, aspartate and GABA. METHODOLOGY/FINDINGS: WE USED A DUAL APPROACH: in vivo experiments (microdialysis technique on freely moving rats) in parallel to in vitro experiments (isolated nerve endings derived from rat hippocampus). Both in vivo and in vitro the administration of nicotine stimulated an overflow of aspartate, glutamate and GABA. This effect was greatly inhibited by the highest concentrations of Aβ considered (10 µM in vivo and 100 nM in vitro). In vivo administration of 100 nM Aβ (the lowest concentration considered) potentiated the GABA overflow evoked by nicotine. All these effects were specific for Aβ and for nicotinic secretory stimuli. The in vitro administration of either choline or 5-Iodo-A-85380 dihydrochloride (α7 and α4β2 nAChRs selective agonists, respectively) elicited the hippocampal release of aspartate, glutamate, and GABA. High Aβ concentrations (100 nM) inhibited the overflow of all three neurotransmitters evoked by both choline and 5-Iodo-A-85380 dihydrochloride. On the contrary, low Aβ concentrations (1 nM and 100 pM) selectively acted on α7 subtypes potentiating the choline-induced release of both aspartate and glutamate, but not the one of GABA. CONCLUSIONS/SIGNIFICANCE: The results reinforce the concept that Aβ has relevant neuromodulatory effects, which may span from facilitation to inhibition of stimulated release depending upon the concentration used

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.ISSN:2634-438

Self-Rated Attentiveness Interacts with Transcranial Direct Current Stimulation and Noise Stimulation in Reaction Time in a Go/No-Go Task

Previous research has found that stimulating inattentive people with auditory white noise induces enhancement in cognitive performance. This enhancement is believed to occur due to a statistical phenomenon called stochastic resonance, where noise increases the probability of a signal passing the firing threshold in the neural cells. Here we investigate whether people with low attentiveness benefit to a larger extent than attentive people from stimulation by auditory white noise and transcranial direct current stimulation (tDCS). The results show, for both auditory noise and tDCS stimulation, that the changes in performance relative to nonstimulation correlate with the degree of attentiveness in a Go/No-Go task, but not in a N-back task. These results suggest that the benefit of tDCS may interact with inattentiveness