Evidence accumulation in a Laplace domain decision space

Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log-likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially, across neurons there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform, these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a "decision axis." This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.Comment: Revised for CB

Evidence accumulation in a Laplace domain decision space

Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here, we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially; across neurons, there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform; these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a “decision axis.” This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.Accepted manuscrip

Deregulation of the EGFR/PI3K/PTEN/Akt/mTORC1 pathway in breast cancer: possibilities for therapeutic intervention

The EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway plays prominent roles in malignant transformation, prevention of apoptosis, drug resistance and metastasis. The expression of this pathway is frequently altered in breast cancer due to mutations at or aberrant expression of: HER2, ERalpha, BRCA1, BRCA2, EGFR1, PIK3CA, PTEN, TP53, RB as well as other oncogenes and tumor suppressor genes. In some breast cancer cases, mutations at certain components of this pathway (e.g., PIK3CA) are associated with a better prognosis than breast cancers lacking these mutations. The expression of this pathway and upstream HER2 has been associated with breast cancer initiating cells (CICs) and in some cases resistance to treatment. The anti-diabetes drug metformin can suppress the growth of breast CICs and herceptin-resistant HER2+ cells. This review will discuss the importance of the EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway primarily in breast cancer but will also include relevant examples from other cancer types. The targeting of this pathway will be discussed as well as clinical trials with novel small molecule inhibitors. The targeting of the hormone receptor, HER2 and EGFR1 in breast cancer will be reviewed in association with suppression of the EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway.USAMRMC {[}BC022276]; Intramural RECDA Award; Italian Association for Cancer Research (AIRC); MIUR-PRIN; Italian MIUR-FIRB Accordi di Programma; Italian ``Ministero dell'Istruzione, dell'Universita e della Ricerca (Ministry for Education, Universities and Research) - FIRB-MERIT {[}RBNE08YYBM]; Italian Ministry of Economy and Finance; Italian Ministry of Health, Ricerca Finalizzata Stemness; MIUR FIRB {[}RBAP11ZJFA\_001]; CRO; Italian Association for Cancer Research, (AIRC) (RM PI); Italian Association for Cancer Research, (AIRC) {[}MCO10016]; Italian Ministry of Health; Regione Friuli Venezia-Giuli

Probabilistic reward- and punishment-based learning in opioid addiction : experimental and computational data

Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals’ performance on the task. Although behavioral results showed that opioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to “chase reward” when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction

An adaptive drift-diffusion model of interval timing dynamics

Animals readily learn the timing between salient events. They can even adapt their timed responding to rapidly changing intervals, sometimes as quickly as a single trial. Recently, drift-diffusion models—widely used to model response times in decision making—have been extended with new learning rules that allow them to accommodate steady-state interval timing, including scalar timing and timescale invariance. These time-adaptive drift-diffusion models (TDDMs) work by accumulating evidence of elapsing time through their drift rate, thereby encoding the to-be-timed interval. One outstanding challenge for these models lies in the dynamics of interval timing—when the to-be-timed intervals are non-stationary. On these schedules, animals often fail to exhibit strict timescale invariance, as expected by the TDDMs and most other timing models. Here, we introduce a simple extension to these TDDMs, where the response threshold is a linear function of the observed event rate. This new model compares favorably against the basic TDDMs and the multiple-time-scale (MTS) habituation model when evaluated against three published datasets on timing dynamics in pigeons. Our results suggest that the threshold for triggering responding in interval timing changes as a function of recent intervals