Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

Spatio-molecular domains identified in the mouse subthalamic nucleus and neighboring glutamatergic and GABAergic brain structures

The subthalamic nucleus (STN) is crucial for normal motor, limbic and associative function. STN dysregulation is correlated with several brain disorders, including Parkinsonʼs disease and obsessive compulsive disorder (OCD), for which high-frequency stimulation of the STN is increasing as therapy. However, clinical progress is hampered by poor knowledge of the anatomical–functional organization of the STN. Today, experimental mouse genetics provides outstanding capacity for functional decoding, provided selective promoters are available. Here, we implemented single-nuclei RNA sequencing (snRNASeq) of the mouse STN followed through with histological analysis of 16 candidate genes of interest. Our results demonstrate that the mouse STN is composed of at least four spatio-molecularly defined domains, each distinguished by defined sets of promoter activities. Further, molecular profiles dissociate the STN from the adjoining para-STN (PSTN) and neighboring structures of the hypothalamus, mammillary nuclei and zona incerta. Enhanced knowledge of STN´s internal organization should prove useful towards genetics-based functional decoding of this clinically relevant brain structure.

Word Imageability Enhances Association-memory by Increasing Hippocampal Engagement

The hippocampus is thought to support association-memory, particularly when tested with cued recall. One of the most well-known and studied factors that influences accuracy of verbal association-memory is imageability; participants remember pairs of high-imageability words better than pairs of low-imageability words. High-imageability words are also remembered better in tests of item-memory. However, we previously found that item-memory effects could not explain the enhancement in cued recall, suggesting that imageability enhances association-memory strength. Here we report an fMRI study designed to ask, what is the role of the hippocampus in the memory advantage for associations due to imageability? We tested two alternative hypotheses: (1) Recruitment Hypothesis: High-imageability pairs are remembered better because they recruit the underlying hippocampal association-memory function more effectively. Alternatively, (2) Bypassing Hypothesis: Imageability functions by making the association-forming process easier, enhancing memory in a way that bypasses the hippocampus, as has been found, for example, with explicit unitization imagery strategies. Results found, first, hippocampal BOLD signal was greater during study and recall of high- than low-imageability word pairs. Second, the difference in activity between recalled and forgotten pairs showed a main effect, but no significant interaction with imageability, challenging the bypassing hypothesis, but consistent with the predictions derived from the recruitment hypothesis. Our findings suggest that certain stimulus properties, like imageability, may leverage, rather than avoid, the associative function of the hippocampus to support superior association-memory.

Prefrontal Cortex–Projecting Glutamatergic Thalamic Paraventricular Nucleus-Excited by Hypocretin: A Feedforward Circuit That May Enhance Cognitive Arousal

The paraventricular thalamic nucleus (PVT) receives one of the most dense innervations by hypothalamic hypocretin/orexin (Hcrt) neurons, which play important roles in sleep-wakefulness, attention, and autonomic function. The PVT projects to several loci, including the medial prefrontal cortex (mPFC), a cortical region involved in associative function and attention. To study the effect of Hcrt on excitatory PVT neurons that project to the mPFC, we used a new line of transgenic mice expressing green fluorescent protein (GFP) under the control of the vesicular glutamate-transporter-2 promoter. These neurons were retrogradely labeled with cholera toxin subunit B that had been microinjected into the mPFC. Membrane characteristics and responses to hypocretin-1 and -2 (Hcrt-1 and -2) were studied using whole cell recording ( n > 300). PVT neurons showed distinct membrane properties including inward rectification, H-type potassium currents, low threshold spikes, and spike frequency adaptation. Cortically projecting neurons were depolarized and excited by Hcrt-2. Hcrt-2 actions were stronger than those of Hcrt-1, and the action persisted in TTX and in low calcium/high magnesium artificial cerebrospinal fluid, consistent with direct actions mediated by Hcrt receptor-2. Two mechanisms of Hcrt excitation were found: an increase in input resistance caused by closure of potassium channels and activation of nonselective cation channels. The robust excitation evoked by Hcrt-2 on cortically projecting glutamate PVT neurons could generate substantial excitation in multiple layers of the mPFC, adding to the more selective direct excitatory actions of Hcrt in the mPFC and potentially increasing cortical arousal and attention to limbic or visceral states.

FUNCTIONAL PEARL Derivation of a logarithmic time carry lookahead addition circuit

Using Haskell as a digital circuit description language, we transform a ripple carry adder that requires $O(n)$ time to add two $n$-bit words into a parallel carry lookahead adder that requires $O(\log n)$ time. The ripple carry adder uses a scan function to calculate carry bits, but this scan cannot be parallelized directly since it is applied to a non-associative function. Several techniques are applied in order to introduce parallelism, including partial evaluation and symbolic function representation. The derivation given here constitutes a semi-formal correctness proof, and it also brings out explicitly each of the ideas underlying the algorithm.

Learning to Design Synergetic Computers with an Extended Symmetric Diffusion Network

This article proposes an extended symmetric diffusion network that is applied to the design of synergetic computers. The state of a synergetic computer is translated to that of order parameters whose dynamics is described by a stochastic differential equation. The order parameter converges to the Boltzmann distribution, under some condition on the drift term, derived by the Fokker-Planck equation. The network can learn the dynamics of the order parameters from a nonlinear potential. This property is necessary to design the coefficient values of the synergetic computer. We propose a searching function for the image processing executed by the synergetic computer. It is shown that the image processing with the searching function is superior to the usual image-associative function of synergetic computation. The proposed network can be related, as a special case, to the discrete-state Boltzmann machine by some transformation. Finally, the extended symmetric diffusion network is applied to the estimation problem of an entire density function, as well as the proposed searching function for the image processing.