
局所的な漏斗状パターンの視覚刺激によって誘発される視覚錯覚を、興奮性および抑制性ニューロンの相互作用を考慮したアマリ型神経場モデルで再現できることを示した。


coremsg

視覚錯覚の再現性-局所的刺激によって誘発される神経場モデル


視覚錯覚の再現性:局所的刺激によって誘発される神経場モデル


title_rewrite


提案された自己符号化器モデルは、側膝状体と一次視覚野の間の順方向および逆方向の相互作用を効果的にモデル化し、優れた分類性能を示す。


視覚系の側膝状体を効率的にモデル化するための自己符号化器アーキテクチャ


本モデルは、皮質の二つの帰納的バイアス(表現のバイアスと構造のバイアス)を活用することで、生物学的に妥当な学習ルールを用いて刺激間の連合学習を実現する。


予測刺激学習を実現するrnnモデル-皮質の帰納的バイアスを活用する


予測刺激学習を実現するRNNモデル：皮質の帰納的バイアスを活用する



感覚評価と行動選択という異なる神経プロセスは、選択性特化型のフィードバック接続を介して相互作用し、柔軟な意思決定の最適化に寄与する。


Neural activity in the primate brain correlates with both sensory evaluation and action selection aspects of decision-making. However, the intricate interaction between these distinct neural processes and their impact on decision behaviors remains unexplored. Here, we examined the interplay of these decision processes in posterior parietal cortex (PPC) when monkeys performed a flexible decision task. We found that the PPC activity related to monkeys’ abstract decisions about visual stimuli was nonlinearly modulated by monkeys’ following saccade choices directing outside each neuron’s response field. Recurrent neural network modeling indicated that the feedback connections, matching the learned stimuli-response associations during the task, mediated such feedback modulation. Further analysis on network dynamics revealed that selectivity-specific feedback connectivity intensified the attractor basins of population activity underlying saccade choices, thereby increasing the consistency of flexible decisions. These results highlight an iterative computation between different decision processes, mediated primarily by precise feedback connectivity, contributing to the optimization of flexible decision-making.

### Competing Interest Statement

The authors have declared no competing interest.

Nonlinear feedback modulation contributes to the optimization of flexible decision-making

柔軟な意思決定中の異なる神経プロセスの相互作用を仲介する選択性特化型フィードバック接続


一次元の柔軟な引力子アーキテクチャでも、二次元の空間表現を生成できることを示した。


Entorhinal grid cells implement a spatial code with hexagonal periodicity, signaling the position of the animal within an environment. Grid maps of cells belonging to the same module share spacing and orientation, only differing in relative two-dimensional spatial phase, which could result from being interconnected by a two-dimensional attractor guided by path integration. However, this architecture has the drawbacks of being complex to construct and rigid, path integration allowing for no deviations from the hexagonal pattern such as the ones observed under a variety of experimental manipulations. Here we show that a simpler one-dimensional attractor is enough to align grid cells equally well. Using topological data analysis, we show that the resulting population activity is a sample of a torus, while the ensemble of maps preserves features of the network architecture. The flexibility of this low dimensional attractor allows it to negotiate the geometry of the representation manifold with the feedforward inputs, rather than imposing it. More generally, our results represent a proof of principle against the intuition that the architecture and the representation manifold of an attractor are topological objects of the same dimensionality, with implications to the study of attractor networks across the brain.

### Competing Interest Statement

The authors have declared no competing interest.

Modeled grid cells aligned by a flexible attractor

六角格子状の空間表現を生成する柔軟な引力子モデル


精密に均衡した記憶ネットワークでは、学習された入力に対する活動パターンが連続的な表現空間に制限されることで、パターン分類を支援する。


Biological memory networks are thought to store information in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we created a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that “focused” activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual’s experience. Such continuous internal representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

### Competing Interest Statement

The authors have declared no competing interest.

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

正確に記憶された嗅覚情報の表現に関する幾何学と力学


神経処理におけるアンチフラジャイル性は、不確実性や変動性から利益を得ることができる。


神経処理におけるアンチフラジャイル制御システム-感覚運動制御の観点から


神経処理におけるアンチフラジャイル制御システム：感覚運動制御の観点から
