Human Memory Modeled With Standard Analog And D...
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N-methyl-D-aspartate (NMDA) glutamate receptor antagonists are reported to induce schizophrenia-like symptoms in humans, including cognitive impairments. Shortcomings of most previous investigations include failure to maintain steady-state infusion conditions, test multiple doses and/or measure antagonist plasma concentrations. This double-blind, placebo-controlled, randomized, within-subjects comparison of three fixed subanesthetic, steady-state doses of intravenous ketamine in healthy males (n = 15) demonstrated dose-dependent increases in Brief Psychiatric Rating Scale positive (F[3,42] = 21.84; p < 0.0001) and negative symptoms (F[3,42] = 2.89; p = 0.047), and Scale for the Assessment of Negative Symptoms (SANS) total scores (F[3,42] = 10.55; p < 0.0001). Ketamine also produced a robust dose-dependent decrease in verbal declarative memory performance (F[3,41] = 5.11; p = 0.004), and preliminary evidence for a similar dose-dependent decrease in nonverbal declarative memory, occurring at or below plasma concentrations producing other symptoms. Increasing NMDA receptor hypofunction is associated with early occurring memory impairments followed by other schizophrenia-like symptoms.
Ketamine infusions in healthy adult men produced mild dose-dependent increases in schizophrenia-like psychiatric symptoms, consistent with previous reports (Krystal et al. 1994; Malhotra et al. 1996). Measuring cognitive performance with a comprehensive battery, ketamine infusions additionally produced robust, dose-dependent impairment in verbal, and preliminary evidence for impairment in non-verbal, declarative memory performance. Notably, the effect of ketamine on memory performance occurred at and below plasma concentrations producing other schizophrenia-like symptoms. These results complement previous reports of ketamine-induced memory impairment in humans (Harris et al. 1975; Ghoneim et al. 1985; Krystal et al. 1994; Malhotra et al. 1996), and extend those reports by defining the dose-dependence of this effect on a well-validated measure and by suggesting a preferential effect on declarative memory, versus other differentiated elements of cognitive performance, at these steady-state plasma conditions. The results are relevant to an NMDA receptor hypofunction (NRH) model of schizophrenia (Olney and Farber 1995) and suggest that progressively increasing levels of NRH are associated with an early occurring and prominent impairment of memory performance, consistent with evidence for an early and prominent memory impairment in schizophrenia.
The cognitive and behavioral effects of ketamine, and the success of an intravenous infusion technique in producing steady-state plasma ketamine concentrations, support the further use of this model for testing the role of NRH in the regulation of schizophrenia-like symptoms in humans. Deficits in declarative memory function represent a prominent and clinically significant feature of schizophrenia (Green 1996). Reductions in the volume of medial temporal lobe structures are associated with memory decreases (Nestor et al. 1993) but specific changes in neural circuitry remain incompletely understood. The current results support the hypothesis that changes in NMDA glutamate receptor activity in one or more neural circuits relevant to declarative memory may be related to impairments found in schizophrenia, suggesting an important focus for future efforts to pharmacologically remediate memory impairments in this disorder.
On the mechanistic side, persistent neural activity has been widely hypothesized to form the substrate for short-term memory. The hypothesis is based on a corpus of electrophysiological work establishing a link between short-term memory and persistent neural activity (Funahashi, 2006; Smith and Jonides, 1998; Wimmer et al., 2014). Neural network models of analog persistent activity predict a degradation of information over time (Compte et al., 2000; Brody et al., 2003; Boucheny et al., 2005; Burak and Fiete, 2009; Fung et al., 2010; Mongillo et al., 2008; Burak and Fiete, 2012; Wei et al., 2012), because of noise in synaptic and neural activation. If individual analog features are assumed to be directly stored as variables in such persistent activity networks, the time course of degradation of persistent activity should directly predict the time course of degradation in short-term memory performance. However, these models do not typically consider the direct storage of multiple variables (but see (Wei et al., 2012) ), and in general their predictions have not been directly compared against human psychophysics experiments in which the memory load and delay period are varied.
In the present work, we make the following contributions: (1) Generate psychophysics predictions for information degradation as a function of delay period and number of stored items, if information is stored directly, without recoding, in persistent activity neural networks of a fixed total size; (2) Generate psychophysics predictions (though the use of joint source-channel coding theory) for a model that assumes information is restructured by encoding and decoding stages before and after storage in persistent activity neural networks; (3) Compare these models to new analog measurements (Pertzov et al., 2017) of human memory performance on an analog task as the demands on both maintenance duration and capacity are varied.
We show that the direct storage predictions are at odds with human memory performance. We propose that noisy storage systems, such as persistent activity networks, may be viewed as noisy channels through which information is passed, to be accessed at another time. We use the theory of channel coding and joint source-channel coding to derive the information-theoretic upper-bound on the achievable accuracy of short-term memory as a function of time and number of items to be remembered, assuming a core of graded persistent activity networks. According to the channel coding view, the brain might strategically restructure information before storing it, to use the available neurons in a way that minimizes the impact of noise upon the ability to retrieve that information later. We apply our framework, which requires the assumption of additional encoding and decoding stages in the memory process, to psychophysical data obtained using the technique of delayed estimation (Ma et al., 2014), which provides a sensitive measure of short-term memory recall using a continuous, analog response space, rather than discrete (Yes/No) binary recall responses.
Next, we used empirical data from analog measurements of memory error as a function of both temporal delay and the number of stored items. Using results from the theory of diffusion on continuous attractor manifolds in neural networks, we derived an expression for memory performance if the memorized variables were stored directly in graded persistent activity networks. The resulting predictions did not match human performance. The mismatch invites further investigation into whether and how direct-storage models can be modified to account for real memory performance.
The newer models cited in the paragraph may exhibit different dynamics, and be subject to different types of noise, in which case the general principle of restructuring of information to improve memory would still be true but the functional form of error versus number of items and N could be somewhat different. However, if the synaptic facilitation states in these models were subject to a Gaussian drift (e.g. if the facilitation states are analog-valued and some biophysical noise-process drives a random walk through the set of possible states even in the absence of neural activity), then they too could be could be treated as a bank of information channels with Gaussian noise and potentially our theory would extend to these, but with different parameters.
The best models of disease are similar in etiology (mechanism of cause) and phenotype (signs and symptoms) to the human equivalent. However complex human diseases can often be better understood in a simplified system in which individual parts of the disease process are isolated and examined. For instance, behavioral analogues of anxiety or pain in laboratory animals can be used to screen and test new drugs for the treatment of these conditions in humans. A 2000 study found that animal models concorded (coincided on true positives and false negatives) with human toxicity in 71% of cases, with 63% for nonrodents alone and 43% for rodents alone.[63]
There are two types of long-term memory: explicit and implicit (Figure 8.6). Understanding the difference between explicit memory and implicit memory is important because aging, particular types of brain trauma, and certain disorders can impact explicit and implicit memory in different ways. Explicit memories are those we consciously try to remember, recall, and report. For example, if you are studying for your chemistry exam, the material you are learning will be part of your explicit memory. In keeping with the computer analogy, some information in your long-term memory would be like the information you have saved on the hard drive. It is not there on your desktop (your short-term memory), but most of the time you can pull up this information when you want it. Not all long-term memories are strong memories, and some memories can only be recalled using prompts. For example, you might easily recall a fact, such as the capital of the United States, but you might struggle to recall the name of the restaurant at which you had dinner when you visited a nearby city last summer. A prompt, such as that the restaurant was named after its owner, might help you recall the name of the restaurant. Explicit memory is sometimes referred to as declarative memory, because it can be put into words. Explicit memory is divided into episodic memory and semantic memory. 59ce067264
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