玉川大学脳科学研究所特別講演会（第12回）、脳科学研究科「脳科学先端セミナー」「心の科学先端セミナー」を2023年8月7日（月）にハイブリッドで開催
2023.07.12
玉川大学脳科学研究所特別講演会（第12回）、脳科学研究科「脳科学先端セミナー」「心の科学先端セミナー」の開催について
| 日時 | 2023年8月7日（月）15時00分 〜16 時30 分 |
|---|---|
| 開催方法 | ハイブリッド開催（Zoom meeting） |
| 講演者 | Barbara Feulner先生 (Aignostics GmbH, Berlin) |
| 講演タイトル | Sensory feedback can drive adaptation in motor cortex and facilitate generalization |
| 講演内容 | Experimental and computational studies suggest that motor cortex acts as a feedback controller, allowing for ‘on-the-fly’ movement corrections in response to afferent sensory feedback. However, it remains unclear whether feedback control relates to longer-term learning, and how this would be implemented in neural circuitry. Here, we tackled these questions by testing how a recurrent neural network (RNN) can use feedback to control its own output, and whether this process can enable learning. We built an RNN that received feedback signaling the error between its intended and observed output. An initial training phase that required producing a broad range of outputs (i.e., ‘movements’) enabled the model to learn to use this feedback to correct its output on-the-fly. After constructing this RNN, we tested directly whether the feedback signal used for online output correction could enable learning by guiding synaptic plasticity in the recurrent connections within the network. We devised a biologically plausible plasticity rule where the recurrent weight changes were proportional to the error feedback signals received by the postsynaptic neurons. This simple rule allowed the network to adapt to persistent perturbations (e.g., a ‘visuomotor rotation’) by changing its initial output pattern, a process that was mediated through recurrent connectivity changes. Remarkably, the model learned in a way that was similar to adaptation studies in humans [1,2]: i) learning generalized to non-learned but similar movements [1] and ii) followed multiple learning timescales [2]. When we examined the network activity before and after adaptation, we found a signature of our learning rule that was also present in neural population recordings from monkey motor cortex (data from [3]). In short, this work links computational models of motor control and learning to a biologically plausible implementation in neural circuitry, thus offering the potential to guide future experimental studies on the neural basis of motor learning. References: [1] Krakauer et al., 2000, J Neurosci, [2] Smith et al., 2006, PLOS Biol., [3] Perich et al., 2018, Neuron |
| 参加登録 | 学外の方につきましては、オンラインでの参加となります。 Zoom meetingでの開催のため、下記サイトにて登録をお願いします。登録後、Zoom meetingのURL等をお送りします（登録時の個人情報は本講演会のご案内以外には使用しません）。 事前登録は終了いたしました |
| 登録締切日 | 2023年8月6日（日）17時 |
| 個人情報の取り組み | 下記、個人情報への取り組みをご確認のうえ、同意する場合のみ参加登録をお願いします。 |
| 参加費 | 無料 |
| 共催 | 玉川大学脳科学研究所 玉川大学脳科学研究科 文部科学省科研費新学術領域研究「超適応」 |
| お問い合わせ | 玉川大学研究推進事業部研究推進課 eメール:research@tamagawa.ac.jp Tel：042-739-8666 |
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