Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Encoding-dependent generalization bounds for parametrized quantum circuits

A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-sample performance of such models, in terms of generalization bounds, have emerged. However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC. We derive generalization bounds for PQC-based models that depend explicitly on the strategy used for data-encoding. These imply bounds on the performance of trained PQC-based models on unseen data. Moreover, our results facilitate the selection of optimal data-encoding strategies via structural risk minimization, a mathematically rigorous framework for model selection. We obtain our generalization bounds by bounding the complexity of PQC-based models as measured by the Rademacher complexity and the metric entropy, two complexity measures from statistical learning theory. To achieve this, we rely on a representation of PQC-based models via trigonometric functions. Our generalization bounds emphasize the importance of well-considered data-encoding strategies for PQC-based models.

Memory and Reward-Based Learning: A Value-Directed Remembering Perspective

The ability to prioritize valuable information is critical for the efficient use of memory in daily life. When information is important, we engage more effective encoding mechanisms that can better support retrieval. Here, we describe a dual-mechanism framework of value-directed remembering in which both strategic and automatic processes lead to differential encoding of valuable information. Strategic processes rely on metacognitive awareness of effective deep encoding strategies that allow younger and healthy older adults to selectively remember important information. In contrast, some high-value information may also be encoded automatically in the absence of intention to remember, but this may be more impaired in older age. These different mechanisms are subserved by different neural substrates, with left-hemisphere semantic processing regions active during the strategic encoding of high-value items, and automatic enhancement of encoding of high-value items may be supported by activation of midbrain dopaminergic projections to the hippocampal region. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Encoding time in neural dynamic regimes with distinct computational tradeoffs

Converging evidence suggests the brain encodes time in time-varying patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Temporal tasks that require producing the same time-dependent output patterns may have distinct computational requirements in regard to the need to exhibit temporal scaling or generalize to novel contexts. It is not known how neural circuits can both encode time and satisfy distinct computational and generalization requirements, it is also not known whether similar patterns of neural activity at the population level can emerge from distinctly different network configurations. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamics for single intervals were associated with fundamentally different encoding strategies and network configurations. Critically, some regimes were better suited for generalization, categorical timing, or robustness to noise. Further analysis revealed different connection patterns underlying the different encoding strategies. Our results predict that apparently similar neural dynamic regimes at the population level can be produced through fundamentally different mechanisms—e.g., in regard to network connectivity and the role of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

Performance of Convolution and CRC Channel Encoded V-Blast 4x4 MIMO MC-CDMA Wireless Communication System

Wireless communications are among the rapidly growing fields in our current life and have a massive effect on every aspect of our everyday life. In this paper, the performance of the various digital modulation techniques (BPSK, DPSK, QPSK, and QAM) based wireless communication system on the audio signal transmission through the additive Gaussian Noise (AWGN) channel is assessed on the basis of bit error rate (BER) as a function of the signal-to-noise ratio (SNR). Based on the results of this study, BPSK modulation outperforms the DPSK, QPSK, and QAM modulation strategies in the MIMO MC-CDMA VBlast based wireless communication system. The digital modulation of QPSK shows the worst performance in audio signal transmission especially in comparison to other digital modulations. It is clear from the current simulation study based on MATLAB that the V-Blast encoded 4×4 MIMO MC-CDMA wireless system with minimum mean square error (MMSE) signal detection and 1⁄2-rated convolution and cyclic redundancy check (CRC) channel encoding strategies show good performance utilizing BPSK digital modulation in audio signal transmission.

STOP SHOUTING AT ME: The Influence of Case and Self-Referencing on Explicit and Implicit Memory

Evidence suggests that physical changes in word appearance, such as those written in all capital letters, and the use of effective encoding strategies, such as self-referential processing, improves memory. In this study we examined the extent both physical changes in word appearance (case) and encoding strategies engaged at study influence memory as measured by both explicit and implicit memory measures. Participants studied words written in upper and lower case under three encoding conditions (self-reference, semantic control, case judgment), which was followed by an implicit (word stem completion) and then an explicit (item and context) memory test. There were two primary results. First, analyses indicated a case enhancement effect for item memory where words written in upper case were better remembered than lower case, but only when participants were prompted to attend to the case of the word. Importantly, this case enhancement effect came at a cost to context memory for words written in upper case. Second, self-referencing increased explicit memory performance relative to control, but there was no effect on implicit memory. Overall, results suggest an item-context memory trade-off for words written in upper case, highlighting a potential downside to writing in all capital letters, and further, that both physical changes to the appearance of words and differing encoding strategies have a strong influence on explicit, but not implicit memory.

Learning and organization of memory for evolving patterns

Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor, and establish distributed memory that can be retrieved upon re-exposure. In contrast, the immune system encodes specialized memory by diverse receptors that recognize a multitude of evolving pathogens. Despite the mechanistic differences between the olfactory and the immune memory, these systems can still be viewed as different information encoding strategies. Here, we present a theoretical framework with artificial neural networks to characterize optimal memory strategies for both static and dynamic (evolving) patterns. Our approach is a generalization of the energy-based Hopfield model in which memory is stored as a network's energy minima. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a distributed network should use a higher learning rate, which in turn, can distort the energy landscape associated with the stored memory attractors. Specifically, narrow connecting paths emerge between memory attractors, leading to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to encoded a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors.

The Survival Effect in Memory: Does It Hold into Old Age and Non-Ancestral Scenarios?

The survival effect in memory refers to the memory enhancement for materials encoded in reference to a survival scenario compared to those encoded in reference to a control scenario or with other encoding strategies [1]. The current study examined whether this effect is well maintained in old age by testing young (ages 18–29) and older adults (ages 65–87) on the survival effect in memory for words encoded in ancestral and/or non-ancestral modern survival scenarios relative to a non-survival control scenario. A pilot study was conducted to select the best matched comparison scenarios based on potential confounding variables, such as valence and arousal. Experiment 1 assessed the survival effect with a well-matched negative control scenario in both young and older adults. The results showed an age-equivalent survival effect across an ancestral and a non-ancestral modern survival scenario. Experiment 2 replicated the survival effect in both age groups with a positive control scenario. Taken together, the data suggest a robust survival effect that is well preserved in old age across ancestral and non-ancestral survival scenarios.

The Survival Effect in Memory: Does It Hold into Old Age and Non-Ancestral Scenarios?

The survival effect in memory refers to the memory enhancement for materials encoded in reference to a survival scenario compared to those encoded in reference to a control scenario or with other encoding strategies [1]. The current study examined whether this effect is well maintained in old age by testing young (ages 18–29) and older adults (ages 65–87) on the survival effect in memory for words encoded in ancestral and/or non-ancestral modern survival scenarios relative to a non-survival control scenario. A pilot study was conducted to select the best matched comparison scenarios based on potential confounding variables, such as valence and arousal. Experiment 1 assessed the survival effect with a well-matched negative control scenario in both young and older adults. The results showed an age-equivalent survival effect across an ancestral and a non-ancestral modern survival scenario. Experiment 2 replicated the survival effect in both age groups with a positive control scenario. Taken together, the data suggest a robust survival effect that is well preserved in old age across ancestral and non-ancestral survival scenarios.

Age-related associative memory deficit: simulation and strategies to improve performance

According to the associative deficit hypothesis, older adults experience greater difficulty in remembering associations between pieces of information (associative memory) than young adults, despite their relatively intact memory for individual items (item memory). Recent research suggests that this deficit might be related to older adults’ reduced availability of attentional resources – the reservoir of mental energy needed for the operations of cognition functions. The purpose of this Dissertation was to examine the role of attentional resources in associative deficit, and to explore encoding manipulations that might alleviate the deficit in older adults. In Study 1, young adults’ attentional resources during encoding of word pairs were depleted using a divided attention task. These participants showed an associative deficit commonly observed in older adults, and were less likely to use effective encoding strategies and recollection-based processes to support their memory in comparison to young adults under full attention. The resemblance in memory performance between young adults under divided attention and older adults suggests that lack of attentional resources might be a contributing factor in older adults’ associative deficit. In Study 2, participants’ resource load during encoding was reduced by learning individual items and their associations sequentially in two phases. Older adults in this condition showed equivalent memory performance to young adults, and were more likely to use effective encoding strategies and recollection-based processes than older adults in Study 1 who studied items and associations simultaneously. Finally, Study 3 employed a value-directed learning paradigm, in which participants studied high- and low-value word pairs. Older adults showed similar memory performance for both high- and low-value word pairs as young adults, without any signs of associative deficit. Assigning value to associative information might prompt older adults to prioritize associative encoding over item encoding, which benefits their associative memory. Taken together, these results suggest that depletion of attentional resources during encoding could impair associative memory. Furthermore, older adults’ associative deficit could be effectively alleviated with sufficient environmental support during encoding, such as when resource competition between item and associative encoding is minimized (Study 2) or when being guided to prioritize encoding of associations over items (Study 3).