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We combined pharmacological studies and electrophysiological recordings to investigate modifications in muscarinic acetylcholine (ACh) receptors (mAChR) in the rat olfactory (piriform) cortex, following odor-discrimination rule learning. Rats were trained to discriminate between positive and negative cues in pairs of odors, until they reached a phase of high capability to learn unfamiliar odors, using the same paradigm ("rule learning"). It has been reported that at 1-3 d after the acquisition of odor-discrimination rule learning, pyramidal neurons in the rat piriform cortex show enhanced excitability, due to a reduction in the spike-activated potassium current I AHP , which is modulated by ACh. Further, ACh and its analog, carbachol (CCh), lost the ability to reduce the I AHP in neurons from trained rats. Here we show that the reduced sensitivity to CCh in the piriform cortex results from a decrease in the number of mAChRs, as well as a reduction in the affinity of the receptors to CCh. Also, it has been reported that 3-8 d after the acquisition of odor-discrimination rule learning, synaptic transmission in the piriform cortex is enhanced, and paired-pulse facilitation (PPF) in response to twin stimulations is reduced. Here, intracellular recordings from pyramidal neurons show that CCh increases PPF in the piriform cortex from odor-trained rats more than in control rats, suggesting enhanced effect of ACh in inhibiting presynaptic glutamate release after odor training.

An attack graph is a method used to enumerate the possible paths that an attacker can execute in the organization network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the representation of network protocol vulnerabilities, and thus it cannot be used to model common network attacks such as ARP poisoning, DNS spoofing, and SYN flooding. Second, it does not support advanced types of communication such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols (e.g., Bluetooth), (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service, as well as attacks on advanced types of communication.We demonstrate the proposed model on a testbed implementing a simplified network architecture comprised of both IT and industrial components.